feat(metering): per-model audio rates stored in the clearing house (#386 )

Audio jobs are metered in seconds of generated audio, converted to ledger tokens at a per-model rate that is now configuration rather than a compiled-in constant: - helexa-upstream gains a model_rates table (model_id, unit, tokens_per_unit; audited like app_config) and serves it at GET /authz/v1/rates behind the existing client bearer. model_id '*' is a unit's default; the migration seeds a placeholder audio default. - cortex caches the table, refreshing it every minute (every five seconds until the first load). A model resolves to its own rate, else the default; with neither, metered submissions are refused with a retryable 503 rather than served free. A cortex without upstream prices audio at a local default. - A job records the rate it was reserved at and settles at that rate, so a price change never alters work already accepted. Re-queued jobs after a restart re-reserve at the current rate. - /v1/models advertises audio_tokens_per_second for audio models (aliases priced as their target), so clients can show an estimate before submitting. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017ohK2pULMVJctL5B1Gixdp
feat(audio): async audio generation job API across neuron, cortex and router (#382 )
2026-10-17 14:30:35 +03:00 · 2026-10-17 14:20:01 +03:00 · 2026-10-14 16:05:36 +00:00 · 2026-10-14 18:44:59 +03:00 · 2026-10-14 18:08:27 +03:00 · 2026-10-01 10:05:11 +03:00
229 changed files with 330915 additions and 150 deletions
@@ -525,7 +525,14 @@ jobs:
            cuda_home: /usr/local/cuda-13.0
            build_jobs: 8
            nvcc_threads: 4
-            cargo_features: "cuda cudnn"
+            # flash-attn on ampere (#337): Laya's encoder runs every
+            # attention through flash_attn_varlen_windowed, matching the
+            # PyTorch reference on a 3060 (36 vs 39 ms at 512 tokens) where
+            # the eager path is 1.4-1.7x slower. This also moves the
+            # flavour's other flash-capable paths onto flash -- qwen3_5
+            # full attention and Z-Image -- which validate-neuron.sh and
+            # validate-image.sh gate. NEURON_FLASH_ATTN=0 rolls back.
+            cargo_features: "cuda cudnn flash-attn"
          - flavour: ada
            compute_cap: "89"
            runner: cuda-13.0
@@ -540,8 +547,8 @@ jobs:
            build_jobs: 8
            nvcc_threads: 4
            # flash-attn on blackwell first (#95): beast carries the
-            # agentic prefill pain; ada/ampere follow once the win is
-            # measured. NEURON_FLASH_ATTN=0 is the runtime rollback.
+            # agentic prefill pain. NEURON_FLASH_ATTN=0 is the runtime
+            # rollback.
            cargo_features: "cuda cudnn flash-attn"
    runs-on: ${{ matrix.runner }}
    env:
@@ -244,10 +244,14 @@ jobs:
            flavour: blackwell
            load_timeout: 900
            max_prompt_tokens: 131072
-          - host: benjy.hanzalova.internal
-            flavour: ada
-            load_timeout: 300
-            max_prompt_tokens: 16384
+          # benjy is out of the matrix while its boot NVMe is replaced
+          # (dead 2026-09-29): every deploy failed at SSH init against a
+          # host that is not there. Restore these four lines when it is
+          # back on the mesh.
+          # - host: benjy.hanzalova.internal
+          #   flavour: ada
+          #   load_timeout: 300
+          #   max_prompt_tokens: 16384
          - host: quadbrat.hanzalova.internal
            flavour: ampere
            load_timeout: 300
@@ -28,3 +28,6 @@ doc/plan/*
 .claude/
 helexa-upstream.toml
 helexa-angels.toml
+
+# Local scratch space for playground example checks (never committed).
+.pg-scratch/
@@ -1143,3 +1143,102 @@ constructors on the MoE and MLP.
  budget matches a naive oracle — are closed.
 - **MTP (#313), vision (#314), quantisation and rollout (#315)** are
  untouched.
+
+## 2026-09-29 addendum: System-One decisions (Laya, epic #333)
+
+A third modality beside text and images: **typed decisions**.
+`convaiinnovations/laya` is a 421M non-autoregressive decision model, a
+ModernBERT encoder plus an option-scoring head. It answers `choice` /
+`score` / `noul` questions over a *state* in one encoder forward, with
+calibrated probabilities, on the TypeSafe Jev wire protocol:
+`POST /v1/systemone`. It never generates text. Resident on quadbrat's
+3060 since #351, replacing the Qwen3-1.7B pre-warm.
+
+**The reference is the `laya` pip SDK** (`common.py`, `agent.py`,
+`router.py`, `serve.py`), **not** the `rl_common.py` in the model repo,
+which has diverged. Fixtures recorded from SDK 0.3.21 at model revision
+`55cf4c4e` live in `crates/neuron/src/harness/testdata/laya/`:
+- `reference.json`: 28 cases, f32 CPU.
+- `reference_cuda_bf16.json`: the SDK's own GPU bf16 run, the envelope
+  our bf16 is judged against.
+- `lang_corpus.json`: 924 detector cases.
+
+Recorders are `script/laya-reference.py` and `script/laya-lang-reference.py`.
+
+**Where it lives:**
+- `cortex-core/src/decisions.rs`: wire types, and `Json`, an
+  order-preserving, correctly rounded, `json.loads`-compatible value.
+  Request data must not go through `serde_json::Value`: the workspace
+  has no `preserve_order`, and without `float_roundtrip` serde_json
+  misreads floats (`…445` → `…443`). Key order changes token ids and
+  what the language detector reads.
+- `neuron/src/harness/decision/`: question validation, sequence
+  assembly under every budget rule, calibration in numpy's float32
+  order, answer shaping, and `route.rs` (checkpoint choice, ported from
+  upstream `Router._route`, reason strings included).
+- `neuron/src/harness/lang_detect.rs`: the script/language detector.
+  Python-semantics Unicode classes: `[^\W\d_]` never matches a combining
+  mark.
+- `neuron/src/harness/arch/laya/`: the engine. The ModernBERT backbone
+  is **vendored**, because candle's only runs in f32.
+- `decision_model.rs`, plus `CandleHarness::systemone` in `candle.rs`.
+
+**One repo, three checkpoints:** English at the root, `multilingual/`
+and `typed-decisions/`, all resident. Precedence: a `model` pin (a
+checkpoint name, an alias, or a published id such as
+`convaiinnovations/laya-multilingual`), else the detected script and
+language, else English. The English checkpoint collapses outside
+English (Khmer: 0.000 accuracy at 0.952 confidence upstream), so
+language routing is correctness, not polish.
+
+**Surface semantics:**
+- On this endpoint only, a missing or unknown `model` resolves to
+  **`helexa/one`** at every hop, because Jev clients send their own
+  service's name (`jev-1`).
+- The body is never rewritten, since `model` may be a checkpoint pin;
+  the routed model travels in `x-helexa-decision-model`.
+- Metering follows Jev: **input tokens only**. That is the sum of
+  encoded sequence lengths, and the state is encoded once per question.
+  Reserved as questions × `max_len`, settled to `usage.input_tokens`,
+  priced at $0.042/M.
+- Errors carry the #63 envelope plus FastAPI's `detail`.
+
+**GPU performance (3060, #337).** Laya needs the ampere flavour's
+`flash-attn` (enabled in #350; `NEURON_FLASH_ATTN=0` rolls back). What
+the profile found:
+- candle's `LayerNorm` only fuses when a bias exists, and ModernBERT's
+  norms have none: six kernels per norm, about 25% of GPU time. Fixed
+  with an explicit zero bias.
+- Mask add and scale over the full score tensor. Elided when nothing is
+  masked; scale folded into q.
+- Eager attention replaced by `flash_attn_varlen_windowed` over an
+  unpadded pack (sliding layers ±64).
+
+Measured, bf16: 36 ms for one question over a 512-token state (reference
+39), 32 seq/s batched. Live: 10.6 ms median at the neuron over the mesh.
+bf16 drift is autocast-style: residual, norms and attention scores stay
+in f32. Beyond that, per-case drift is chaotic; judge it by
+mean-vs-reference-envelope, not single cases.
+
+**Validation:**
+- `script/validate-decision.py <url>` replays the corpus against any
+  hop: checkpoint and usage exact, answers except on reference
+  near-ties, probabilities within a bf16 tolerance.
+- The public edge is `https://helexa.ai/v1/systemone`. It is rate-limited
+  (10 r/min per IP), so replay against the router (gallumbits:8088).
+  Probing the public names has network constraints; see
+  `doc/infra/edge-ingress.md` in the private helexa/security repo.
+
+**Sharp edges:**
+- **rustingface#6:** a neuron cold load of anything rf.internal hasn't
+  cached fails on a 503 to hf-hub's bounded-range size probe. A Laya
+  family member then loads *without* that checkpoint, logged but
+  silent. Warm with full GETs, then reload.
+- helexa-bench chat scenarios must gate on `serves_chat()`, which
+  excludes both `image` and `decision` models.
+
+**Open:** #353 (revision pinning, per-language temperatures, fetch
+retries, a bench `decision:` scenario, a dashboard panel,
+micro-batching); #342 (serving fine-tuned checkpoints); #352 (Z-Image on
+the ampere flash build, unrelated to Laya but enabled by the same
+flavour change).
@@ -40,7 +40,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "cabdc9d845d08ec7ed2d0c9de1ae4a1b198301407d55855261572761be90ec9f"
 dependencies = [
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -261,7 +261,7 @@ checksum = "c7c24de15d275a1ecfd47a380fb4d5ec9bfe0933f309ed5e705b775596a3574d"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -272,13 +272,13 @@ checksum = "8b75356056920673b02621b35afd0f7dda9306d03c79a30f5c56c44cf256e3de"

 [[package]]
 name = "async-trait"
-version = "0.1.89"
+version = "0.1.92"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9035ad2d096bed7955a320ee7e2230574d28fd3c3a0f186cbea1ff3c7eed5dbb"
+checksum = "82f6aeea286b8eb4dd3431a1be1b59d290ace00f5bfd8e2a159bc2a05e2c1667"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 3.0.9",
 ]

 [[package]]
@@ -394,7 +394,7 @@ checksum = "7aa268c23bfbbd2c4363b9cd302a4f504fb2a9dfe7e3451d66f35dd392e20aca"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -508,7 +508,7 @@ checksum = "f9abbd1bc6865053c427f7198e6af43bfdedc55ab791faed4fbd361d789575ff"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -697,7 +697,7 @@ dependencies = [
 "heck",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -853,6 +853,7 @@ dependencies = [
 "reqwest",
 "serde",
 "serde_json",
+ "tempfile",
 "thiserror 2.0.18",
 "tokio",
 "tokio-stream",
@@ -861,6 +862,7 @@ dependencies = [
 "tracing",
 "url",
 "urlencoding",
+ "uuid",
 ]

 [[package]]
@@ -966,7 +968,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "10a2a99df6e410a8ff4245aa2006499ea662245f967cc7c0a38c83ef8eb44dbf"
 dependencies = [
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1039,7 +1041,7 @@ dependencies = [
 "proc-macro2",
 "quote",
 "strsim",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1052,7 +1054,7 @@ dependencies = [
 "proc-macro2",
 "quote",
 "strsim",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1063,7 +1065,7 @@ checksum = "fc34b93ccb385b40dc71c6fceac4b2ad23662c7eeb248cf10d529b7e055b6ead"
 dependencies = [
 "darling_core 0.20.11",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1074,7 +1076,7 @@ checksum = "ac3984ec7bd6cfa798e62b4a642426a5be0e68f9401cfc2a01e3fa9ea2fcdb8d"
 dependencies = [
 "darling_core 0.23.0",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1125,7 +1127,7 @@ dependencies = [
 "darling 0.20.11",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1135,7 +1137,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "ab63b0e2bf4d5928aff72e83a7dace85d7bba5fe12dcc3c5a572d78caffd3f3c"
 dependencies = [
 "derive_builder_core",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1157,7 +1159,7 @@ dependencies = [
 "proc-macro2",
 "quote",
 "rustc_version",
- "syn",
+ "syn 2.0.117",
 "unicode-xid",
 ]

@@ -1202,7 +1204,7 @@ checksum = "97369cbbc041bc366949bc74d34658d6cda5621039731c6310521892a3a20ae0"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1337,7 +1339,7 @@ dependencies = [
 "heck",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -1667,7 +1669,7 @@ checksum = "e835b70203e41293343137df5c0664546da5745f82ec9b84d40be8336958447b"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3096,7 +3098,7 @@ checksum = "e4db6d5580af57bf992f59068d4ea26fd518574ff48d7639b255a36f9de6e7e9"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3166,6 +3168,8 @@ dependencies = [
 "toml",
 "tracing",
 "tracing-subscriber",
+ "unicode-normalization",
+ "unicode-properties",
 ]

 [[package]]
@@ -3372,7 +3376,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3471,7 +3475,7 @@ dependencies = [
 "proc-macro2",
 "proc-macro2-diagnostics",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3540,7 +3544,7 @@ dependencies = [
 "phf_shared",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3569,7 +3573,7 @@ checksum = "c96395f0a926bc13b1c17622aaddda1ecb55d49c8f1bf9777e4d877800a43f8b"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3692,7 +3696,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b"
 dependencies = [
 "proc-macro2",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -3712,7 +3716,7 @@ checksum = "af066a9c399a26e020ada66a034357a868728e72cd426f3adcd35f80d88d88c8"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "version_check",
 "yansi",
 ]
@@ -4064,7 +4068,7 @@ checksum = "b7186006dcb21920990093f30e3dea63b7d6e977bf1256be20c3563a5db070da"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4189,7 +4193,7 @@ dependencies = [
 "proc-macro2",
 "quote",
 "serde_json",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4392,7 +4396,7 @@ dependencies = [
 "proc-macro2",
 "quote",
 "serde_derive_internals",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4482,7 +4486,7 @@ checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4493,7 +4497,7 @@ checksum = "18d26a20a969b9e3fdf2fc2d9f21eda6c40e2de84c9408bb5d3b05d499aae711"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4579,7 +4583,7 @@ dependencies = [
 "darling 0.23.0",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4812,7 +4816,7 @@ dependencies = [
 "quote",
 "sqlx-core",
 "sqlx-macros-core",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -4835,7 +4839,7 @@ dependencies = [
 "sqlx-mysql",
 "sqlx-postgres",
 "sqlx-sqlite",
- "syn",
+ "syn 2.0.117",
 "tokio",
 "url",
 ]
@@ -5020,7 +5024,7 @@ dependencies = [
 "heck",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5040,6 +5044,17 @@ dependencies = [
 "unicode-ident",
 ]

+[[package]]
+name = "syn"
+version = "3.0.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d78c8dee4c7bf0e14673097256fed6142ce9d3b85a408189d07482442145823b"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "unicode-ident",
+]
+
 [[package]]
 name = "sync_wrapper"
 version = "1.0.2"
@@ -5057,7 +5072,7 @@ checksum = "728a70f3dbaf5bab7f0c4b1ac8d7ae5ea60a4b5549c8a5914361c99147a709d2"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5143,7 +5158,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5154,7 +5169,7 @@ checksum = "ebc4ee7f67670e9b64d05fa4253e753e016c6c95ff35b89b7941d6b856dec1d5"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5280,7 +5295,7 @@ checksum = "385a6cb71ab9ab790c5fe8d67f1645e6c450a7ce006a33de03daa956cf70a496"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5438,7 +5453,7 @@ checksum = "7490cfa5ec963746568740651ac6781f701c9c5ea257c58e057f3ba8cf69e8da"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5798,7 +5813,7 @@ dependencies = [
 "bumpalo",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "wasm-bindgen-shared",
 ]

@@ -5988,7 +6003,7 @@ checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -5999,7 +6014,7 @@ checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -6313,7 +6328,7 @@ dependencies = [
 "heck",
 "indexmap 2.14.0",
 "prettyplease",
- "syn",
+ "syn 2.0.117",
 "wasm-metadata",
 "wit-bindgen-core",
 "wit-component",
@@ -6329,7 +6344,7 @@ dependencies = [
 "prettyplease",
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "wit-bindgen-core",
 "wit-bindgen-rust",
 ]
@@ -6423,7 +6438,7 @@ checksum = "2380878cad4ac9aac1e2435f3eb4020e8374b5f13c296cb75b4620ff8e229154"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "synstructure",
 ]

@@ -6435,7 +6450,7 @@ checksum = "de844c262c8848816172cef550288e7dc6c7b7814b4ee56b3e1553f275f1858e"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "synstructure",
 ]

@@ -6456,7 +6471,7 @@ checksum = "70e3cd084b1788766f53af483dd21f93881ff30d7320490ec3ef7526d203bad4"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -6476,7 +6491,7 @@ checksum = "11532158c46691caf0f2593ea8358fed6bbf68a0315e80aae9bd41fbade684a1"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 "synstructure",
 ]

@@ -6516,7 +6531,7 @@ checksum = "625dc425cab0dca6dc3c3319506e6593dcb08a9f387ea3b284dbd52a92c40555"
 dependencies = [
 "proc-macro2",
 "quote",
- "syn",
+ "syn 2.0.117",
 ]

 [[package]]
@@ -52,21 +52,10 @@ curl -s 'http://localhost:26559/api/v1/targets' | jq '.data.activeTargets[]|sele

 `grafana-helexa-fleet.json` is a raw dashboard model with a `datasource`
 template variable, so it binds to whichever Prometheus data source you pick
-at import. Import via the API (creds in `/etc/grafana/grafana.env`):
-
-```sh
-# on golgafrinchans; GF_SECURITY_ADMIN_USER/PASSWORD live in the root-only
-# env file, Grafana is published on :28767
-set -a; source <(sudo cat /etc/grafana/grafana.env); set +a
-jq -n --slurpfile d grafana-helexa-fleet.json \
-  '{dashboard: $d[0], overwrite: true, folderUid: null}' \
-| curl -s -u "$GF_SECURITY_ADMIN_USER:$GF_SECURITY_ADMIN_PASSWORD" \
-    -H 'Content-Type: application/json' \
-    -d @- http://localhost:28767/api/dashboards/db | jq '{status,uid,version}'
-```
-
-The dashboard lands at uid `helexa-fleet`. Re-importing with `overwrite:true`
-updates it in place.
+at import. Import it through the Grafana dashboards API with admin
+credentials (`overwrite: true` updates it in place; it lands at uid
+`helexa-fleet`). The exact command and where the admin credentials live are
+in `doc/infra/monitoring-access.md` in the private helexa/security repo.

 ## What the dashboard shows

@@ -7,9 +7,10 @@
 # ─────────────────────────────────────────────────────────────────────
 # neuron.toml for quadbrat.hanzalova.internal
 #
-# 1x RTX 3060 (12 GB) — small / quantised tier. Pre-warms Qwen3-1.7B
-# (bf16, ~4 GB), leaving ~7 GB for KV cache so long contexts on a small
-# model still have plenty of room.
+# 1x RTX 3060 (12 GB). Resident: Laya, the System-One decision model
+# (#333, ~2.3 GB bf16 for its three checkpoints). It replaced the
+# Qwen3-1.7B pre-warm (operator decision 2026-09-29, #341); the 1.7B is
+# still cold-loadable beside it for helexa/small.
 #
 # Synced to /etc/neuron/neuron.toml by script/infra-setup.sh.

@@ -21,10 +22,21 @@ name = "candle"
 [harness.candle]

 [[default_models]]
-model_id = "Qwen/Qwen3-1.7B"
+model_id = "convaiinnovations/laya"
 harness = "candle"
 devices = [0]

+# A decision request is one forward of tens of milliseconds (36 ms for
+# one question over a 512-token state, 162 ms for five), so a one-slot
+# queue of 8 rejects ordinary bursts. Admit
+# several at once -- the device worker still runs forwards one at a
+# time -- and let a deep, short-deadline queue absorb the rest. Applies
+# to every model on this host, including a cold-loaded helexa/small.
+[harness.candle.admission]
+max_in_flight = 4
+max_queue_depth = 64
+max_wait_secs = 10
+
 # Text-to-image (#204): the 12 GB tier serves the q8_0-quantized DiT
 # (8.7 GB resident incl. VAE). Ceiling is 768² — naive f32 attention
 # transients OOM 1024² beside the resident model; lifts to 1024² if
@@ -34,8 +34,8 @@ server {

 server {
    # TCP 443 on the edge is owned by the stream SNI router
-    # (streams-enabled/sni-router.conf, architecture reverse-proxies.md
-    # §5), which preads the SNI and hands every local name to this http
+    # (streams-enabled/sni-router.conf; see doc/infra/edge-ingress.md in
+    # the private helexa/security repo), which preads the SNI and hands every local name to this http
    # tier over PROXY protocol. Listening on :443 directly here would
    # never be reached — the router would answer first with whichever
    # vhost happened to be default, which is exactly the symptom seen
@@ -1,6 +1,7 @@
-# Internal bench UI vhost — https://bench.internal, reachable from inside
-# the WireGuard mesh (the public bench.helexa.ai dead-ends at the OPNsense
-# LAN interface, which only port-forwards :443 from the WAN). Same SPA +
+# Internal bench UI vhost — https://bench.internal, for clients inside
+# the WireGuard mesh, where the public bench.helexa.ai doesn't resolve
+# to a usable path (see doc/infra/edge-ingress.md in the private
+# helexa/security repo). Same SPA +
 # /api→bob proxy as bench.helexa.ai, but with an internal-CA cert
 # (smallstep "lair", renewed by step@bench.timer). Mirrors the
 # *.internal vhost convention on oolon.kosherinata.internal.
@@ -4,7 +4,7 @@
 # Installed on BOTH edge proxies (oolon.kosherinata.internal and
 # hanzalova.internal); Cloudflare DNS load balancing routes the public
 # name to the two site WAN IPs. Mesh clients use helexa.internal instead
-# (public names don't hairpin — see architecture/reverse-proxies.md §2).
+# (see doc/infra/edge-ingress.md in the private helexa/security repo).
 #
 # Same-origin backends (#167): /v1 (+ /health) reverse-proxies to
 # helexa-router and /api to helexa-upstream, both on gallumbits — so the
@@ -1,7 +1,7 @@
-# Internal helexa website vhost — https://helexa.internal, reachable from
-# inside the WireGuard mesh (the public helexa.ai dead-ends at the
-# OPNsense LAN interface, which only port-forwards :443 from the WAN —
-# see architecture/reverse-proxies.md §2). Same SPA webroot as the
+# Internal helexa website vhost — https://helexa.internal, for clients
+# inside the WireGuard mesh, where the public name doesn't resolve to a
+# usable path (see doc/infra/edge-ingress.md in the private
+# helexa/security repo). Same SPA webroot as the
 # public vhost, but with an internal-CA cert (smallstep "lair", renewed
 # by step@helexa.timer). Installed on hanzalova.internal only; the
 # split-horizon helexa.internal record points at hanzalova's mesh IP on
@@ -113,3 +113,19 @@ enabled = false
 # timeout_secs = 5
 # How often to flush served-usage counters to upstream for reconciliation (#58).
 # served_usage_report_interval_secs = 60
+
+# -- Async audio generation jobs (#382) -----------------------------------
+# Music renders take seconds to minutes, so /v1/audio/generations is a job
+# API: submit, poll, fetch the audio. Job records and rendered audio live
+# under `dir` and survive a restart (the packaged unit's StateDirectory
+# creates /var/lib/cortex). All values below are the defaults.
+[audio_jobs]
+# dir = "/var/lib/cortex/audio-jobs"
+# Finished jobs and their audio are deleted after this many hours.
+# retention_hours = 72
+# Renders running at once across the fleet; the rest queue in cortex.
+# max_concurrent = 4
+# Dispatch attempts per job; a busy (429/503) model does not use one up.
+# max_attempts = 2
+# How long a job may wait on a busy model before failing, in seconds.
+# queue_timeout_secs = 3600
@@ -0,0 +1,342 @@
+//! Music / audio generation surface (#382).
+//!
+//! OpenAI has no music endpoint, so this is a helexa extension. It follows
+//! the images surface where that makes sense (#197) but is **asynchronous**:
+//! a song takes seconds to minutes to render, so the public API is a job API.
+//!
+//! - `POST   /v1/audio/generations`               submit → `202` + [`AudioJob`]
+//! - `GET    /v1/audio/generations`               list the caller's jobs
+//! - `GET    /v1/audio/generations/{id}`          poll → [`AudioJob`]
+//! - `GET    /v1/audio/generations/{id}/content`  the rendered audio bytes
+//! - `DELETE /v1/audio/generations/{id}`          cancel
+//!
+//! cortex owns the job record. A neuron renders exactly one generation per
+//! dispatch, synchronously, on `POST /v1/audio/generations`: the response
+//! body is the audio (`audio/wav`) and the result metadata travels in the
+//! [`RESULT_HEADER`] header as JSON ([`NeuronAudioResult`]). Keeping the
+//! bytes out of a JSON envelope matters here — ten minutes of 48 kHz stereo
+//! is over 100 MB of PCM.
+
+use serde::{Deserialize, Serialize};
+
+/// Header carrying [`NeuronAudioResult`] JSON on a neuron's audio response.
+pub const RESULT_HEADER: &str = "x-helexa-audio-result";
+
+/// Capability string a model advertises when it serves this surface.
+pub const CAPABILITY: &str = "audio";
+
+/// `POST /v1/audio/generations` request body. The same body is accepted by
+/// cortex (which turns it into a job) and by neuron (which renders it).
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct AudioGenerationRequest {
+    pub model: String,
+    /// Style / caption: what the music should sound like.
+    pub prompt: String,
+    /// Lyrics for models that sing. Absent, empty, or `[Instrumental]`
+    /// means an instrumental.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub lyrics: Option<String>,
+    /// Requested length in seconds. Defaults to the model's advertised
+    /// default; validated against its advertised range.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub duration: Option<f64>,
+    /// Fixed RNG seed for reproducible renders. The seed actually used is
+    /// always reported back, so a random render can be reproduced too.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub seed: Option<u64>,
+    /// Container for the delivered audio. Only `"wav"` today.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub response_format: Option<String>,
+
+    // ── model-specific extensions ───────────────────────────────
+    /// Diffusion steps (diffusion models only).
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub steps: Option<u32>,
+    /// Guidance / CFG scale.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub guidance_scale: Option<f64>,
+    /// Song planner: `"off"`, `"auto"` (model default) or `"on"`, for
+    /// models that have one (ACE-Step's LM).
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub planner: Option<String>,
+    /// Sampling temperature (autoregressive models only).
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub temperature: Option<f64>,
+    /// Top-k sampling (autoregressive models only).
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub top_k: Option<u32>,
+}
+
+/// Response formats the surface delivers.
+pub const SUPPORTED_FORMATS: &[&str] = &["wav"];
+
+impl AudioGenerationRequest {
+    /// True when the request asks for an instrumental (no singing).
+    pub fn is_instrumental(&self) -> bool {
+        match self.lyrics.as_deref().map(str::trim) {
+            None | Some("") => true,
+            Some(l) => l.eq_ignore_ascii_case("[instrumental]"),
+        }
+    }
+
+    /// Transport-level validation every hop can do without knowing the
+    /// model: required fields, sane numbers, a known format. Model-specific
+    /// limits ([`AudioLimits`]) are checked separately.
+    pub fn validate_shape(&self) -> Result<(), String> {
+        if self.model.trim().is_empty() {
+            return Err("'model' must be non-empty".into());
+        }
+        if self.prompt.trim().is_empty() {
+            return Err("'prompt' must be non-empty".into());
+        }
+        if let Some(d) = self.duration
+            && !(d.is_finite() && d > 0.0)
+        {
+            return Err(format!("'duration' must be a positive number, got {d}"));
+        }
+        if let Some(f) = self.response_format.as_deref()
+            && !SUPPORTED_FORMATS.contains(&f)
+        {
+            return Err(format!(
+                "response_format '{f}' is not supported; use one of {SUPPORTED_FORMATS:?}"
+            ));
+        }
+        if let Some(p) = self.planner.as_deref()
+            && !matches!(p, "off" | "auto" | "on")
+        {
+            return Err(format!("planner must be 'off', 'auto' or 'on', got '{p}'"));
+        }
+        Ok(())
+    }
+
+    /// Validate against a model's advertised limits.
+    pub fn validate_limits(&self, limits: &AudioLimits) -> Result<(), String> {
+        let d = self.duration.unwrap_or(limits.default_duration);
+        if d < limits.min_duration || d > limits.max_duration {
+            return Err(format!(
+                "duration {d}s is outside this model's range {}..={}s",
+                limits.min_duration, limits.max_duration
+            ));
+        }
+        if !limits.vocals && !self.is_instrumental() {
+            return Err("this model does not sing; omit 'lyrics' or use [Instrumental]".into());
+        }
+        if let (Some(s), Some(max)) = (self.steps, limits.max_steps)
+            && (s == 0 || s > max)
+        {
+            return Err(format!("steps must be in 1..={max}, got {s}"));
+        }
+        Ok(())
+    }
+}
+
+/// What a music model can do, advertised in `/v1/models` metadata so
+/// clients (and the `/music` page) can build their forms from it.
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct AudioLimits {
+    pub min_duration: f64,
+    pub max_duration: f64,
+    pub default_duration: f64,
+    /// Whether the model sings (accepts lyrics).
+    pub vocals: bool,
+    pub sample_rate: u32,
+    pub channels: u16,
+    /// Upper bound for `steps`, when the model takes one.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub max_steps: Option<u32>,
+}
+
+/// Lifecycle of a job.
+#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
+#[serde(rename_all = "snake_case")]
+pub enum JobStatus {
+    Queued,
+    Running,
+    Succeeded,
+    Failed,
+    Cancelled,
+}
+
+impl JobStatus {
+    pub fn is_terminal(self) -> bool {
+        matches!(self, Self::Succeeded | Self::Failed | Self::Cancelled)
+    }
+}
+
+/// The public job object returned by submit, poll, list and cancel.
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct AudioJob {
+    pub id: String,
+    /// Always `"audio.generation"`.
+    pub object: String,
+    pub status: JobStatus,
+    pub model: String,
+    /// Unix seconds.
+    pub created_at: u64,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub started_at: Option<u64>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub completed_at: Option<u64>,
+    /// Requested duration, echoed so a client can show progress context.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub duration: Option<f64>,
+    /// Present when `status == succeeded`.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub result: Option<AudioResult>,
+    /// Present when `status == succeeded`.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub usage: Option<AudioUsage>,
+    /// Present when `status == failed`: the #63 envelope's inner `error`.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub error: Option<serde_json::Value>,
+}
+
+pub const JOB_OBJECT: &str = "audio.generation";
+
+/// The rendered audio's properties.
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct AudioResult {
+    /// Actual length in seconds.
+    pub duration: f64,
+    pub sample_rate: u32,
+    pub channels: u16,
+    /// The seed the render used (random when the request gave none).
+    pub seed: u64,
+    /// Container of the stored audio (`"wav"`).
+    pub format: String,
+    /// Size of the stored audio in bytes.
+    pub bytes: u64,
+}
+
+/// Metering block. `helexa_audio_units` is audio-seconds generated (#386).
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct AudioUsage {
+    pub helexa_audio_units: f64,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub helexa_timing: Option<AudioTiming>,
+}
+
+/// Phase timing for one render, measured on the neuron.
+#[derive(Debug, Clone, Copy, Default, Serialize, Deserialize, PartialEq)]
+pub struct AudioTiming {
+    /// Text / lyric encoding.
+    pub encode_ms: u64,
+    /// Song planner (0 when it did not run).
+    pub plan_ms: u64,
+    /// Denoising, or autoregressive token generation.
+    pub generate_ms: u64,
+    /// Latent / codec decode to waveform.
+    pub decode_ms: u64,
+}
+
+/// What a neuron reports, in [`RESULT_HEADER`], beside the audio bytes.
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
+pub struct NeuronAudioResult {
+    pub duration: f64,
+    pub sample_rate: u32,
+    pub channels: u16,
+    pub seed: u64,
+    pub usage: AudioUsage,
+}
+
+/// `GET /v1/audio/generations` response.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct AudioJobList {
+    /// Always `"list"`.
+    pub object: String,
+    pub data: Vec<AudioJob>,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn req() -> AudioGenerationRequest {
+        serde_json::from_str(r#"{"model": "m", "prompt": "lofi"}"#).unwrap()
+    }
+
+    fn limits() -> AudioLimits {
+        AudioLimits {
+            min_duration: 10.0,
+            max_duration: 600.0,
+            default_duration: 30.0,
+            vocals: true,
+            sample_rate: 48_000,
+            channels: 2,
+            max_steps: Some(20),
+        }
+    }
+
+    #[test]
+    fn minimal_request_deserializes_and_validates() {
+        let r = req();
+        assert!(r.validate_shape().is_ok());
+        assert!(r.validate_limits(&limits()).is_ok());
+        assert!(r.is_instrumental());
+    }
+
+    #[test]
+    fn instrumental_detection() {
+        let mut r = req();
+        r.lyrics = Some("  [Instrumental] ".into());
+        assert!(r.is_instrumental());
+        r.lyrics = Some("[verse]\nla la".into());
+        assert!(!r.is_instrumental());
+    }
+
+    #[test]
+    fn shape_rejects_bad_values() {
+        let mut r = req();
+        r.prompt = " ".into();
+        assert!(r.validate_shape().is_err());
+        let mut r = req();
+        r.duration = Some(f64::NAN);
+        assert!(r.validate_shape().is_err());
+        let mut r = req();
+        r.duration = Some(-1.0);
+        assert!(r.validate_shape().is_err());
+        let mut r = req();
+        r.response_format = Some("ogg".into());
+        assert!(r.validate_shape().is_err());
+        let mut r = req();
+        r.planner = Some("maybe".into());
+        assert!(r.validate_shape().is_err());
+    }
+
+    #[test]
+    fn limits_reject_out_of_range() {
+        let l = limits();
+        let mut r = req();
+        r.duration = Some(5.0);
+        assert!(r.validate_limits(&l).is_err());
+        r.duration = Some(601.0);
+        assert!(r.validate_limits(&l).is_err());
+        r.duration = Some(600.0);
+        assert!(r.validate_limits(&l).is_ok());
+        r.steps = Some(21);
+        assert!(r.validate_limits(&l).is_err());
+        r.steps = Some(0);
+        assert!(r.validate_limits(&l).is_err());
+    }
+
+    #[test]
+    fn lyrics_refused_by_non_singing_model() {
+        let mut l = limits();
+        l.vocals = false;
+        let mut r = req();
+        r.lyrics = Some("sing this".into());
+        assert!(r.validate_limits(&l).is_err());
+        r.lyrics = Some("[Instrumental]".into());
+        assert!(r.validate_limits(&l).is_ok());
+    }
+
+    #[test]
+    fn job_status_serializes_snake_case() {
+        assert_eq!(
+            serde_json::to_string(&JobStatus::Succeeded).unwrap(),
+            "\"succeeded\""
+        );
+        assert!(JobStatus::Cancelled.is_terminal());
+        assert!(!JobStatus::Running.is_terminal());
+    }
+}
@@ -89,6 +89,11 @@ pub struct ModelProfile {
    /// are unioned with this set in the gateway's `/v1/models` response.
    #[serde(default)]
    pub capabilities: Vec<String>,
+    /// What an audio model can do (#382): duration range, vocals, sample
+    /// rate. Advertised in `/v1/models` and checked by cortex before a job
+    /// is accepted. `[models.audio]` in TOML.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub audio: Option<crate::audio::AudioLimits>,
 }

 fn default_min_devices() -> u32 {
@@ -283,6 +288,7 @@ mod tests {
            limit: None,
            cost: None,
            capabilities: vec![],
+            audio: None,
        }
    }

@@ -28,6 +28,63 @@ pub struct GatewayConfig {
    /// — a single operator runs purely local.
    #[serde(default)]
    pub upstream: UpstreamClientConfig,
+    /// Async music/audio generation jobs (#382).
+    #[serde(default)]
+    pub audio_jobs: AudioJobsConfig,
+}
+
+/// `[audio_jobs]` — where cortex keeps async audio generation jobs (#382).
+///
+/// A job record and its rendered audio are written under `dir`, so finished
+/// results and the job list survive a cortex restart. If `dir` cannot be
+/// created the store runs in memory only and says so at startup.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct AudioJobsConfig {
+    #[serde(default = "default_audio_jobs_dir")]
+    pub dir: String,
+    /// How long a finished job and its audio are kept, in hours.
+    #[serde(default = "default_audio_retention_hours")]
+    pub retention_hours: u64,
+    /// How many renders cortex runs at once across the fleet. Each one
+    /// occupies a neuron device for its whole duration; queued jobs wait
+    /// in cortex rather than piling onto neuron admission queues.
+    #[serde(default = "default_audio_max_concurrent")]
+    pub max_concurrent: usize,
+    /// Dispatch attempts per job before it fails. A busy neuron (429/503)
+    /// does not consume an attempt; an unreachable or failing one does.
+    #[serde(default = "default_audio_max_attempts")]
+    pub max_attempts: u32,
+    /// How long a job may wait for a busy model before it fails, in seconds.
+    #[serde(default = "default_audio_queue_timeout")]
+    pub queue_timeout_secs: u64,
+}
+
+impl Default for AudioJobsConfig {
+    fn default() -> Self {
+        Self {
+            dir: default_audio_jobs_dir(),
+            retention_hours: default_audio_retention_hours(),
+            max_concurrent: default_audio_max_concurrent(),
+            max_attempts: default_audio_max_attempts(),
+            queue_timeout_secs: default_audio_queue_timeout(),
+        }
+    }
+}
+
+fn default_audio_jobs_dir() -> String {
+    "/var/lib/cortex/audio-jobs".into()
+}
+fn default_audio_retention_hours() -> u64 {
+    72
+}
+fn default_audio_max_concurrent() -> usize {
+    4
+}
+fn default_audio_max_attempts() -> u32 {
+    2
+}
+fn default_audio_queue_timeout() -> u64 {
+    3600
 }

 /// `[upstream]` — the helexa-upstream authority client (#57). Locally
@@ -200,6 +257,7 @@ impl Default for GatewayConfig {
            models_config: default_models_path(),
            entitlements: EntitlementsConfig::default(),
            upstream: UpstreamClientConfig::default(),
+            audio_jobs: AudioJobsConfig::default(),
        }
    }
 }
@@ -1,7 +1,9 @@
 pub mod anthropic;
+pub mod audio;
 pub mod build_info;
 pub mod catalogue;
 pub mod config;
+pub mod decisions;
 pub mod discovery;
 pub mod entitlements;
 pub mod error_envelope;
@@ -186,6 +186,14 @@ pub struct CortexModelEntry {
    /// the absent (not priced) vs `0.0` (intentionally free) distinction.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub cost: Option<ModelCost>,
+    /// What an audio model can do (#382), from its catalogue profile:
+    /// duration range, vocals, sample rate. Clients build forms from it.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub audio: Option<crate::audio::AudioLimits>,
+    /// Ledger tokens charged per second of generated audio (#386), for
+    /// audio models with a configured rate.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub audio_tokens_per_second: Option<u64>,
    /// `true` when any neuron reports this model supports tool calls.
    #[serde(default)]
    pub tool_call: bool,
@@ -27,6 +27,8 @@ eventsource-stream.workspace = true
 bytes = "1"
 urlencoding = "2"
 url = "2"
+uuid = { version = "1", features = ["v4"] }

 [dev-dependencies]
 tokio = { workspace = true, features = ["test-util"] }
+tempfile = "3"
@@ -0,0 +1,884 @@
+//! Async music / audio generation jobs (#382).
+//!
+//! A render takes seconds to minutes, so the public surface is a job API
+//! (see [`cortex_core::audio`] for the routes). cortex owns the job: it
+//! records it, queues it, dispatches it to a neuron serving the model, keeps
+//! the rendered audio, and meters it. A neuron renders one generation per
+//! dispatch, synchronously, and knows nothing about jobs.
+//!
+//! Records and audio are written under `[audio_jobs] dir`, so the job list
+//! and finished results survive a cortex restart. Jobs that were queued or
+//! running when cortex stopped are re-queued at startup. A job whose neuron
+//! fails is retried on whatever node the router picks next, up to
+//! `max_attempts`; a neuron that is merely busy (429/503) is waited on, up
+//! to `queue_timeout_secs`, without spending an attempt.
+//!
+//! Ownership: a job belongs to the account that submitted it, and only that
+//! account can see, fetch or cancel it. Anonymous jobs (no principal) are
+//! reachable by id alone — the id is an unguessable capability — and never
+//! appear in a listing.
+
+use crate::metering::ReservationGuard;
+use crate::state::CortexState;
+use axum::Router;
+use axum::body::{Body, Bytes};
+use axum::extract::{Path, State};
+use axum::http::{HeaderMap, HeaderValue, StatusCode, header};
+use axum::response::{IntoResponse, Json, Response};
+use axum::routing::get;
+use cortex_core::audio::{
+    AudioGenerationRequest, AudioJob, AudioJobList, AudioResult, JOB_OBJECT, JobStatus,
+    NeuronAudioResult, RESULT_HEADER,
+};
+use cortex_core::config::AudioJobsConfig;
+use cortex_core::entitlements::{HEADER_ACCOUNT_ID, HEADER_KEY_ID, Principal};
+use cortex_core::error_envelope::OpenAiError;
+use serde::{Deserialize, Serialize};
+use serde_json::Value;
+use std::collections::HashMap;
+use std::path::PathBuf;
+use std::sync::{Arc, Mutex};
+use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
+use tokio::sync::{RwLock, Semaphore};
+use tokio::task::JoinHandle;
+
+/// Path of the job collection; `{id}` and `{id}/content` hang off it.
+pub const JOBS_PATH: &str = "/v1/audio/generations";
+
+/// Wait between dispatches to a busy model when the neuron sends no
+/// `Retry-After`.
+const DEFAULT_BUSY_WAIT: Duration = Duration::from_secs(5);
+
+/// How often expired jobs are swept.
+const SWEEP_INTERVAL: Duration = Duration::from_secs(600);
+
+/// A job as cortex stores it: the public object plus what is needed to run
+/// (or re-run) it.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+struct JobRecord {
+    job: AudioJob,
+    request: AudioGenerationRequest,
+    /// The submitting principal; `None` for an anonymous submission.
+    owner: Option<Principal>,
+    /// Ledger tokens per audio-second this job was reserved at (#386), so
+    /// it settles at the price it was accepted at even if the rate changes
+    /// while it runs. `None` for anonymous (unmetered) jobs.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    tokens_per_second: Option<u64>,
+}
+
+/// The job store and dispatcher, held in [`CortexState`].
+pub struct AudioJobs {
+    config: AudioJobsConfig,
+    /// See [`AudioJobs::dir`].
+    dir: std::sync::OnceLock<Option<PathBuf>>,
+    jobs: RwLock<HashMap<String, JobRecord>>,
+    /// Worker tasks for jobs that are queued or running, for cancellation.
+    workers: Mutex<HashMap<String, JoinHandle<()>>>,
+    /// Bounds concurrent renders across the fleet.
+    slots: Arc<Semaphore>,
+    /// Rendered audio, when there is no job directory to write it to.
+    memory_audio: Mutex<HashMap<String, Bytes>>,
+    /// Client for neuron dispatches. A neuron sends nothing until the render
+    /// is finished, so the read timeout has to cover a whole render.
+    http: reqwest::Client,
+}
+
+impl AudioJobs {
+    pub fn new(config: &AudioJobsConfig) -> Self {
+        let http = reqwest::Client::builder()
+            .read_timeout(Duration::from_secs(config.queue_timeout_secs.max(1800)))
+            .build()
+            .expect("failed to build audio HTTP client");
+        Self {
+            config: config.clone(),
+            dir: std::sync::OnceLock::new(),
+            jobs: RwLock::new(HashMap::new()),
+            workers: Mutex::new(HashMap::new()),
+            slots: Arc::new(Semaphore::new(config.max_concurrent.max(1))),
+            memory_audio: Mutex::new(HashMap::new()),
+            http,
+        }
+    }
+
+    /// The job directory, created on first use. `None` (memory only) when it
+    /// cannot be created. Lazy so that building a gateway does not touch the
+    /// filesystem until audio jobs are actually used.
+    fn dir(&self) -> Option<&PathBuf> {
+        self.dir
+            .get_or_init(|| {
+                let dir = PathBuf::from(&self.config.dir);
+                match std::fs::create_dir_all(&dir) {
+                    Ok(()) => Some(dir),
+                    Err(e) => {
+                        tracing::warn!(
+                            dir = %self.config.dir,
+                            error = %e,
+                            "audio job directory unavailable; jobs will not survive a restart"
+                        );
+                        None
+                    }
+                }
+            })
+            .as_ref()
+    }
+
+    fn record_path(&self, id: &str) -> Option<PathBuf> {
+        self.dir().map(|d| d.join(format!("{id}.json")))
+    }
+
+    fn audio_path(&self, id: &str) -> Option<PathBuf> {
+        self.dir().map(|d| d.join(format!("{id}.wav")))
+    }
+
+    async fn persist(&self, record: &JobRecord) {
+        let Some(path) = self.record_path(&record.job.id) else {
+            return;
+        };
+        let bytes = match serde_json::to_vec_pretty(record) {
+            Ok(b) => b,
+            Err(e) => {
+                tracing::warn!(job = %record.job.id, error = %e, "audio job serialise failed");
+                return;
+            }
+        };
+        // Write-then-rename so a crash never leaves a torn record.
+        let tmp = path.with_extension("json.tmp");
+        if let Err(e) = tokio::fs::write(&tmp, &bytes).await {
+            tracing::warn!(job = %record.job.id, error = %e, "audio job write failed");
+            return;
+        }
+        if let Err(e) = tokio::fs::rename(&tmp, &path).await {
+            tracing::warn!(job = %record.job.id, error = %e, "audio job rename failed");
+        }
+    }
+
+    async fn update<F: FnOnce(&mut JobRecord)>(&self, id: &str, f: F) -> Option<JobRecord> {
+        let snapshot = {
+            let mut jobs = self.jobs.write().await;
+            let record = jobs.get_mut(id)?;
+            f(record);
+            record.clone()
+        };
+        self.persist(&snapshot).await;
+        Some(snapshot)
+    }
+
+    async fn get(&self, id: &str) -> Option<JobRecord> {
+        self.jobs.read().await.get(id).cloned()
+    }
+
+    fn forget_worker(&self, id: &str) {
+        if let Ok(mut w) = self.workers.lock() {
+            w.remove(id);
+        }
+    }
+}
+
+/// Load persisted jobs, re-queue unfinished ones, and start the retention
+/// sweep. Called once at startup.
+pub async fn start(fleet: Arc<CortexState>) {
+    let Some(dir) = fleet.audio_jobs.dir().cloned() else {
+        spawn_sweeper(fleet);
+        return;
+    };
+    let mut resumed = Vec::new();
+    if let Ok(mut entries) = tokio::fs::read_dir(&dir).await {
+        while let Ok(Some(entry)) = entries.next_entry().await {
+            let path = entry.path();
+            if path.extension().and_then(|e| e.to_str()) != Some("json") {
+                continue;
+            }
+            let record: JobRecord = match tokio::fs::read(&path)
+                .await
+                .ok()
+                .and_then(|b| serde_json::from_slice(&b).ok())
+            {
+                Some(r) => r,
+                None => {
+                    tracing::warn!(path = %path.display(), "skipping unreadable audio job record");
+                    continue;
+                }
+            };
+            if !record.job.status.is_terminal() {
+                resumed.push(record.job.id.clone());
+            }
+            fleet
+                .audio_jobs
+                .jobs
+                .write()
+                .await
+                .insert(record.job.id.clone(), record);
+        }
+    }
+    for id in resumed {
+        let Some(record) = fleet
+            .audio_jobs
+            .update(&id, |r| {
+                r.job.status = JobStatus::Queued;
+                r.job.started_at = None;
+            })
+            .await
+        else {
+            continue;
+        };
+        // The reservation died with the old process: take a fresh one, and
+        // fail the job if the budget no longer allows it.
+        let guard = match reserve(&fleet, record.owner.as_ref(), &record.request).await {
+            Ok((g, rate)) => {
+                fleet
+                    .audio_jobs
+                    .update(&id, |r| r.tokens_per_second = rate)
+                    .await;
+                g
+            }
+            Err(env) => {
+                fail(&fleet, &id, envelope_error(&env)).await;
+                continue;
+            }
+        };
+        tracing::info!(job = %id, "re-queued audio job after restart");
+        spawn_worker(Arc::clone(&fleet), id, guard);
+    }
+    spawn_sweeper(fleet);
+}
+
+fn spawn_sweeper(fleet: Arc<CortexState>) {
+    tokio::spawn(async move {
+        loop {
+            tokio::time::sleep(SWEEP_INTERVAL).await;
+            sweep_expired(&fleet).await;
+        }
+    });
+}
+
+/// Delete finished jobs older than the retention window, with their audio.
+pub async fn sweep_expired(fleet: &CortexState) {
+    let jobs = &fleet.audio_jobs;
+    let cutoff = now_secs().saturating_sub(jobs.config.retention_hours * 3600);
+    let expired: Vec<String> = jobs
+        .jobs
+        .read()
+        .await
+        .values()
+        .filter(|r| r.job.status.is_terminal())
+        .filter(|r| r.job.completed_at.unwrap_or(r.job.created_at) < cutoff)
+        .map(|r| r.job.id.clone())
+        .collect();
+    for id in expired {
+        jobs.jobs.write().await.remove(&id);
+        if let Ok(mut m) = jobs.memory_audio.lock() {
+            m.remove(&id);
+        }
+        for path in [jobs.record_path(&id), jobs.audio_path(&id)]
+            .into_iter()
+            .flatten()
+        {
+            let _ = tokio::fs::remove_file(path).await;
+        }
+        tracing::debug!(job = %id, "expired audio job removed");
+    }
+}
+
+/// Routes for the job API.
+pub fn routes() -> Router<Arc<CortexState>> {
+    Router::new()
+        .route(JOBS_PATH, get(list_jobs).post(submit))
+        .route(
+            "/v1/audio/generations/{id}",
+            get(get_job).delete(cancel_job),
+        )
+        .route("/v1/audio/generations/{id}/content", get(get_content))
+}
+
+/// `POST /v1/audio/generations` — validate, reserve budget, queue.
+async fn submit(
+    State(fleet): State<Arc<CortexState>>,
+    headers: HeaderMap,
+    body: Bytes,
+) -> Response {
+    let request: AudioGenerationRequest = match serde_json::from_slice(&body) {
+        Ok(r) => r,
+        Err(e) => {
+            return invalid_params(format!("request body is not a valid audio request: {e}"));
+        }
+    };
+    if let Err(msg) = request.validate_shape() {
+        return invalid_params(msg);
+    }
+
+    // The model must be one this fleet knows. Resolving it fully would
+    // cold-load it at submit time, so only check that it exists here; the
+    // worker resolves (and loads) when the job actually runs.
+    let model = fleet.catalogue.resolve_alias(&request.model).to_string();
+    let known = fleet.catalogue.get(&model).is_some()
+        || fleet
+            .nodes
+            .read()
+            .await
+            .values()
+            .any(|n| n.models.contains_key(&model));
+    if !known {
+        return crate::error::envelope_response(OpenAiError::new(
+            404,
+            "invalid_request_error",
+            "model_not_found",
+            format!(
+                "model '{}' not found on any node and not in catalogue",
+                request.model
+            ),
+        ));
+    }
+    if let Some(profile) = fleet.catalogue.get(&model)
+        && let Some(limits) = &profile.audio
+        && let Err(msg) = request.validate_limits(limits)
+    {
+        return invalid_params(msg);
+    }
+
+    let owner = crate::metering::principal_from_headers(&headers);
+    let mut request = request;
+    request.model = model.clone();
+    let (guard, tokens_per_second) = match reserve(&fleet, owner.as_ref(), &request).await {
+        Ok(r) => r,
+        Err(env) => return crate::error::envelope_response(env),
+    };
+
+    let id = format!("audiogen_{}", uuid::Uuid::new_v4().simple());
+    let job = AudioJob {
+        id: id.clone(),
+        object: JOB_OBJECT.into(),
+        status: JobStatus::Queued,
+        model,
+        created_at: now_secs(),
+        started_at: None,
+        completed_at: None,
+        duration: request.duration,
+        result: None,
+        usage: None,
+        error: None,
+    };
+    let record = JobRecord {
+        job: job.clone(),
+        request,
+        owner,
+        tokens_per_second,
+    };
+    fleet
+        .audio_jobs
+        .jobs
+        .write()
+        .await
+        .insert(id.clone(), record.clone());
+    fleet.audio_jobs.persist(&record).await;
+    metrics::counter!("cortex_audio_jobs_submitted_total", "model" => job.model.clone())
+        .increment(1);
+    tracing::info!(job = %id, model = %job.model, "audio job queued");
+
+    spawn_worker(Arc::clone(&fleet), id, guard);
+    (StatusCode::ACCEPTED, Json(job)).into_response()
+}
+
+/// `GET /v1/audio/generations` — the caller's jobs, newest first.
+async fn list_jobs(State(fleet): State<Arc<CortexState>>, headers: HeaderMap) -> Response {
+    let caller = crate::metering::principal_from_headers(&headers);
+    let mut data: Vec<AudioJob> = match &caller {
+        None => Vec::new(),
+        Some(p) => fleet
+            .audio_jobs
+            .jobs
+            .read()
+            .await
+            .values()
+            .filter(|r| {
+                r.owner
+                    .as_ref()
+                    .is_some_and(|o| o.account_id == p.account_id)
+            })
+            .map(|r| r.job.clone())
+            .collect(),
+    };
+    data.sort_by(|a, b| b.created_at.cmp(&a.created_at).then(b.id.cmp(&a.id)));
+    Json(AudioJobList {
+        object: "list".into(),
+        data,
+    })
+    .into_response()
+}
+
+/// `GET /v1/audio/generations/{id}`.
+async fn get_job(
+    State(fleet): State<Arc<CortexState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    match visible_record(&fleet, &headers, &id).await {
+        Some(r) => Json(r.job).into_response(),
+        None => job_not_found(&id),
+    }
+}
+
+/// `GET /v1/audio/generations/{id}/content` — the rendered audio.
+async fn get_content(
+    State(fleet): State<Arc<CortexState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    let Some(record) = visible_record(&fleet, &headers, &id).await else {
+        return job_not_found(&id);
+    };
+    if record.job.status != JobStatus::Succeeded {
+        return crate::error::envelope_response(OpenAiError::new(
+            409,
+            "invalid_request_error",
+            "job_not_complete",
+            format!(
+                "audio job '{id}' is {}; content is available once it has succeeded",
+                status_word(record.job.status)
+            ),
+        ));
+    }
+    let bytes = match fleet.audio_jobs.audio_path(&id) {
+        Some(path) => tokio::fs::read(&path).await.ok(),
+        None => fleet.audio_jobs.memory_audio(&id),
+    };
+    let Some(bytes) = bytes else {
+        return crate::error::envelope_response(OpenAiError::new(
+            410,
+            "invalid_request_error",
+            "content_expired",
+            format!("the audio for job '{id}' is no longer stored"),
+        ));
+    };
+    let mut resp = Response::new(Body::from(bytes));
+    resp.headers_mut()
+        .insert(header::CONTENT_TYPE, HeaderValue::from_static("audio/wav"));
+    if let Ok(v) = HeaderValue::from_str(&format!("attachment; filename=\"{id}.wav\"")) {
+        resp.headers_mut().insert(header::CONTENT_DISPOSITION, v);
+    }
+    resp
+}
+
+/// `DELETE /v1/audio/generations/{id}` — cancel a queued or running job.
+/// Cancelling a finished job is a no-op that returns it unchanged.
+async fn cancel_job(
+    State(fleet): State<Arc<CortexState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    let Some(record) = visible_record(&fleet, &headers, &id).await else {
+        return job_not_found(&id);
+    };
+    if record.job.status.is_terminal() {
+        return Json(record.job).into_response();
+    }
+    // Aborting the worker drops its reservation guard, which releases the
+    // budget. A render already running on a neuron finishes there unseen.
+    let handle = fleet
+        .audio_jobs
+        .workers
+        .lock()
+        .ok()
+        .and_then(|mut w| w.remove(&id));
+    if let Some(h) = handle {
+        h.abort();
+    }
+    let updated = fleet
+        .audio_jobs
+        .update(&id, |r| {
+            r.job.status = JobStatus::Cancelled;
+            r.job.completed_at = Some(now_secs());
+        })
+        .await;
+    metrics::counter!("cortex_audio_jobs_cancelled_total", "model" => record.job.model.clone())
+        .increment(1);
+    tracing::info!(job = %id, "audio job cancelled");
+    match updated {
+        Some(r) => Json(r.job).into_response(),
+        None => job_not_found(&id),
+    }
+}
+
+/// A job the caller may see: owned by the caller's account, or anonymous.
+async fn visible_record(fleet: &CortexState, headers: &HeaderMap, id: &str) -> Option<JobRecord> {
+    let record = fleet.audio_jobs.get(id).await?;
+    let caller = crate::metering::principal_from_headers(headers);
+    match (&record.owner, &caller) {
+        (None, _) => Some(record),
+        (Some(owner), Some(caller)) if owner.account_id == caller.account_id => Some(record),
+        _ => None,
+    }
+}
+
+fn spawn_worker(fleet: Arc<CortexState>, id: String, guard: ReservationGuard) {
+    let jobs_fleet = Arc::clone(&fleet);
+    let task_id = id.clone();
+    let handle = tokio::spawn(async move {
+        run_job(&jobs_fleet, &task_id, guard).await;
+        jobs_fleet.audio_jobs.forget_worker(&task_id);
+    });
+    if let Ok(mut w) = fleet.audio_jobs.workers.lock() {
+        // The task may already have finished (and tried to forget itself)
+        // before this insert; only track it while it is still running.
+        if !handle.is_finished() {
+            w.insert(id, handle);
+        }
+    }
+}
+
+/// Outcome of one dispatch to a neuron.
+enum Dispatch {
+    Done(Bytes, NeuronAudioResult),
+    /// The model is busy; try again after the given wait.
+    Busy(Duration),
+    /// The request itself was refused; retrying will not help.
+    Rejected(Value),
+    /// The node failed or was unreachable; another attempt may succeed.
+    Failed(Value),
+}
+
+async fn run_job(fleet: &Arc<CortexState>, id: &str, guard: ReservationGuard) {
+    let jobs = &fleet.audio_jobs;
+    let Ok(_slot) = Arc::clone(&jobs.slots).acquire_owned().await else {
+        return;
+    };
+    let Some(record) = jobs
+        .update(id, |r| {
+            if !r.job.status.is_terminal() {
+                r.job.status = JobStatus::Running;
+                r.job.started_at = Some(now_secs());
+            }
+        })
+        .await
+    else {
+        return;
+    };
+    // Cancelled while it waited for a slot.
+    if record.job.status != JobStatus::Running {
+        return;
+    }
+    let model = record.job.model.clone();
+    let deadline = Instant::now() + Duration::from_secs(jobs.config.queue_timeout_secs);
+    let mut attempts = 0u32;
+    let mut last_error = error_value(
+        "api_error",
+        "audio_generation_failed",
+        "the audio job could not be dispatched",
+    );
+
+    while attempts < jobs.config.max_attempts.max(1) {
+        let outcome = dispatch_once(fleet, &record).await;
+        match outcome {
+            Dispatch::Done(bytes, result) => {
+                complete(fleet, id, &record, bytes, result, guard).await;
+                return;
+            }
+            Dispatch::Busy(wait) => {
+                if Instant::now() + wait > deadline {
+                    last_error = error_value(
+                        "rate_limit_error",
+                        "queue_timeout",
+                        "the model stayed busy for longer than the job queue timeout",
+                    );
+                    break;
+                }
+                tokio::time::sleep(wait).await;
+            }
+            Dispatch::Rejected(err) => {
+                last_error = err;
+                break;
+            }
+            Dispatch::Failed(err) => {
+                attempts += 1;
+                tracing::warn!(job = %id, model = %model, attempt = attempts, error = %err, "audio dispatch failed");
+                last_error = err;
+                if attempts < jobs.config.max_attempts {
+                    tokio::time::sleep(Duration::from_secs(2)).await;
+                }
+            }
+        }
+    }
+    drop(guard); // releases the reservation: nothing was rendered
+    fail(fleet, id, last_error).await;
+}
+
+async fn dispatch_once(fleet: &Arc<CortexState>, record: &JobRecord) -> Dispatch {
+    let route = match crate::router::resolve(fleet, &record.job.model).await {
+        Ok(r) => r,
+        Err(e) => {
+            let err = error_value(e.broad_type(), e.code(), &e.to_string());
+            return match e.retry_after_secs() {
+                Some(secs) => Dispatch::Busy(Duration::from_secs(secs.max(1))),
+                None if e.http_status() < 500 => Dispatch::Rejected(err),
+                None => Dispatch::Failed(err),
+            };
+        }
+    };
+    crate::handlers::touch_model(fleet, &route.node_name, &route.resolved_model_id).await;
+
+    let mut body = record.request.clone();
+    body.model = route.resolved_model_id.clone();
+    let url = format!("{}{JOBS_PATH}", route.endpoint);
+    let mut req = fleet.audio_jobs.http.post(&url).json(&body);
+    if let Some(owner) = &record.owner {
+        req = req
+            .header(HEADER_ACCOUNT_ID, &owner.account_id)
+            .header(HEADER_KEY_ID, &owner.key_id);
+    }
+    let labels = [
+        ("model", route.resolved_model_id.clone()),
+        ("node", route.node_name.clone()),
+    ];
+    metrics::counter!("cortex_requests_total", &labels).increment(1);
+    if route.cold_start {
+        metrics::counter!("cortex_cold_starts_total", &labels).increment(1);
+    }
+    let start = Instant::now();
+    let resp = match req.send().await {
+        Ok(r) => r,
+        Err(e) => {
+            metrics::counter!("cortex_request_errors_total", &labels).increment(1);
+            return Dispatch::Failed(error_value(
+                "api_error",
+                "upstream_unreachable",
+                &format!("failed to reach the serving node: {e}"),
+            ));
+        }
+    };
+    let status = resp.status();
+    let retry_after = resp
+        .headers()
+        .get(header::RETRY_AFTER)
+        .and_then(|v| v.to_str().ok())
+        .and_then(|v| v.trim().parse::<u64>().ok())
+        .map(|s| Duration::from_secs(s.max(1)));
+    let result_header = resp
+        .headers()
+        .get(RESULT_HEADER)
+        .and_then(|v| v.to_str().ok())
+        .map(str::to_string);
+    let bytes = match resp.bytes().await {
+        Ok(b) => b,
+        Err(e) => {
+            metrics::counter!("cortex_request_errors_total", &labels).increment(1);
+            return Dispatch::Failed(error_value(
+                "api_error",
+                "upstream_read_failed",
+                &format!("failed to read the serving node's response: {e}"),
+            ));
+        }
+    };
+    metrics::histogram!("cortex_audio_render_seconds", &labels)
+        .record(start.elapsed().as_secs_f64());
+
+    if status.is_success() {
+        let parsed = result_header
+            .as_deref()
+            .and_then(|h| serde_json::from_str::<NeuronAudioResult>(h).ok());
+        return match parsed {
+            Some(result) => Dispatch::Done(bytes, result),
+            None => Dispatch::Failed(error_value(
+                "api_error",
+                "upstream_bad_response",
+                "the serving node returned audio without its result metadata",
+            )),
+        };
+    }
+    metrics::counter!("cortex_request_errors_total", &labels).increment(1);
+    if status.as_u16() == 429 || status.as_u16() == 503 {
+        return Dispatch::Busy(retry_after.unwrap_or(DEFAULT_BUSY_WAIT));
+    }
+    let err = serde_json::from_slice::<Value>(&bytes)
+        .ok()
+        .and_then(|v| v.get("error").cloned())
+        .unwrap_or_else(|| {
+            error_value(
+                "api_error",
+                "upstream_error",
+                &format!("the serving node answered {status}"),
+            )
+        });
+    if status.is_client_error() {
+        Dispatch::Rejected(err)
+    } else {
+        Dispatch::Failed(err)
+    }
+}
+
+async fn complete(
+    fleet: &Arc<CortexState>,
+    id: &str,
+    record: &JobRecord,
+    bytes: Bytes,
+    result: NeuronAudioResult,
+    guard: ReservationGuard,
+) {
+    let jobs = &fleet.audio_jobs;
+    // Cancelled while the render was in flight: drop the result, release
+    // the budget, and leave the job cancelled.
+    if jobs.get(id).await.map(|r| r.job.status) == Some(JobStatus::Cancelled) {
+        drop(guard);
+        return;
+    }
+    let size = bytes.len() as u64;
+    match jobs.audio_path(id) {
+        Some(path) => {
+            if let Err(e) = tokio::fs::write(&path, &bytes).await {
+                drop(guard);
+                fail(
+                    fleet,
+                    id,
+                    error_value(
+                        "api_error",
+                        "storage_failed",
+                        &format!("could not store the rendered audio: {e}"),
+                    ),
+                )
+                .await;
+                return;
+            }
+        }
+        None => jobs.keep_in_memory(id, bytes),
+    }
+
+    let units = result.usage.helexa_audio_units;
+    match &record.owner {
+        Some(owner) => {
+            // An owned job always carries the rate it was reserved at.
+            let rate = record
+                .tokens_per_second
+                .unwrap_or(crate::rates::LOCAL_DEFAULT_TOKENS_PER_AUDIO_SECOND);
+            crate::metering::settle_audio_usage(owner, guard, &fleet.served_usage, units, rate)
+        }
+        None => drop(guard),
+    }
+    jobs.update(id, |r| {
+        r.job.status = JobStatus::Succeeded;
+        r.job.completed_at = Some(now_secs());
+        r.job.result = Some(AudioResult {
+            duration: result.duration,
+            sample_rate: result.sample_rate,
+            channels: result.channels,
+            seed: result.seed,
+            format: "wav".into(),
+            bytes: size,
+        });
+        r.job.usage = Some(result.usage.clone());
+    })
+    .await;
+    metrics::counter!("cortex_audio_jobs_succeeded_total", "model" => record.job.model.clone())
+        .increment(1);
+    tracing::info!(job = %id, model = %record.job.model, seconds = result.duration, "audio job succeeded");
+}
+
+async fn fail(fleet: &CortexState, id: &str, error: Value) {
+    let updated = fleet
+        .audio_jobs
+        .update(id, |r| {
+            r.job.status = JobStatus::Failed;
+            r.job.completed_at = Some(now_secs());
+            r.job.error = Some(error.clone());
+        })
+        .await;
+    if let Some(r) = updated {
+        metrics::counter!("cortex_audio_jobs_failed_total", "model" => r.job.model.clone())
+            .increment(1);
+        tracing::warn!(job = %id, model = %r.job.model, error = %error, "audio job failed");
+    }
+}
+
+/// Reserve budget for a job at its model's current rate (#386). Returns the
+/// guard and the rate it reserved at; anonymous jobs are unmetered.
+#[allow(clippy::result_large_err)] // the Err is the #63 envelope; see metering::reserve_or_reject
+async fn reserve(
+    fleet: &Arc<CortexState>,
+    owner: Option<&Principal>,
+    request: &AudioGenerationRequest,
+) -> Result<(ReservationGuard, Option<u64>), OpenAiError> {
+    let Some(p) = owner else {
+        return Ok((
+            ReservationGuard::anonymous(Arc::clone(&fleet.entitlements)),
+            None,
+        ));
+    };
+    let Some(rate) = fleet.rates.audio_tokens_per_second(&request.model).await else {
+        return Err(OpenAiError::service_unavailable(
+            format!("no audio rate is configured for model '{}'", request.model),
+            Some(5),
+        ));
+    };
+    let default = fleet
+        .catalogue
+        .get(&request.model)
+        .and_then(|m| m.audio.as_ref())
+        .map(|l| l.default_duration);
+    let seconds = crate::metering::audio_reservation_seconds(request.duration, default);
+    let guard = crate::metering::reserve_or_reject(
+        Arc::clone(&fleet.entitlements),
+        p,
+        crate::metering::audio_seconds_to_tokens(seconds, rate),
+    )
+    .await?;
+    Ok((guard, Some(rate)))
+}
+
+impl AudioJobs {
+    fn keep_in_memory(&self, id: &str, bytes: Bytes) {
+        if let Ok(mut m) = self.memory_audio.lock() {
+            m.insert(id.to_string(), bytes);
+        }
+    }
+
+    fn memory_audio(&self, id: &str) -> Option<Vec<u8>> {
+        self.memory_audio
+            .lock()
+            .ok()
+            .and_then(|m| m.get(id).map(|b| b.to_vec()))
+    }
+}
+
+fn now_secs() -> u64 {
+    SystemTime::now()
+        .duration_since(UNIX_EPOCH)
+        .map(|d| d.as_secs())
+        .unwrap_or(0)
+}
+
+fn status_word(s: JobStatus) -> &'static str {
+    match s {
+        JobStatus::Queued => "queued",
+        JobStatus::Running => "running",
+        JobStatus::Succeeded => "succeeded",
+        JobStatus::Failed => "failed",
+        JobStatus::Cancelled => "cancelled",
+    }
+}
+
+fn error_value(typ: &str, code: &str, message: &str) -> Value {
+    serde_json::json!({"type": typ, "code": code, "message": message})
+}
+
+fn envelope_error(env: &OpenAiError) -> Value {
+    env.body()
+        .get("error")
+        .cloned()
+        .unwrap_or_else(|| error_value("api_error", "budget_rejected", "budget reservation failed"))
+}
+
+fn invalid_params(message: impl Into<String>) -> Response {
+    crate::error::envelope_response(OpenAiError::new(
+        400,
+        "invalid_request_error",
+        "invalid_audio_params",
+        format!("invalid audio generation parameters: {}", message.into()),
+    ))
+}
+
+fn job_not_found(id: &str) -> Response {
+    crate::error::envelope_response(OpenAiError::new(
+        404,
+        "invalid_request_error",
+        "job_not_found",
+        format!("no audio job '{id}'"),
+    ))
+}
@@ -24,6 +24,7 @@ pub fn api_routes() -> Router<Arc<CortexState>> {
        .route("/v1/completions", post(completions))
        .route("/v1/responses", post(responses))
        .route("/v1/images/generations", post(images_generations))
+        .merge(crate::audio_jobs::routes())
        .route("/v1/systemone", post(systemone))
        .route("/v1/models", get(list_models))
        .route("/v1/messages", post(anthropic_messages))
@@ -977,6 +978,8 @@ async fn list_models(State(fleet): State<Arc<CortexState>>) -> Json<Value> {
                // `cost` stays operator-set and flows from the catalogue.
                limit: None,
                cost: profile.cost.clone(),
+                audio: profile.audio.clone(),
+                audio_tokens_per_second: None,
                // Runtime-detected — will be OR-ed in Pass 2 from neuron data.
                tool_call: false,
                reasoning: false,
@@ -1059,6 +1062,8 @@ async fn list_models(State(fleet): State<Arc<CortexState>>) -> Json<Value> {
                    capabilities: entry.capabilities.clone(),
                    limit: entry.limit.clone(),
                    cost: None,
+                    audio: None,
+                    audio_tokens_per_second: None,
                    tool_call: entry.tool_call,
                    reasoning: entry.reasoning,
                    max_model_len: None,
@@ -1128,6 +1133,8 @@ async fn list_models(State(fleet): State<Arc<CortexState>>) -> Json<Value> {
                    capabilities: discovered.get(model_id).cloned().unwrap_or_default(),
                    limit: None,
                    cost: None,
+                    audio: None,
+                    audio_tokens_per_second: None,
                    tool_call: false,
                    reasoning: false,
                    max_model_len: None,
@@ -1169,6 +1176,8 @@ async fn list_models(State(fleet): State<Arc<CortexState>>) -> Json<Value> {
                capabilities: target_entry.capabilities,
                limit: target_entry.limit.clone(),
                cost: target_entry.cost.clone(),
+                audio: target_entry.audio.clone(),
+                audio_tokens_per_second: None,
                tool_call: target_entry.tool_call,
                reasoning: target_entry.reasoning,
                max_model_len: None,
@@ -1186,10 +1195,19 @@ async fn list_models(State(fleet): State<Arc<CortexState>>) -> Json<Value> {
    // from each entry's now-settled `limit`, so vLLM-convention clients
    // (Hermes Agent et al.) can read the window without knowing helexa's
    // `limit` schema.
+    //
+    // Audio models also carry their current rate (#386), so a client can
+    // estimate a job's cost before submitting it. An alias is priced as
+    // the model it names.
+    let rates = fleet.rates.snapshot().await;
    let data: Vec<Value> = entries
        .values_mut()
        .map(|e| {
            e.sync_flat_limit();
+            if e.audio.is_some() {
+                let priced_as = catalogue.resolve_alias(&e.id);
+                e.audio_tokens_per_second = rates.audio_tokens_per_second(priced_as);
+            }
            json!(e)
        })
        .collect();
@@ -1455,7 +1473,7 @@ fn context_length_exceeded_response(
 }

 /// Update `last_accessed` timestamp for a model on a node (drives LRU eviction).
-async fn touch_model(fleet: &CortexState, node_name: &str, model_id: &str) {
+pub(crate) async fn touch_model(fleet: &CortexState, node_name: &str, model_id: &str) {
    let mut nodes = fleet.nodes.write().await;
    if let Some(node) = nodes.get_mut(node_name)
        && let Some(entry) = node.models.get_mut(model_id)
@@ -1,4 +1,5 @@
 pub mod anthropic_sse;
+pub mod audio_jobs;
 pub mod auth;
 pub mod entitlements_chain;
 pub mod entitlements_local;
@@ -10,6 +11,7 @@ pub mod metering;
 pub mod metrics;
 pub mod poller;
 pub mod proxy;
+pub mod rates;
 pub mod router;
 pub mod served_usage;
 pub mod state;
@@ -52,6 +54,15 @@ pub async fn run(config: GatewayConfig) -> Result<()> {
        poller::poll_loop(poller_fleet).await;
    });

+    // Per-model rates (#386): keep the cached table in step with upstream.
+    if config.upstream.enabled {
+        rates::spawn_refresh(Arc::clone(&fleet.rates), &config.upstream);
+    }
+
+    // Async audio jobs (#382): reload persisted jobs, re-queue unfinished
+    // ones, start the retention sweep.
+    audio_jobs::start(Arc::clone(&fleet)).await;
+
    // Spawn the evictor (reacts to VRAM pressure events from the router).
    let evictor_fleet = Arc::clone(&fleet);
    tokio::spawn(async move {
@@ -228,6 +228,42 @@ pub fn settle_image_usage(
    guard.settle(tokens);
 }

+/// Audio-seconds to reserve for a job: the requested duration, else the
+/// model's advertised default, else a minute. Settle corrects to the actual
+/// length the neuron reports.
+pub fn audio_reservation_seconds(requested: Option<f64>, model_default: Option<f64>) -> f64 {
+    requested
+        .or(model_default)
+        .filter(|d| d.is_finite() && *d > 0.0)
+        .unwrap_or(60.0)
+}
+
+/// Convert audio-seconds to budget tokens at a per-model rate (#386),
+/// rounding up.
+pub fn audio_seconds_to_tokens(seconds: f64, tokens_per_second: u64) -> u64 {
+    (seconds * tokens_per_second as f64).ceil() as u64
+}
+
+/// Record per-principal audio spend and settle the reservation with the
+/// audio-seconds actually generated.
+pub fn settle_audio_usage(
+    principal: &Principal,
+    guard: ReservationGuard,
+    served_usage: &crate::served_usage::ServedUsage,
+    audio_seconds: f64,
+    tokens_per_second: u64,
+) {
+    let labels = [
+        ("account", principal.account_id.clone()),
+        ("key", principal.key_id.clone()),
+    ];
+    metrics::counter!("cortex_spend_audio_milliseconds_total", &labels)
+        .increment((audio_seconds * 1000.0).round() as u64);
+    let tokens = audio_seconds_to_tokens(audio_seconds, tokens_per_second);
+    served_usage.add(&principal.account_id, &principal.key_id, tokens);
+    guard.settle(tokens);
+}
+
 /// Map a [`BudgetError`] to the #63 envelope. The provider chose the window
 /// semantics; this only translates them to HTTP.
 fn budget_error_to_envelope(err: BudgetError) -> OpenAiError {
@@ -0,0 +1,208 @@
+//! Per-model rates for work metered in units other than tokens (#386).
+//!
+//! The budget ledger counts tokens. Audio jobs are metered in seconds of
+//! generated audio, converted to ledger tokens at a per-model rate. The
+//! rates are configuration held by the clearing house (helexa-upstream's
+//! `model_rates` table), so pricing changes without a deploy: cortex
+//! fetches the table from `/authz/v1/rates`, caches it, and refreshes it in
+//! the background.
+//!
+//! Resolution: the model's own rate, else the unit's `"*"` default. A model
+//! with neither is not priced, and cortex refuses its metered work rather
+//! than serving it free. Until the first fetch succeeds nothing is priced,
+//! for the same reason.
+//!
+//! A cortex running without upstream has no clearing house to ask. It
+//! prices every audio-second at [`LOCAL_DEFAULT_TOKENS_PER_AUDIO_SECOND`],
+//! which only matters for the operator's own local key caps.
+
+use cortex_core::config::UpstreamClientConfig;
+use serde::Deserialize;
+use std::collections::HashMap;
+use std::sync::Arc;
+use std::time::Duration;
+use tokio::sync::RwLock;
+
+/// Unit name for one second of generated audio, as stored upstream.
+pub const AUDIO_SECOND: &str = "audio_second";
+
+/// The default-rate model id.
+pub const ANY_MODEL: &str = "*";
+
+/// Audio rate used when this cortex runs without an upstream authority.
+pub const LOCAL_DEFAULT_TOKENS_PER_AUDIO_SECOND: u64 = 1000;
+
+/// How often the rate table is refreshed from upstream.
+const REFRESH_INTERVAL: Duration = Duration::from_secs(60);
+
+/// Retry interval while no table has loaded yet.
+const INITIAL_RETRY: Duration = Duration::from_secs(5);
+
+/// A snapshot of the rate table: `(unit, model_id) → tokens per unit`.
+#[derive(Debug, Clone, Default)]
+pub struct RateTable {
+    rates: HashMap<(String, String), u64>,
+}
+
+impl RateTable {
+    /// The table a cortex without upstream uses.
+    pub fn local_default() -> Self {
+        let mut t = Self::default();
+        t.set(
+            AUDIO_SECOND,
+            ANY_MODEL,
+            LOCAL_DEFAULT_TOKENS_PER_AUDIO_SECOND,
+        );
+        t
+    }
+
+    pub fn set(&mut self, unit: &str, model_id: &str, tokens_per_unit: u64) {
+        self.rates
+            .insert((unit.to_string(), model_id.to_string()), tokens_per_unit);
+    }
+
+    /// The model's rate for `unit`, else the unit's default, else `None`.
+    pub fn rate(&self, unit: &str, model_id: &str) -> Option<u64> {
+        self.rates
+            .get(&(unit.to_string(), model_id.to_string()))
+            .or_else(|| self.rates.get(&(unit.to_string(), ANY_MODEL.to_string())))
+            .copied()
+    }
+
+    /// Ledger tokens per second of audio for `model_id`.
+    pub fn audio_tokens_per_second(&self, model_id: &str) -> Option<u64> {
+        self.rate(AUDIO_SECOND, model_id)
+    }
+}
+
+/// The live rate table, held in `CortexState`.
+pub struct Rates {
+    table: RwLock<RateTable>,
+}
+
+impl Rates {
+    /// Rates for a cortex with no upstream: the local default.
+    pub fn local() -> Self {
+        Self {
+            table: RwLock::new(RateTable::local_default()),
+        }
+    }
+
+    /// Rates for a cortex with upstream: empty (nothing priced) until the
+    /// first fetch lands.
+    pub fn upstream() -> Self {
+        Self {
+            table: RwLock::new(RateTable::default()),
+        }
+    }
+
+    pub async fn snapshot(&self) -> RateTable {
+        self.table.read().await.clone()
+    }
+
+    pub async fn replace(&self, table: RateTable) {
+        *self.table.write().await = table;
+    }
+
+    pub async fn audio_tokens_per_second(&self, model_id: &str) -> Option<u64> {
+        self.table.read().await.audio_tokens_per_second(model_id)
+    }
+}
+
+#[derive(Deserialize)]
+struct RatesResp {
+    data: Vec<RateRow>,
+}
+
+#[derive(Deserialize)]
+struct RateRow {
+    model_id: String,
+    unit: String,
+    tokens_per_unit: i64,
+}
+
+/// Fetch the full rate table from upstream's `/authz/v1/rates`.
+pub async fn fetch(
+    client: &reqwest::Client,
+    base_url: &str,
+    bearer: &str,
+) -> anyhow::Result<RateTable> {
+    let url = format!("{}/authz/v1/rates", base_url.trim_end_matches('/'));
+    let resp = client
+        .get(&url)
+        .bearer_auth(bearer)
+        .send()
+        .await?
+        .error_for_status()?;
+    let body: RatesResp = resp.json().await?;
+    let mut table = RateTable::default();
+    for row in body.data {
+        // The database forbids non-positive rates; skip one rather than
+        // trust it if that ever changes.
+        if let Ok(tokens) = u64::try_from(row.tokens_per_unit)
+            && tokens > 0
+        {
+            table.set(&row.unit, &row.model_id, tokens);
+        }
+    }
+    Ok(table)
+}
+
+/// Keep `rates` in step with upstream. Retries quickly until the first
+/// table loads, then refreshes every minute. A failed refresh keeps the
+/// last good table.
+pub fn spawn_refresh(rates: Arc<Rates>, cfg: &UpstreamClientConfig) {
+    let client = reqwest::Client::builder()
+        .timeout(Duration::from_secs(cfg.timeout_secs.max(1)))
+        .build()
+        .expect("failed to build rates HTTP client");
+    let url = cfg.url.clone();
+    let bearer = cfg.bearer.clone();
+    tokio::spawn(async move {
+        let mut loaded = false;
+        loop {
+            match fetch(&client, &url, &bearer).await {
+                Ok(table) => {
+                    if !loaded {
+                        tracing::info!("model rates loaded from upstream");
+                    }
+                    loaded = true;
+                    rates.replace(table).await;
+                }
+                Err(e) => tracing::warn!(error = %e, "model rate refresh failed"),
+            }
+            tokio::time::sleep(if loaded {
+                REFRESH_INTERVAL
+            } else {
+                INITIAL_RETRY
+            })
+            .await;
+        }
+    });
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn model_rate_beats_default_and_absent_unit_is_unpriced() {
+        let mut t = RateTable::default();
+        assert_eq!(t.audio_tokens_per_second("m"), None);
+        t.set(AUDIO_SECOND, ANY_MODEL, 1000);
+        assert_eq!(t.audio_tokens_per_second("m"), Some(1000));
+        t.set(AUDIO_SECOND, "m", 250);
+        assert_eq!(t.audio_tokens_per_second("m"), Some(250));
+        assert_eq!(t.audio_tokens_per_second("other"), Some(1000));
+        assert_eq!(t.rate("some_other_unit", "m"), None);
+    }
+
+    #[test]
+    fn local_default_prices_every_audio_model() {
+        let t = RateTable::local_default();
+        assert_eq!(
+            t.audio_tokens_per_second("anything"),
+            Some(LOCAL_DEFAULT_TOKENS_PER_AUDIO_SECOND)
+        );
+    }
+}
@@ -663,6 +663,7 @@ mod tests {
            limit: None,
            cost: None,
            capabilities: vec![],
+            audio: None,
        }
    }

@@ -52,6 +52,11 @@ pub struct CortexState {
    /// rather than an edge — it hid a GGUF that two of three nodes could
    /// describe perfectly well.
    pub capability_probe_attempts: RwLock<HashMap<(String, String), std::time::Instant>>,
+    /// Async audio generation jobs (#382).
+    pub audio_jobs: crate::audio_jobs::AudioJobs,
+    /// Per-model rates for non-token units (#386), from upstream when it
+    /// is enabled, else the local default.
+    pub rates: Arc<crate::rates::Rates>,
 }

 impl CortexState {
@@ -131,6 +136,12 @@ impl CortexState {
            served_usage: Arc::new(crate::served_usage::ServedUsage::new()),
            discovered_capabilities: RwLock::new(HashMap::new()),
            capability_probe_attempts: RwLock::new(HashMap::new()),
+            audio_jobs: crate::audio_jobs::AudioJobs::new(&config.audio_jobs),
+            rates: Arc::new(if config.upstream.enabled {
+                crate::rates::Rates::upstream()
+            } else {
+                crate::rates::Rates::local()
+            }),
        }
    }
 }
@@ -87,6 +87,7 @@ async fn test_alias_resolves_in_chat_completions() {
        models_config: models_path.to_string_lossy().to_string(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -176,6 +177,7 @@ async fn test_aliases_surface_in_v1_models() {
        models_config: models_path.to_string_lossy().to_string(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -268,6 +270,7 @@ async fn test_alias_falls_through_for_unmapped_model() {
        models_config: models_path.to_string_lossy().to_string(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -0,0 +1,820 @@
+//! Async audio generation jobs (#382): submit → poll → content, cancel,
+//! ownership, retries, busy back-off, and restart recovery, against a
+//! scriptable mock neuron.
+
+use axum::Router;
+use axum::extract::Path;
+use axum::http::{HeaderMap, StatusCode};
+use axum::response::{IntoResponse, Response};
+use axum::routing::{get, post};
+use axum::{Json, body::Bytes};
+use cortex_core::audio::{AudioUsage, NeuronAudioResult, RESULT_HEADER};
+use cortex_core::config::{
+    ApiKeyConfig, AudioJobsConfig, EntitlementsConfig, EvictionSettings, EvictionStrategy,
+    GatewayConfig, GatewaySettings, NeuronEndpoint,
+};
+use cortex_core::node::{ModelEntry, ModelStatus};
+use cortex_gateway::state::CortexState;
+use serde_json::{Value, json};
+use std::sync::Arc;
+use std::sync::atomic::{AtomicUsize, Ordering};
+use std::time::Duration;
+use tokio::net::TcpListener;
+
+const MODEL: &str = "music-model";
+const WAV: &[u8] = b"RIFF\x24\x00\x00\x00WAVEfmt fake-audio-bytes";
+
+/// How the mock neuron answers `POST /v1/audio/generations`.
+#[derive(Clone, Copy)]
+enum Mode {
+    /// Render immediately.
+    Ok,
+    /// Answer 503 + Retry-After for the first N calls, then render.
+    BusyThenOk(usize),
+    /// Answer 500 for the first N calls, then render.
+    FailThenOk(usize),
+    /// Always 500.
+    AlwaysFail,
+    /// Always 422 wrong_modality.
+    Reject,
+    /// Sleep this long, then render.
+    Slow(Duration),
+    /// Always 503.
+    AlwaysBusy,
+}
+
+struct Mock {
+    url: String,
+    calls: Arc<AtomicUsize>,
+    /// Account header seen on each render call.
+    accounts: Arc<std::sync::Mutex<Vec<Option<String>>>>,
+}
+
+async fn spawn_mock(mode: Mode) -> Mock {
+    let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let url = format!("http://{}", listener.local_addr().unwrap());
+    let calls = Arc::new(AtomicUsize::new(0));
+    let accounts = Arc::new(std::sync::Mutex::new(Vec::new()));
+    let endpoint = url.clone();
+    let (c, a) = (Arc::clone(&calls), Arc::clone(&accounts));
+    let app = Router::new()
+        .route(
+            "/models/{id}/endpoint",
+            get(move |Path(_id): Path<String>| {
+                let url = endpoint.clone();
+                async move { Json(json!({"url": url})) }
+            }),
+        )
+        .route(
+            "/v1/audio/generations",
+            post(move |headers: HeaderMap, body: Bytes| {
+                let (c, a) = (Arc::clone(&c), Arc::clone(&a));
+                async move { render(mode, &c, &a, &headers, &body).await }
+            }),
+        );
+    tokio::spawn(async move { axum::serve(listener, app).await.unwrap() });
+    Mock {
+        url,
+        calls,
+        accounts,
+    }
+}
+
+async fn render(
+    mode: Mode,
+    calls: &AtomicUsize,
+    accounts: &std::sync::Mutex<Vec<Option<String>>>,
+    headers: &HeaderMap,
+    body: &[u8],
+) -> Response {
+    let n = calls.fetch_add(1, Ordering::SeqCst);
+    accounts.lock().unwrap().push(
+        headers
+            .get(cortex_core::entitlements::HEADER_ACCOUNT_ID)
+            .and_then(|v| v.to_str().ok())
+            .map(str::to_string),
+    );
+    let busy = || {
+        (
+            StatusCode::SERVICE_UNAVAILABLE,
+            [("retry-after", "1")],
+            Json(json!({"error": {"type": "rate_limit_error", "code": "rate_limit_exceeded", "message": "busy"}})),
+        )
+            .into_response()
+    };
+    let fail = || {
+        (
+            StatusCode::INTERNAL_SERVER_ERROR,
+            Json(json!({"error": {"type": "api_error", "code": "inference_failed", "message": "boom"}})),
+        )
+            .into_response()
+    };
+    match mode {
+        Mode::BusyThenOk(k) if n < k => return busy(),
+        Mode::AlwaysBusy => return busy(),
+        Mode::FailThenOk(k) if n < k => return fail(),
+        Mode::AlwaysFail => return fail(),
+        Mode::Reject => {
+            return (
+                StatusCode::UNPROCESSABLE_ENTITY,
+                Json(json!({"error": {"type": "invalid_request_error", "code": "wrong_modality", "message": "not an audio model"}})),
+            )
+                .into_response();
+        }
+        Mode::Slow(d) => tokio::time::sleep(d).await,
+        _ => {}
+    }
+    let req: Value = serde_json::from_slice(body).unwrap();
+    let duration = req["duration"].as_f64().unwrap_or(30.0);
+    let result = NeuronAudioResult {
+        duration,
+        sample_rate: 48_000,
+        channels: 2,
+        seed: req["seed"].as_u64().unwrap_or(7),
+        usage: AudioUsage {
+            helexa_audio_units: duration,
+            helexa_timing: None,
+        },
+    };
+    (
+        StatusCode::OK,
+        [
+            ("content-type", "audio/wav".to_string()),
+            (RESULT_HEADER, serde_json::to_string(&result).unwrap()),
+        ],
+        WAV,
+    )
+        .into_response()
+}
+
+fn config(neuron: &str, dir: &std::path::Path) -> GatewayConfig {
+    GatewayConfig {
+        gateway: GatewaySettings {
+            listen: "127.0.0.1:0".into(),
+            metrics_listen: "127.0.0.1:0".into(),
+        },
+        eviction: EvictionSettings {
+            strategy: EvictionStrategy::Lru,
+            defrag_after_cycles: 0,
+        },
+        neurons: vec![NeuronEndpoint {
+            name: "mock-node".into(),
+            endpoint: neuron.to_string(),
+        }],
+        models_config: "/dev/null".into(),
+        entitlements: EntitlementsConfig {
+            require_auth: false,
+            keys: ["alice", "bob"]
+                .iter()
+                .map(|who| ApiKeyConfig {
+                    key: format!("key-{who}"),
+                    account_id: format!("acct-{who}"),
+                    key_id: None,
+                    hard_cap: None,
+                    window: Default::default(),
+                })
+                .collect(),
+        },
+        upstream: Default::default(),
+        audio_jobs: AudioJobsConfig {
+            dir: dir.to_string_lossy().into_owned(),
+            queue_timeout_secs: 20,
+            ..Default::default()
+        },
+    }
+}
+
+async fn gateway(neuron: &str, dir: &std::path::Path) -> (Arc<CortexState>, String) {
+    let fleet = Arc::new(CortexState::from_config(&config(neuron, dir)));
+    seed_model(&fleet).await;
+    let url = serve(&fleet).await;
+    (fleet, url)
+}
+
+async fn seed_model(fleet: &CortexState) {
+    let mut nodes = fleet.nodes.write().await;
+    let node = nodes.get_mut("mock-node").unwrap();
+    node.healthy = true;
+    node.models.insert(
+        MODEL.into(),
+        ModelEntry {
+            id: MODEL.into(),
+            status: ModelStatus::Loaded,
+            last_accessed: None,
+            vram_estimate_mb: Some(8000),
+            capabilities: vec!["audio".into()],
+            tool_call: false,
+            reasoning: false,
+            limit: None,
+            servable: None,
+            reasoning_budget: Vec::new(),
+        },
+    );
+}
+
+async fn serve(fleet: &Arc<CortexState>) -> String {
+    let app = cortex_gateway::build_app(Arc::clone(fleet));
+    let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let addr = listener.local_addr().unwrap();
+    tokio::spawn(async move { axum::serve(listener, app).await.unwrap() });
+    format!("http://{addr}")
+}
+
+async fn submit(gw: &str, key: Option<&str>, body: Value) -> reqwest::Response {
+    let mut req = reqwest::Client::new()
+        .post(format!("{gw}/v1/audio/generations"))
+        .json(&body);
+    if let Some(k) = key {
+        req = req.bearer_auth(k);
+    }
+    req.send().await.unwrap()
+}
+
+async fn get_job(gw: &str, key: Option<&str>, id: &str) -> reqwest::Response {
+    let mut req = reqwest::Client::new().get(format!("{gw}/v1/audio/generations/{id}"));
+    if let Some(k) = key {
+        req = req.bearer_auth(k);
+    }
+    req.send().await.unwrap()
+}
+
+/// Poll until the job reaches a terminal status (or panic after `secs`).
+async fn wait_terminal(gw: &str, key: Option<&str>, id: &str, secs: u64) -> Value {
+    let deadline = std::time::Instant::now() + Duration::from_secs(secs);
+    loop {
+        let job: Value = get_job(gw, key, id).await.json().await.unwrap();
+        if matches!(
+            job["status"].as_str(),
+            Some("succeeded" | "failed" | "cancelled")
+        ) {
+            return job;
+        }
+        assert!(
+            std::time::Instant::now() < deadline,
+            "job {id} never finished: {job}"
+        );
+        tokio::time::sleep(Duration::from_millis(50)).await;
+    }
+}
+
+fn song() -> Value {
+    json!({"model": MODEL, "prompt": "warm lofi", "duration": 12.5, "seed": 42})
+}
+
+#[tokio::test]
+async fn submit_poll_fetch_content() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+
+    let resp = submit(&gw, Some("key-alice"), song()).await;
+    assert_eq!(resp.status(), 202);
+    let job: Value = resp.json().await.unwrap();
+    assert_eq!(job["object"], "audio.generation");
+    assert_eq!(job["status"], "queued");
+    assert_eq!(job["model"], MODEL);
+    let id = job["id"].as_str().unwrap().to_string();
+    assert!(id.starts_with("audiogen_"));
+
+    let done = wait_terminal(&gw, Some("key-alice"), &id, 10).await;
+    assert_eq!(done["status"], "succeeded", "{done}");
+    assert_eq!(done["result"]["duration"], 12.5);
+    assert_eq!(done["result"]["sample_rate"], 48_000);
+    assert_eq!(done["result"]["seed"], 42);
+    assert_eq!(done["result"]["bytes"], WAV.len());
+    assert_eq!(done["usage"]["helexa_audio_units"], 12.5);
+
+    let content = reqwest::Client::new()
+        .get(format!("{gw}/v1/audio/generations/{id}/content"))
+        .bearer_auth("key-alice")
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(content.status(), 200);
+    assert_eq!(content.headers()["content-type"], "audio/wav");
+    assert_eq!(content.bytes().await.unwrap().as_ref(), WAV);
+
+    // The neuron saw the submitting account, for fair-share admission.
+    assert_eq!(
+        mock.accounts.lock().unwrap().as_slice(),
+        &[Some("acct-alice".to_string())]
+    );
+    // Record and audio are on disk.
+    assert!(dir.path().join(format!("{id}.json")).exists());
+    assert!(dir.path().join(format!("{id}.wav")).exists());
+}
+
+#[tokio::test]
+async fn content_before_completion_is_409() {
+    let mock = spawn_mock(Mode::Slow(Duration::from_secs(3))).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let id = job["id"].as_str().unwrap();
+    let resp = reqwest::get(format!("{gw}/v1/audio/generations/{id}/content"))
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 409);
+    let body: Value = resp.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "job_not_complete");
+}
+
+#[tokio::test]
+async fn jobs_are_private_to_their_account() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+
+    let job: Value = submit(&gw, Some("key-alice"), song())
+        .await
+        .json()
+        .await
+        .unwrap();
+    let id = job["id"].as_str().unwrap().to_string();
+    wait_terminal(&gw, Some("key-alice"), &id, 10).await;
+
+    // Another account, and an anonymous caller, cannot see it.
+    assert_eq!(get_job(&gw, Some("key-bob"), &id).await.status(), 404);
+    assert_eq!(get_job(&gw, None, &id).await.status(), 404);
+    let content = reqwest::Client::new()
+        .get(format!("{gw}/v1/audio/generations/{id}/content"))
+        .bearer_auth("key-bob")
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(content.status(), 404);
+    let cancel = reqwest::Client::new()
+        .delete(format!("{gw}/v1/audio/generations/{id}"))
+        .bearer_auth("key-bob")
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(cancel.status(), 404);
+
+    // Listings are per account.
+    let list = |key: &'static str| {
+        let gw = gw.clone();
+        async move {
+            reqwest::Client::new()
+                .get(format!("{gw}/v1/audio/generations"))
+                .bearer_auth(key)
+                .send()
+                .await
+                .unwrap()
+                .json::<Value>()
+                .await
+                .unwrap()
+        }
+    };
+    let alice = list("key-alice").await;
+    assert_eq!(alice["object"], "list");
+    assert_eq!(alice["data"].as_array().unwrap().len(), 1);
+    assert_eq!(alice["data"][0]["id"], id.as_str());
+    assert_eq!(list("key-bob").await["data"].as_array().unwrap().len(), 0);
+}
+
+#[tokio::test]
+async fn anonymous_jobs_are_reachable_by_id_but_never_listed() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let id = job["id"].as_str().unwrap().to_string();
+    let done = wait_terminal(&gw, None, &id, 10).await;
+    assert_eq!(done["status"], "succeeded");
+    let list: Value = reqwest::get(format!("{gw}/v1/audio/generations"))
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(list["data"].as_array().unwrap().len(), 0);
+}
+
+#[tokio::test]
+async fn cancel_running_job() {
+    let mock = spawn_mock(Mode::Slow(Duration::from_secs(5))).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, Some("key-alice"), song())
+        .await
+        .json()
+        .await
+        .unwrap();
+    let id = job["id"].as_str().unwrap().to_string();
+
+    // Wait until it is running on the neuron.
+    let deadline = std::time::Instant::now() + Duration::from_secs(5);
+    while mock.calls.load(Ordering::SeqCst) == 0 {
+        assert!(std::time::Instant::now() < deadline);
+        tokio::time::sleep(Duration::from_millis(20)).await;
+    }
+    let resp = reqwest::Client::new()
+        .delete(format!("{gw}/v1/audio/generations/{id}"))
+        .bearer_auth("key-alice")
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 200);
+    let cancelled: Value = resp.json().await.unwrap();
+    assert_eq!(cancelled["status"], "cancelled");
+
+    // It stays cancelled: the slow render finishing must not resurrect it.
+    tokio::time::sleep(Duration::from_secs(6)).await;
+    let job: Value = get_job(&gw, Some("key-alice"), &id)
+        .await
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(job["status"], "cancelled");
+    assert!(job.get("result").is_none());
+}
+
+#[tokio::test]
+async fn busy_neuron_is_waited_on_without_spending_attempts() {
+    // Three busy answers exceed max_attempts (2), so this only succeeds if
+    // busy answers don't count as attempts.
+    let mock = spawn_mock(Mode::BusyThenOk(3)).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let id = job["id"].as_str().unwrap().to_string();
+    let done = wait_terminal(&gw, None, &id, 15).await;
+    assert_eq!(done["status"], "succeeded", "{done}");
+    assert_eq!(mock.calls.load(Ordering::SeqCst), 4);
+}
+
+#[tokio::test]
+async fn failing_node_is_retried_then_succeeds() {
+    let mock = spawn_mock(Mode::FailThenOk(1)).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let done = wait_terminal(&gw, None, job["id"].as_str().unwrap(), 15).await;
+    assert_eq!(done["status"], "succeeded", "{done}");
+    assert_eq!(mock.calls.load(Ordering::SeqCst), 2);
+}
+
+#[tokio::test]
+async fn failing_node_exhausts_attempts() {
+    let mock = spawn_mock(Mode::AlwaysFail).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let done = wait_terminal(&gw, None, job["id"].as_str().unwrap(), 15).await;
+    assert_eq!(done["status"], "failed");
+    assert_eq!(done["error"]["code"], "inference_failed");
+    assert_eq!(mock.calls.load(Ordering::SeqCst), 2);
+}
+
+#[tokio::test]
+async fn neuron_rejection_fails_without_retry() {
+    let mock = spawn_mock(Mode::Reject).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let done = wait_terminal(&gw, None, job["id"].as_str().unwrap(), 10).await;
+    assert_eq!(done["status"], "failed");
+    assert_eq!(done["error"]["code"], "wrong_modality");
+    assert_eq!(mock.calls.load(Ordering::SeqCst), 1);
+}
+
+#[tokio::test]
+async fn submit_validation() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+
+    let resp = submit(&gw, None, json!({"model": MODEL, "prompt": ""})).await;
+    assert_eq!(resp.status(), 400);
+    let body: Value = resp.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "invalid_audio_params");
+
+    let resp = submit(
+        &gw,
+        None,
+        json!({"model": MODEL, "prompt": "x", "response_format": "ogg"}),
+    )
+    .await;
+    assert_eq!(resp.status(), 400);
+
+    let resp = submit(&gw, None, json!({"model": "nope", "prompt": "x"})).await;
+    assert_eq!(resp.status(), 404);
+    let body: Value = resp.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "model_not_found");
+
+    let resp = submit(&gw, None, json!({"prompt": "x"})).await;
+    assert_eq!(resp.status(), 400);
+
+    // Nothing reached the neuron.
+    assert_eq!(mock.calls.load(Ordering::SeqCst), 0);
+}
+
+#[tokio::test]
+async fn unknown_job_is_404() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_fleet, gw) = gateway(&mock.url, dir.path()).await;
+    let resp = get_job(&gw, None, "audiogen_doesnotexist").await;
+    assert_eq!(resp.status(), 404);
+    let body: Value = resp.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "job_not_found");
+}
+
+#[tokio::test]
+async fn jobs_survive_a_restart() {
+    let dir = tempfile::tempdir().unwrap();
+
+    // First cortex: one job finishes, one is stuck behind a busy model.
+    let ok = spawn_mock(Mode::Ok).await;
+    let (_a, gw_a) = gateway(&ok.url, dir.path()).await;
+    let done: Value = submit(&gw_a, Some("key-alice"), song())
+        .await
+        .json()
+        .await
+        .unwrap();
+    let done_id = done["id"].as_str().unwrap().to_string();
+    wait_terminal(&gw_a, Some("key-alice"), &done_id, 10).await;
+
+    let busy = spawn_mock(Mode::AlwaysBusy).await;
+    let (_b, gw_b) = gateway(&busy.url, dir.path()).await;
+    let stuck: Value = submit(&gw_b, Some("key-alice"), song())
+        .await
+        .json()
+        .await
+        .unwrap();
+    let stuck_id = stuck["id"].as_str().unwrap().to_string();
+    let deadline = std::time::Instant::now() + Duration::from_secs(5);
+    while busy.calls.load(Ordering::SeqCst) == 0 {
+        assert!(std::time::Instant::now() < deadline);
+        tokio::time::sleep(Duration::from_millis(20)).await;
+    }
+
+    // A fresh cortex over the same directory, with a healthy neuron.
+    let healthy = spawn_mock(Mode::Ok).await;
+    let fleet = Arc::new(CortexState::from_config(&config(&healthy.url, dir.path())));
+    seed_model(&fleet).await;
+    cortex_gateway::audio_jobs::start(Arc::clone(&fleet)).await;
+    let gw = serve(&fleet).await;
+
+    // The finished job and its audio are still there.
+    let job: Value = get_job(&gw, Some("key-alice"), &done_id)
+        .await
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(job["status"], "succeeded");
+    let content = reqwest::Client::new()
+        .get(format!("{gw}/v1/audio/generations/{done_id}/content"))
+        .bearer_auth("key-alice")
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(content.bytes().await.unwrap().as_ref(), WAV);
+
+    // The unfinished one was re-queued and now completes.
+    let resumed = wait_terminal(&gw, Some("key-alice"), &stuck_id, 10).await;
+    assert_eq!(resumed["status"], "succeeded", "{resumed}");
+}
+
+#[tokio::test]
+async fn expired_jobs_are_swept() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let mut cfg = config(&mock.url, dir.path());
+    cfg.audio_jobs.retention_hours = 0;
+    let fleet = Arc::new(CortexState::from_config(&cfg));
+    seed_model(&fleet).await;
+    let gw = serve(&fleet).await;
+
+    let job: Value = submit(&gw, None, song()).await.json().await.unwrap();
+    let id = job["id"].as_str().unwrap().to_string();
+    wait_terminal(&gw, None, &id, 10).await;
+    // completed_at has one-second resolution; let it fall behind the cutoff.
+    tokio::time::sleep(Duration::from_millis(1100)).await;
+
+    cortex_gateway::audio_jobs::sweep_expired(&fleet).await;
+    assert_eq!(get_job(&gw, None, &id).await.status(), 404);
+    assert!(!dir.path().join(format!("{id}.json")).exists());
+    assert!(!dir.path().join(format!("{id}.wav")).exists());
+}
+
+// ── Per-model rates (#386) ───────────────────────────────────────────
+
+/// A gateway whose keys are capped at `cap` tokens and whose audio rate
+/// table is `rates` (model id → tokens per second; "*" is the default).
+async fn priced_gateway(
+    neuron: &str,
+    dir: &std::path::Path,
+    cap: Option<u64>,
+    rates: &[(&str, u64)],
+) -> (Arc<CortexState>, String) {
+    let mut cfg = config(neuron, dir);
+    for k in &mut cfg.entitlements.keys {
+        k.hard_cap = cap;
+    }
+    let fleet = Arc::new(CortexState::from_config(&cfg));
+    let mut table = cortex_gateway::rates::RateTable::default();
+    for (model, rate) in rates {
+        table.set(cortex_gateway::rates::AUDIO_SECOND, model, *rate);
+    }
+    fleet.rates.replace(table).await;
+    seed_model(&fleet).await;
+    let url = serve(&fleet).await;
+    (fleet, url)
+}
+
+async fn spent(fleet: &CortexState, who: &str) -> u64 {
+    let principal = cortex_core::entitlements::Principal {
+        account_id: format!("acct-{who}"),
+        key_id: format!("acct-{who}"),
+    };
+    fleet
+        .entitlements
+        .snapshot(&principal)
+        .await
+        .map(|s| s.spent)
+        .unwrap_or(0)
+}
+
+#[tokio::test]
+async fn reservation_uses_the_models_rate() {
+    // A 12.5 s song against a 10,000-token cap: refused at 1000 tokens/s
+    // (12,500), accepted at 500 tokens/s (6,250).
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (_f, gw) = priced_gateway(&mock.url, dir.path(), Some(10_000), &[("*", 1000)]).await;
+    let resp = submit(&gw, Some("key-alice"), song()).await;
+    assert_eq!(resp.status(), 429, "{:?}", resp.text().await);
+
+    let dir = tempfile::tempdir().unwrap();
+    let (_f, gw) = priced_gateway(
+        &mock.url,
+        dir.path(),
+        Some(10_000),
+        &[("*", 1000), (MODEL, 500)],
+    )
+    .await;
+    assert_eq!(submit(&gw, Some("key-alice"), song()).await.status(), 202);
+}
+
+#[tokio::test]
+async fn unpriced_model_is_refused_for_metered_callers_only() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    // An empty table: what a cortex with upstream sees before its first fetch.
+    let (_f, gw) = priced_gateway(&mock.url, dir.path(), None, &[]).await;
+    let resp = submit(&gw, Some("key-alice"), song()).await;
+    assert_eq!(resp.status(), 503);
+    let body: Value = resp.json().await.unwrap();
+    assert!(
+        body["error"]["message"]
+            .as_str()
+            .unwrap()
+            .contains("no audio rate"),
+        "{body}"
+    );
+    // Anonymous work is not metered, so it does not need a price.
+    assert_eq!(submit(&gw, None, song()).await.status(), 202);
+}
+
+#[tokio::test]
+async fn settles_at_the_rate_reserved_even_if_it_changes_mid_job() {
+    let mock = spawn_mock(Mode::Slow(Duration::from_millis(800))).await;
+    let dir = tempfile::tempdir().unwrap();
+    let (fleet, gw) = priced_gateway(&mock.url, dir.path(), None, &[(MODEL, 300)]).await;
+    let job: Value = submit(&gw, Some("key-alice"), song())
+        .await
+        .json()
+        .await
+        .unwrap();
+    // Reprice while the render is in flight.
+    let mut table = cortex_gateway::rates::RateTable::default();
+    table.set(cortex_gateway::rates::AUDIO_SECOND, MODEL, 1);
+    fleet.rates.replace(table).await;
+
+    let done = wait_terminal(&gw, Some("key-alice"), job["id"].as_str().unwrap(), 10).await;
+    assert_eq!(done["status"], "succeeded");
+    // 12.5 s × 300 tokens/s, settled asynchronously.
+    let deadline = std::time::Instant::now() + Duration::from_secs(5);
+    loop {
+        let s = spent(&fleet, "alice").await;
+        if s == 3750 {
+            break;
+        }
+        assert!(std::time::Instant::now() < deadline, "spent {s}, want 3750");
+        tokio::time::sleep(Duration::from_millis(20)).await;
+    }
+}
+
+#[tokio::test]
+async fn rates_are_fetched_from_upstream_with_the_client_bearer() {
+    let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let url = format!("http://{}", listener.local_addr().unwrap());
+    let app = Router::new().route(
+        "/authz/v1/rates",
+        get(|headers: HeaderMap| async move {
+            if headers.get("authorization").and_then(|v| v.to_str().ok())
+                != Some("Bearer op-secret")
+            {
+                return StatusCode::UNAUTHORIZED.into_response();
+            }
+            Json(json!({"data": [
+                {"model_id": "*", "unit": "audio_second", "tokens_per_unit": 1000},
+                {"model_id": MODEL, "unit": "audio_second", "tokens_per_unit": 40},
+                {"model_id": "bad", "unit": "audio_second", "tokens_per_unit": 0}
+            ]}))
+            .into_response()
+        }),
+    );
+    tokio::spawn(async move { axum::serve(listener, app).await.unwrap() });
+
+    let client = reqwest::Client::new();
+    let table = cortex_gateway::rates::fetch(&client, &url, "op-secret")
+        .await
+        .unwrap();
+    assert_eq!(table.audio_tokens_per_second(MODEL), Some(40));
+    assert_eq!(table.audio_tokens_per_second("other"), Some(1000));
+    // A non-positive rate is skipped, falling back to the default.
+    assert_eq!(table.audio_tokens_per_second("bad"), Some(1000));
+    assert!(
+        cortex_gateway::rates::fetch(&client, &url, "wrong")
+            .await
+            .is_err()
+    );
+}
+
+#[tokio::test]
+async fn models_endpoint_advertises_audio_limits_and_rate() {
+    let mock = spawn_mock(Mode::Ok).await;
+    let dir = tempfile::tempdir().unwrap();
+    let catalogue = dir.path().join("models.toml");
+    std::fs::write(
+        &catalogue,
+        format!(
+            r#"
+[[models]]
+id = "{MODEL}"
+harness = "candle"
+quant = "q8_0"
+
+[models.audio]
+min_duration = 10.0
+max_duration = 600.0
+default_duration = 30.0
+vocals = true
+sample_rate = 48000
+channels = 2
+max_steps = 20
+
+[aliases]
+"helexa/music" = "{MODEL}"
+"#
+        ),
+    )
+    .unwrap();
+    let mut cfg = config(&mock.url, dir.path());
+    cfg.models_config = catalogue.to_string_lossy().into_owned();
+    let fleet = Arc::new(CortexState::from_config(&cfg));
+    let mut table = cortex_gateway::rates::RateTable::default();
+    table.set(cortex_gateway::rates::AUDIO_SECOND, MODEL, 77);
+    fleet.rates.replace(table).await;
+    seed_model(&fleet).await;
+    let gw = serve(&fleet).await;
+
+    let models: Value = reqwest::get(format!("{gw}/v1/models"))
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    let find = |id: &str| {
+        models["data"]
+            .as_array()
+            .unwrap()
+            .iter()
+            .find(|m| m["id"] == id)
+            .cloned()
+            .unwrap_or_else(|| panic!("{id} missing from {models}"))
+    };
+    for id in [MODEL, "helexa/music"] {
+        let m = find(id);
+        assert_eq!(m["audio"]["max_duration"], 600.0, "{m}");
+        assert_eq!(m["audio"]["vocals"], true);
+        assert_eq!(m["audio_tokens_per_second"], 77, "{m}");
+    }
+
+    // The catalogue's limits are enforced at submit.
+    let resp = submit(
+        &gw,
+        None,
+        json!({"model": MODEL, "prompt": "x", "duration": 5.0}),
+    )
+    .await;
+    assert_eq!(resp.status(), 400);
+    // An alias is accepted and resolved to the model.
+    let resp = submit(&gw, None, json!({"model": "helexa/music", "prompt": "x"})).await;
+    assert_eq!(resp.status(), 202);
+    let job: Value = resp.json().await.unwrap();
+    assert_eq!(job["model"], MODEL);
+}
@@ -106,6 +106,7 @@ async fn spawn_gateway(neuron_url: &str, entitlements: EntitlementsConfig) -> St
        models_config: "/dev/null".into(),
        entitlements,
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -82,6 +82,7 @@ async fn spawn_gateway(neuron_url: &str, key: ApiKeyConfig) -> (Arc<CortexState>
            keys: vec![key],
        },
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = Arc::new(CortexState::from_config(&config));
    {
@@ -498,6 +498,7 @@ pub async fn spawn_gateway_with_state(mock_url: &str) -> (Arc<CortexState>, Stri
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -559,6 +560,7 @@ pub async fn two_neuron_fleet(endpoint_a: &str, endpoint_b: &str) -> Arc<CortexS
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    Arc::new(CortexState::from_config(&config))
 }
@@ -90,6 +90,7 @@ async fn error_response_no_healthy_nodes() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(cortex_gateway::state::CortexState::from_config(&config));
@@ -82,6 +82,7 @@ fn make_fleet_with_catalogue(
        models_config: path.to_string_lossy().into_owned(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    (Arc::new(CortexState::from_config(&config)), path)
 }
@@ -144,6 +145,7 @@ fn make_fleet(endpoint: &str, defrag_after: u32) -> Arc<CortexState> {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    Arc::new(CortexState::from_config(&config))
 }
@@ -88,6 +88,7 @@ async fn fleet_with(big_healthy: bool, big_devices: usize) -> Arc<CortexState> {
        models_config: cat.to_string_lossy().into_owned(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = Arc::new(CortexState::from_config(&config));
    {
@@ -165,6 +165,7 @@ async fn spawn_keyed_gateway(neuron_url: &str, hard_cap: u64) -> String {
            }],
        },
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = Arc::new(CortexState::from_config(&config));
    {
@@ -89,6 +89,7 @@ async fn two_neuron_fleet(endpoint_a: &str, endpoint_b: &str) -> Arc<CortexState
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    Arc::new(CortexState::from_config(&config))
 }
@@ -54,6 +54,7 @@ async fn spawn_metered_gateway(neuron_url: &str) -> (Arc<CortexState>, String) {
            }],
        },
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -162,6 +163,7 @@ async fn anonymous_request_records_no_spend() {
        models_config: "/dev/null".into(),
        entitlements: EntitlementsConfig::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = Arc::new(CortexState::from_config(&config));
    {
@@ -204,6 +204,7 @@ async fn test_capacity_gauges_exported_from_health_poll() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = std::sync::Arc::new(cortex_gateway::state::CortexState::from_config(&config));
    cortex_gateway::poller::poll_once(&fleet).await;
@@ -67,6 +67,7 @@ harness = "candle"
        models_config: cat_path.to_string_lossy().into_owned(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -59,6 +59,7 @@ capabilities = ["text"]
        models_config: cat_path.to_string_lossy().into_owned(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -33,6 +33,7 @@ async fn test_poller_discovers_models() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -86,6 +87,7 @@ async fn test_poller_updates_gateway_models_endpoint() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -159,6 +161,7 @@ async fn test_models_endpoint_unions_capabilities_across_nodes() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -223,6 +226,7 @@ async fn test_poller_marks_unreachable_node_unhealthy() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -278,6 +282,7 @@ async fn test_poller_removes_stale_models() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -310,6 +315,7 @@ async fn test_poller_removes_stale_models() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet2 = Arc::new(CortexState::from_config(&config2));
@@ -397,6 +403,7 @@ async fn test_poller_captures_activation_from_health() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -443,6 +450,7 @@ async fn test_poller_parses_recovering_status() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };

    let fleet = Arc::new(CortexState::from_config(&config));
@@ -76,6 +76,7 @@ async fn spawn_gateway(neuron: &str, context: usize) -> String {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = Arc::new(CortexState::from_config(&config));
    {
@@ -119,6 +119,7 @@ async fn test_no_healthy_nodes() {
        models_config: "/dev/null".into(),
        entitlements: Default::default(),
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let fleet = std::sync::Arc::new(cortex_gateway::state::CortexState::from_config(&config));

@@ -119,6 +119,7 @@ async fn spawn_gateway(neuron_url: &str, default_alias: bool, hard_cap: Option<u
        models_config: "/dev/null".into(),
        entitlements,
        upstream: Default::default(),
+        audio_jobs: Default::default(),
    };
    let mut state = CortexState::from_config(&config);
    if default_alias {
@@ -153,17 +153,29 @@ pub trait Scenario: Send + Sync {
    fn prompt_size(&self) -> u32;

    /// Whether this scenario should run against the given model. The
-    /// default runs against everything except image-generation models
-    /// (a chat request at an image model is a 422 `wrong_modality`);
-    /// modality-specific scenarios override this.
+    /// default runs against chat models only: an image-generation model
+    /// or a decision model (#333) answers a chat request with a 422
+    /// `wrong_modality`. Modality-specific scenarios override this.
    fn applies_to(&self, model: &ModelInfo) -> bool {
-        !model.capabilities.iter().any(|c| c == "image")
+        serves_chat(model)
    }

    /// Issue one shaped request and measure it.
    async fn run(&self, ctx: &RunCtx) -> Result<ScenarioMetrics>;
 }

+/// Capabilities that mark a model as serving an endpoint other than
+/// chat completions.
+const NON_CHAT_MODALITIES: [&str; 3] = ["image", "decision", "audio"];
+
+/// Whether a model answers `/v1/chat/completions`.
+pub fn serves_chat(model: &ModelInfo) -> bool {
+    !model
+        .capabilities
+        .iter()
+        .any(|c| NON_CHAT_MODALITIES.contains(&c.as_str()))
+}
+
 /// Fixed-seed image-latency scenario (#203): one 9-step generation at a
 /// configured square size against `/v1/images/generations`, recording
 /// the server-side phase timing (`usage.helexa_timing`) and the metered
@@ -883,6 +895,35 @@ async fn stream_and_measure_inner(
 mod tests {
    use super::*;

+    fn model(capabilities: &[&str]) -> ModelInfo {
+        serde_json::from_value(serde_json::json!({
+            "id": "m",
+            "harness": "candle",
+            "status": "loaded",
+            "devices": [0],
+            "vram_used_mb": null,
+            "capabilities": capabilities,
+        }))
+        .expect("model info")
+    }
+
+    /// Chat scenarios skip every model that answers chat with
+    /// `wrong_modality`: image generators and decision models alike.
+    #[test]
+    fn chat_scenarios_skip_image_and_decision_models() {
+        assert!(serves_chat(&model(&[])));
+        assert!(serves_chat(&model(&["text", "vision"])));
+        assert!(!serves_chat(&model(&["image"])));
+        assert!(!serves_chat(&model(&["decision"])));
+        assert!(!serves_chat(&model(&["audio"])));
+        let image = ImageLatencyScenario {
+            id: "image:512".into(),
+            side_px: 512,
+        };
+        assert!(image.applies_to(&model(&["image"])));
+        assert!(!image.applies_to(&model(&["decision"])));
+    }
+
    /// A burst must report server-side phase timing, or `queue_wait_ms`
    /// (TTFT minus server prefill, #85) cannot be computed for the very
    /// cells it was designed for, and the prefill-tok/s panel stays
@@ -0,0 +1,173 @@
+//! Async audio generation jobs through the router (#382).
+//!
+//! Submitting a job is ordinary capacity dispatch: the router picks a cortex
+//! that serves the model and forwards the body, and that cortex owns the job
+//! from then on. Every later call names only a job id, which carries no hint
+//! of its cortex, so those calls are asked of each cortex in turn and the
+//! first answer that is not `404` wins. Listing merges every cortex's list.
+//!
+//! Ownership is enforced by the cortex holding the job; the router forwards
+//! the caller's bearer unchanged, exactly as for inference.
+
+use crate::dispatch;
+use crate::error::envelope_response;
+use crate::state::RouterState;
+use axum::Router;
+use axum::body::{Body, Bytes};
+use axum::extract::{Path, State};
+use axum::http::{HeaderMap, Method, StatusCode};
+use axum::response::{IntoResponse, Json, Response};
+use axum::routing::get;
+use cortex_core::audio::{AudioJob, AudioJobList};
+use cortex_core::error_envelope::OpenAiError;
+use std::sync::Arc;
+
+const PATH: &str = "/v1/audio/generations";
+
+pub fn routes() -> Router<Arc<RouterState>> {
+    Router::new()
+        .route(PATH, get(list).post(submit))
+        .route("/v1/audio/generations/{id}", get(get_job).delete(cancel))
+        .route("/v1/audio/generations/{id}/content", get(content))
+}
+
+async fn submit(
+    State(state): State<Arc<RouterState>>,
+    headers: HeaderMap,
+    body: Bytes,
+) -> Response {
+    dispatch::dispatch(&state, PATH, headers, body).await
+}
+
+async fn get_job(
+    State(state): State<Arc<RouterState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    find(&state, Method::GET, &format!("{PATH}/{id}"), &headers, &id).await
+}
+
+async fn cancel(
+    State(state): State<Arc<RouterState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    find(
+        &state,
+        Method::DELETE,
+        &format!("{PATH}/{id}"),
+        &headers,
+        &id,
+    )
+    .await
+}
+
+async fn content(
+    State(state): State<Arc<RouterState>>,
+    headers: HeaderMap,
+    Path(id): Path<String>,
+) -> Response {
+    find(
+        &state,
+        Method::GET,
+        &format!("{PATH}/{id}/content"),
+        &headers,
+        &id,
+    )
+    .await
+}
+
+/// Ask each cortex for the job until one answers with something other than
+/// `404`, and stream that answer back verbatim.
+async fn find(
+    state: &RouterState,
+    method: Method,
+    path: &str,
+    headers: &HeaderMap,
+    id: &str,
+) -> Response {
+    for ep in &state.cortexes {
+        let Some(client) = state.client_for(&ep.name) else {
+            continue;
+        };
+        let url = format!("{}{path}", ep.endpoint);
+        let resp = match client
+            .request(method.clone(), &url)
+            .headers(forward_headers(headers))
+            .send()
+            .await
+        {
+            Ok(r) => r,
+            Err(e) => {
+                tracing::warn!(cortex = %ep.name, url = %url, error = %e, "cortex unreachable");
+                continue;
+            }
+        };
+        if resp.status() == reqwest::StatusCode::NOT_FOUND {
+            continue;
+        }
+        let status =
+            StatusCode::from_u16(resp.status().as_u16()).unwrap_or(StatusCode::BAD_GATEWAY);
+        let mut out = Response::builder().status(status);
+        for (k, v) in resp.headers() {
+            if k == "transfer-encoding" || k == "connection" {
+                continue;
+            }
+            out = out.header(k, v);
+        }
+        return out
+            .body(Body::from_stream(resp.bytes_stream()))
+            .unwrap_or_else(|_| StatusCode::BAD_GATEWAY.into_response());
+    }
+    envelope_response(OpenAiError::new(
+        404,
+        "invalid_request_error",
+        "job_not_found",
+        format!("no audio job '{id}'"),
+    ))
+}
+
+/// Merge every reachable cortex's job list, newest first.
+async fn list(State(state): State<Arc<RouterState>>, headers: HeaderMap) -> Response {
+    let mut data: Vec<AudioJob> = Vec::new();
+    for ep in &state.cortexes {
+        let Some(client) = state.client_for(&ep.name) else {
+            continue;
+        };
+        let url = format!("{}{PATH}", ep.endpoint);
+        let page = client
+            .get(&url)
+            .headers(forward_headers(&headers))
+            .send()
+            .await
+            .ok()
+            .filter(|r| r.status().is_success());
+        let Some(page) = page else {
+            tracing::warn!(cortex = %ep.name, "audio job list unavailable from cortex");
+            continue;
+        };
+        if let Ok(list) = page.json::<AudioJobList>().await {
+            data.extend(list.data);
+        }
+    }
+    data.sort_by(|a, b| b.created_at.cmp(&a.created_at).then(b.id.cmp(&a.id)));
+    Json(AudioJobList {
+        object: "list".into(),
+        data,
+    })
+    .into_response()
+}
+
+fn forward_headers(headers: &HeaderMap) -> HeaderMap {
+    let mut out = HeaderMap::new();
+    for (k, v) in headers {
+        if matches!(
+            k.as_str(),
+            "host" | "content-length" | "connection" | "transfer-encoding"
+        ) {
+            continue;
+        }
+        out.append(k.clone(), v.clone());
+    }
+    out
+}
@@ -168,6 +168,8 @@ mod tests {
            capabilities: vec![],
            limit: None,
            cost: None,
+            audio: None,
+            audio_tokens_per_second: None,
            tool_call: false,
            reasoning: false,
            max_model_len: None,
@@ -14,6 +14,7 @@ pub fn api_routes() -> Router<Arc<RouterState>> {
        .route("/v1/chat/completions", post(chat_completions))
        .route("/v1/completions", post(completions))
        .route("/v1/images/generations", post(images_generations))
+        .merge(crate::audio::routes())
        .route("/v1/systemone", post(systemone))
        .route("/v1/responses", post(responses))
        .route("/v1/messages", post(messages))
@@ -11,6 +11,7 @@
 //! on capacity (epic #69). A background [`poller`] keeps a live
 //! per-cortex topology (#72) that the dispatcher (#73) will route on.

+pub mod audio;
 pub mod catalogue;
 pub mod config;
 pub mod dispatch;
@@ -0,0 +1,187 @@
+//! Audio job lookups through the router (#382): a job id carries no hint of
+//! its cortex, so job calls are asked of each cortex until one answers with
+//! something other than 404, and listings merge every cortex's jobs.
+
+use axum::extract::Path;
+use axum::http::{HeaderMap, StatusCode};
+use axum::response::{IntoResponse, Response};
+use axum::routing::get;
+use axum::{Json, Router};
+use helexa_router::config::{CortexEndpoint, RouterConfig};
+use helexa_router::state::RouterState;
+use serde_json::{Value, json};
+use std::sync::Arc;
+use tokio::net::TcpListener;
+
+/// A cortex that holds exactly one job, `job_id`, created at `created`.
+async fn spawn_cortex(job_id: &'static str, created: u64) -> String {
+    let job = move || {
+        json!({"id": job_id, "object": "audio.generation", "status": "succeeded",
+               "model": "m", "created_at": created})
+    };
+    let app = Router::new()
+        .route(
+            "/v1/audio/generations",
+            get(move |headers: HeaderMap| async move {
+                // Only the bearer's own jobs are listed.
+                if headers.get("authorization").is_none() {
+                    return Json(json!({"object": "list", "data": []})).into_response();
+                }
+                Json(json!({"object": "list", "data": [job()]})).into_response()
+            }),
+        )
+        .route(
+            "/v1/audio/generations/{id}",
+            get(move |Path(id): Path<String>| async move {
+                if id == job_id {
+                    Json(job()).into_response()
+                } else {
+                    not_found()
+                }
+            })
+            .delete(move |Path(id): Path<String>| async move {
+                if id == job_id {
+                    let mut j = job();
+                    j["status"] = json!("cancelled");
+                    Json(j).into_response()
+                } else {
+                    not_found()
+                }
+            }),
+        )
+        .route(
+            "/v1/audio/generations/{id}/content",
+            get(move |Path(id): Path<String>| async move {
+                if id == job_id {
+                    (
+                        StatusCode::OK,
+                        [("content-type", "audio/wav")],
+                        format!("audio-of-{job_id}"),
+                    )
+                        .into_response()
+                } else {
+                    not_found()
+                }
+            }),
+        );
+    let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let addr = listener.local_addr().unwrap();
+    tokio::spawn(async move { axum::serve(listener, app).await.unwrap() });
+    format!("http://{addr}")
+}
+
+fn not_found() -> Response {
+    (
+        StatusCode::NOT_FOUND,
+        Json(json!({"error": {"type": "invalid_request_error", "code": "job_not_found", "message": "no"}})),
+    )
+        .into_response()
+}
+
+async fn router_over(cortexes: Vec<(&str, String)>) -> String {
+    let cfg = RouterConfig {
+        cortexes: cortexes
+            .into_iter()
+            .map(|(name, endpoint)| CortexEndpoint {
+                name: name.into(),
+                endpoint,
+                region: None,
+                tls_ca: None,
+            })
+            .collect(),
+        ..Default::default()
+    };
+    let state = Arc::new(RouterState::from_config(&cfg));
+    let app = helexa_router::handlers::api_routes().with_state(state);
+    let listener = TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let addr = listener.local_addr().unwrap();
+    tokio::spawn(async move { axum::serve(listener, app).await.unwrap() });
+    format!("http://{addr}")
+}
+
+#[tokio::test]
+async fn job_calls_find_the_cortex_that_holds_the_job() {
+    let a = spawn_cortex("audiogen_a", 100).await;
+    let b = spawn_cortex("audiogen_b", 200).await;
+    // An unreachable cortex in the list must be skipped, not fatal.
+    let router = router_over(vec![
+        ("dead", "http://127.0.0.1:1".into()),
+        ("a", a),
+        ("b", b),
+    ])
+    .await;
+    let http = reqwest::Client::new();
+
+    let job: Value = http
+        .get(format!("{router}/v1/audio/generations/audiogen_b"))
+        .send()
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(job["id"], "audiogen_b");
+
+    let content = http
+        .get(format!("{router}/v1/audio/generations/audiogen_b/content"))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(content.status(), 200);
+    assert_eq!(content.headers()["content-type"], "audio/wav");
+    assert_eq!(content.text().await.unwrap(), "audio-of-audiogen_b");
+
+    let cancelled: Value = http
+        .delete(format!("{router}/v1/audio/generations/audiogen_a"))
+        .send()
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(cancelled["status"], "cancelled");
+
+    let missing = http
+        .get(format!("{router}/v1/audio/generations/audiogen_nope"))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(missing.status(), 404);
+    let body: Value = missing.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "job_not_found");
+}
+
+#[tokio::test]
+async fn listing_merges_cortexes_newest_first_and_forwards_the_bearer() {
+    let a = spawn_cortex("audiogen_a", 100).await;
+    let b = spawn_cortex("audiogen_b", 200).await;
+    let router = router_over(vec![("a", a), ("b", b)]).await;
+    let http = reqwest::Client::new();
+
+    let list: Value = http
+        .get(format!("{router}/v1/audio/generations"))
+        .bearer_auth("k")
+        .send()
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    let ids: Vec<&str> = list["data"]
+        .as_array()
+        .unwrap()
+        .iter()
+        .map(|j| j["id"].as_str().unwrap())
+        .collect();
+    assert_eq!(ids, ["audiogen_b", "audiogen_a"]);
+
+    let anon: Value = http
+        .get(format!("{router}/v1/audio/generations"))
+        .send()
+        .await
+        .unwrap()
+        .json()
+        .await
+        .unwrap();
+    assert_eq!(anon["data"].as_array().unwrap().len(), 0);
+}
@@ -37,6 +37,8 @@ fn model_entry(loaded: bool, feasible: bool) -> CortexModelEntry {
        capabilities: vec![],
        limit: None,
        cost: None,
+        audio: None,
+        audio_tokens_per_second: None,
        tool_call: false,
        reasoning: false,
        max_model_len: None,
@@ -0,0 +1,32 @@
+-- ── Per-model rates for non-token units (#386) ───────────────────────
+--
+-- The ledger counts tokens. Work metered in another unit — audio-seconds
+-- for music generation today — is converted into ledger tokens at a
+-- per-model rate kept here, so an operator can price a model, or change
+-- its price, without a deploy. cortex fetches the table from
+-- `/authz/v1/rates` and reserves and settles at the rate it read.
+--
+-- `model_id = '*'` is the default for a unit: it applies to every model
+-- without a row of its own. A unit with no row at all, for the model or
+-- for '*', is not priced, and cortex refuses that work rather than serving
+-- it free.
+CREATE TABLE model_rates (
+    model_id         TEXT NOT NULL,
+    -- 'audio_second' — one second of generated audio.
+    unit             TEXT NOT NULL,
+    -- Ledger tokens charged per unit.
+    tokens_per_unit  BIGINT NOT NULL,
+    description      TEXT,
+    -- Audit: who changed it and when (NULL updated_by = shipped default).
+    updated_at       TIMESTAMPTZ NOT NULL DEFAULT now(),
+    updated_by       UUID REFERENCES users(id) ON DELETE SET NULL,
+
+    PRIMARY KEY (model_id, unit),
+    CONSTRAINT model_rates_unit_known CHECK (unit IN ('audio_second')),
+    CONSTRAINT model_rates_positive CHECK (tokens_per_unit > 0)
+);
+
+-- Placeholder default while music generation is being built; set real
+-- per-model prices with an UPDATE or per-model INSERT.
+INSERT INTO model_rates (model_id, unit, tokens_per_unit, description)
+VALUES ('*', 'audio_second', 1000, 'default audio rate (placeholder)');
@@ -21,7 +21,7 @@ use axum::extract::{Request, State};
 use axum::http::{StatusCode, header};
 use axum::middleware::Next;
 use axum::response::{IntoResponse, Response};
-use axum::routing::post;
+use axum::routing::{get, post};
 use axum::{Extension, Json, Router};
 use cortex_core::error_envelope::OpenAiError;
 use serde::{Deserialize, Serialize};
@@ -42,6 +42,7 @@ pub fn router(state: &AppState) -> Router<AppState> {
        .route("/authz/v1/release", post(release))
        .route("/authz/v1/snapshot", post(snapshot))
        .route("/authz/v1/served-usage", post(served_usage))
+        .route("/authz/v1/rates", get(rates))
        .layer(axum::middleware::from_fn_with_state(
            state.clone(),
            client_auth,
@@ -338,3 +339,23 @@ fn bad_request(msg: &str) -> Response {
        msg,
    ))
 }
+
+// ── rates (#386) ────────────────────────────────────────────────────
+
+#[derive(Serialize)]
+struct RatesResp {
+    data: Vec<crate::rates::ModelRate>,
+}
+
+/// `GET /authz/v1/rates` — every per-model rate, for cortex to cache and
+/// resolve locally. A database error is a 500: cortex keeps its last good
+/// table and refuses unpriced work rather than guessing.
+async fn rates(State(state): State<AppState>) -> Response {
+    match crate::rates::list(&state.pool).await {
+        Ok(data) => Json(RatesResp { data }).into_response(),
+        Err(e) => {
+            tracing::error!(error = %e, "rates query failed");
+            (StatusCode::INTERNAL_SERVER_ERROR, "rates unavailable").into_response()
+        }
+    }
+}
@@ -21,6 +21,7 @@ pub mod email;
 pub mod error;
 pub mod handlers;
 pub mod ledger;
+pub mod rates;
 pub mod reconcile;
 pub mod state;
 pub mod topup;
@@ -0,0 +1,35 @@
+//! Per-model rates for work metered in units other than tokens (#386).
+//!
+//! The ledger only counts tokens; a rate converts another unit (one second
+//! of generated audio) into ledger tokens. Rates live in `model_rates` so
+//! they can change without a deploy. cortex reads the whole table from
+//! `/authz/v1/rates` and resolves a model's rate itself: the model's own
+//! row, else the unit's `'*'` default, else the work is not priced.
+
+use serde::Serialize;
+use sqlx::postgres::PgPool;
+
+/// One row of `model_rates`, as served to cortex.
+#[derive(Debug, Clone, Serialize)]
+pub struct ModelRate {
+    pub model_id: String,
+    pub unit: String,
+    pub tokens_per_unit: i64,
+}
+
+/// Every configured rate.
+pub async fn list(pool: &PgPool) -> Result<Vec<ModelRate>, sqlx::Error> {
+    let rows = sqlx::query(
+        "SELECT model_id, unit, tokens_per_unit FROM model_rates ORDER BY unit, model_id",
+    )
+    .fetch_all(pool)
+    .await?;
+    Ok(rows
+        .into_iter()
+        .map(|r| ModelRate {
+            model_id: sqlx::Row::get(&r, "model_id"),
+            unit: sqlx::Row::get(&r, "unit"),
+            tokens_per_unit: sqlx::Row::get(&r, "tokens_per_unit"),
+        })
+        .collect())
+}
@@ -0,0 +1,73 @@
+//! `model_rates` (#386) against a real PostgreSQL. Gated on
+//! `UPSTREAM_TEST_DATABASE_URL`, like the ledger tests: unset, every test
+//! logs a skip and passes.
+
+use helexa_upstream::db::connect_and_migrate;
+use helexa_upstream::rates;
+use sqlx::postgres::PgPool;
+
+async fn pool_or_skip(test: &str) -> Option<PgPool> {
+    let Ok(url) = std::env::var("UPSTREAM_TEST_DATABASE_URL") else {
+        eprintln!("skipping {test}: UPSTREAM_TEST_DATABASE_URL not set");
+        return None;
+    };
+    Some(
+        connect_and_migrate(&url, 4)
+            .await
+            .expect("connect + migrate"),
+    )
+}
+
+#[tokio::test]
+async fn default_audio_rate_is_seeded_and_model_rows_are_listed() {
+    let Some(pool) = pool_or_skip("default_audio_rate_is_seeded").await else {
+        return;
+    };
+    let model = format!("test/model-{}", uuid::Uuid::new_v4());
+    sqlx::query(
+        "INSERT INTO model_rates (model_id, unit, tokens_per_unit) VALUES ($1, 'audio_second', 2500)",
+    )
+    .bind(&model)
+    .execute(&pool)
+    .await
+    .unwrap();
+
+    let all = rates::list(&pool).await.unwrap();
+    assert!(
+        all.iter()
+            .any(|r| r.model_id == "*" && r.unit == "audio_second" && r.tokens_per_unit > 0),
+        "seeded default missing: {all:?}"
+    );
+    assert!(
+        all.iter()
+            .any(|r| r.model_id == model && r.tokens_per_unit == 2500)
+    );
+
+    sqlx::query("DELETE FROM model_rates WHERE model_id = $1")
+        .bind(&model)
+        .execute(&pool)
+        .await
+        .unwrap();
+}
+
+#[tokio::test]
+async fn rates_must_be_positive_and_of_a_known_unit() {
+    let Some(pool) = pool_or_skip("rates_must_be_positive").await else {
+        return;
+    };
+    let insert = |unit: &'static str, tokens: i64| {
+        let pool = pool.clone();
+        async move {
+            sqlx::query(
+                "INSERT INTO model_rates (model_id, unit, tokens_per_unit) VALUES ('test/bad', $1, $2)",
+            )
+            .bind(unit)
+            .bind(tokens)
+            .execute(&pool)
+            .await
+        }
+    };
+    assert!(insert("audio_second", 0).await.is_err());
+    assert!(insert("audio_second", -5).await.is_err());
+    assert!(insert("furlongs", 10).await.is_err());
+}
@@ -76,6 +76,12 @@ toml.workspace = true
 # transitively via candle-core.
 rayon = "1"

+# Unicode tables for the decision-model language detector (#339), which
+# must classify characters exactly as Python's `str.isalpha` / `re` and
+# `unicodedata.combining` do. Both already in the tree via tokenizers.
+unicode-normalization = "0.1"
+unicode-properties = "0.1"
+
 # candle for in-process inference. CUDA support is gated behind the
 # crate's `cuda` feature (default off) so the workspace builds on
 # non-CUDA hosts and CI runners.
@@ -55,6 +55,8 @@ pub fn neuron_routes() -> Router<Arc<NeuronState>> {
        .route("/models/{model_id}/capabilities", get(model_capabilities))
        .route("/v1/chat/completions", post(chat_completions))
        .route("/v1/images/generations", post(images_generations))
+        .route("/v1/audio/generations", post(audio_generations))
+        .route("/v1/systemone", post(systemone))
        .route("/v1/responses", post(responses))
 }

@@ -189,6 +191,7 @@ fn preflight_kind(err: &PreflightError) -> &'static str {
        PreflightError::EmptyRepo { .. } => "empty_repo",
        PreflightError::TpRequiresSafetensors { .. } => "tp_requires_safetensors",
        PreflightError::QuantNotFound { .. } => "quant_not_found",
+        PreflightError::Unsupported { .. } => "unsupported_load_option",
    }
 }

@@ -668,6 +671,138 @@ async fn images_generations(
    }
 }

+/// `POST /v1/audio/generations` (#382): render one audio generation and
+/// return it. This is the neuron half of the async job API — cortex owns
+/// the job and calls this once per job, waiting for the whole render. The
+/// body is the audio (`audio/wav`); the result metadata travels in the
+/// [`cortex_core::audio::RESULT_HEADER`] header so the bytes need no
+/// base64 envelope.
+async fn audio_generations(
+    State(state): State<Arc<NeuronState>>,
+    headers: axum::http::HeaderMap,
+    ApiJson(req): ApiJson<cortex_core::audio::AudioGenerationRequest>,
+) -> axum::response::Response {
+    use cortex_core::audio::RESULT_HEADER;
+    use cortex_core::error_envelope::OpenAiError;
+
+    let Some(candle) = state.candle.as_ref().map(Arc::clone) else {
+        return (
+            StatusCode::SERVICE_UNAVAILABLE,
+            Json(json!({"error": "candle harness not enabled on this neuron"})),
+        )
+            .into_response();
+    };
+    if let Err(msg) = req.validate_shape() {
+        return envelope_response(OpenAiError::new(
+            400,
+            "invalid_request_error",
+            "invalid_audio_params",
+            format!("invalid audio generation parameters: {msg}"),
+        ));
+    }
+    let principal = principal_key(&headers);
+    match candle.audio_generation(&req, principal.as_deref()).await {
+        Ok((wav, result)) => {
+            let meta = match serde_json::to_string(&result) {
+                Ok(m) => m,
+                Err(e) => {
+                    return inference_error_response(InferenceError::Other(anyhow::anyhow!(
+                        "serialise audio result: {e}"
+                    )));
+                }
+            };
+            let mut resp = axum::response::Response::new(axum::body::Body::from(wav));
+            let h = resp.headers_mut();
+            h.insert(
+                axum::http::header::CONTENT_TYPE,
+                axum::http::HeaderValue::from_static("audio/wav"),
+            );
+            if let Ok(v) = axum::http::HeaderValue::from_str(&meta) {
+                h.insert(RESULT_HEADER, v);
+            }
+            resp
+        }
+        Err(e) => inference_error_response(e),
+    }
+}
+
+/// `POST /v1/systemone` (#338): typed decisions over the TypeSafe Jev
+/// wire protocol, from a loaded decision model (Laya). Parsing and the
+/// transport-level limits happen here; routing, validation, admission,
+/// scoring and shaping in `CandleHarness::systemone`.
+///
+/// Errors carry the #63 envelope *and* the reference server's top-level
+/// `detail` string, so OpenAI-style and Jev/laya clients both read them.
+async fn systemone(
+    State(state): State<Arc<NeuronState>>,
+    headers: axum::http::HeaderMap,
+    body: axum::body::Bytes,
+) -> axum::response::Response {
+    use crate::harness::candle::SystemOneError;
+    use cortex_core::decisions::{SystemOneRequest, limits};
+
+    let Some(candle) = state.candle.as_ref().map(Arc::clone) else {
+        return decision_error_response(503, "candle harness not enabled on this neuron");
+    };
+    if body.len() > limits::MAX_BODY_BYTES {
+        return decision_error_response(413, "request body too large");
+    }
+    let request = match SystemOneRequest::parse(&body, limits::DEFAULT_MAX_TOKEN_BUDGET) {
+        Ok(r) => r,
+        Err(e) => return decision_error_response(e.status, &e.detail),
+    };
+    let served = headers
+        .get(cortex_core::catalogue::DECISION_MODEL_HEADER)
+        .and_then(|v| v.to_str().ok())
+        .map(str::to_string);
+    let principal = principal_key(&headers);
+
+    let started = std::time::Instant::now();
+    match candle
+        .systemone(served.as_deref(), request, principal.as_deref())
+        .await
+    {
+        Ok(response) => {
+            let ms = started.elapsed().as_secs_f64() * 1e3;
+            let mut out = Json(response).into_response();
+            for (name, value) in [
+                ("server-timing", format!("inference;dur={ms:.2}")),
+                ("x-inference-time-ms", format!("{ms:.2}")),
+            ] {
+                if let Ok(v) = axum::http::HeaderValue::from_str(&value) {
+                    out.headers_mut().insert(name, v);
+                }
+            }
+            out
+        }
+        Err(SystemOneError::Request(e)) => decision_error_response(e.status, &e.detail),
+        Err(SystemOneError::Inference(e)) => {
+            tracing::warn!(error = %e, "systemone request failed");
+            inference_error_response(e)
+        }
+    }
+}
+
+/// A rejected decision request: the #63 envelope, plus the `detail` field
+/// the reference (FastAPI) server answers with.
+fn decision_error_response(status: u16, detail: &str) -> axum::response::Response {
+    use cortex_core::error_envelope::OpenAiError;
+    let (kind, code) = match status {
+        404 => ("invalid_request_error", "model_not_found"),
+        413 => ("invalid_request_error", "request_too_large"),
+        422 => ("invalid_request_error", "invalid_decision_request"),
+        400..=499 => ("invalid_request_error", "invalid_request"),
+        _ => ("api_error", "service_unavailable"),
+    };
+    let env = OpenAiError::new(status, kind, code, detail.to_string());
+    let mut body = env.body();
+    if let Some(obj) = body.as_object_mut() {
+        obj.insert("detail".into(), json!(detail));
+    }
+    let status = StatusCode::from_u16(status).unwrap_or(StatusCode::BAD_REQUEST);
+    (status, Json(body)).into_response()
+}
+
 fn finish_reason_from_str(s: &str) -> crate::wire::FinishReason {
    use crate::wire::FinishReason;
    match s {
@@ -0,0 +1,478 @@
+//! ModernBERT, the bidirectional encoder under every Laya checkpoint.
+//!
+//! Follows the structure of `candle_transformers::models::modernbert`
+//! (itself a port of transformers' `ModernBertModel`), vendored rather
+//! than used as-is because that module only runs correctly in f32:
+//!
+//! - its attention masks are built in F32 whatever the model dtype, so
+//!   an f16/bf16 load fails at the first `broadcast_add`;
+//! - it computes the rotary angles `position × inv_freq` in the model
+//!   dtype, which in f16 already loses a quarter-radian of phase by
+//!   position 500;
+//! - it loads each layer's `attn_norm` with `.ok()`, so a weight-prefix
+//!   mistake silently yields a model with no pre-attention norms.
+//!
+//! Here the angles are computed in f32 and only the resulting cos/sin
+//! tables are cast; attention scores are softmaxed in f32 against f32
+//! masks; and `attn_norm` is required on every layer except layer 0,
+//! which ModernBERT builds as an identity.
+//!
+//! Precision follows PyTorch autocast, which is how the reference runs on
+//! a GPU: the linear layers (and so the attention inputs) run in the
+//! load dtype, while the residual stream, the embeddings and every
+//! LayerNorm stay in f32. Casting the residual stream to bf16 as well
+//! drifts the released English checkpoint's logits by ~0.25 over its 28
+//! layers; keeping it in f32 is what autocast does and costs little.
+//!
+//! Masked positions use a large *finite* negative rather than `-inf`.
+//! A padded query near the end of a short row can find every key in
+//! its sliding window padded; with `-inf` that row's softmax is NaN,
+//! and NaN in a padded row's value vector poisons the valid rows that
+//! give it zero weight (`0 × NaN = NaN`). A finite value makes such a
+//! row uniform instead, and valid rows never look at it.
+
+use anyhow::{Context, Result, ensure};
+use candle_core::{D, DType, Device, Module, Tensor};
+use candle_nn::{Embedding, LayerNorm, Linear, VarBuilder};
+
+use super::config::{EncoderConfig, LayerKind};
+
+/// Additive mask value for a position that must not be attended.
+pub(crate) const MASKED: f32 = -1e9;
+
+/// Apply `lin` in its weight's dtype, casting `x` in if needed. The
+/// result stays in that dtype; callers cast back to f32 where it joins
+/// the residual stream.
+pub(crate) fn project(lin: &Linear, x: &Tensor) -> candle_core::Result<Tensor> {
+    x.to_dtype(lin.weight().dtype())?.apply(lin)
+}
+
+/// A bias-free LayerNorm that takes candle's fused kernel.
+///
+/// `candle_nn::LayerNorm` dispatches to the fused `layer_norm` op only
+/// when it has a bias; without one it falls back to six elementwise
+/// kernels (mean, subtract, square, mean, divide, scale), which was a
+/// quarter of the encoder's GPU time. ModernBERT's norms have no bias,
+/// so they get an explicit zero one: same result, one kernel.
+fn fused_layer_norm_no_bias(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
+    let weight = vb.get(size, "weight")?;
+    let bias = weight.zeros_like()?;
+    Ok(LayerNorm::new(weight, bias, eps))
+}
+
+/// Precomputed rotary tables for one base, `(max_pos, head_dim / 2)`.
+struct Rotary {
+    cos: Tensor,
+    sin: Tensor,
+}
+
+impl Rotary {
+    fn new(cfg: &EncoderConfig, theta: f64, dtype: DType, device: &Device) -> Result<Self> {
+        let dim = cfg.head_dim();
+        let inv_freq: Vec<f32> = (0..dim)
+            .step_by(2)
+            .map(|i| (1.0 / theta.powf(i as f64 / dim as f64)) as f32)
+            .collect();
+        let n = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, n), device)?;
+        let max_pos = cfg.max_position_embeddings;
+        let pos = Tensor::arange(0u32, max_pos as u32, device)?
+            .to_dtype(DType::F32)?
+            .reshape((max_pos, 1))?;
+        let freqs = pos.matmul(&inv_freq)?;
+        Ok(Self {
+            cos: freqs.cos()?.to_dtype(dtype)?,
+            sin: freqs.sin()?.to_dtype(dtype)?,
+        })
+    }
+
+    fn apply(&self, q: &Tensor, k: &Tensor, seq_len: usize) -> Result<(Tensor, Tensor)> {
+        let cos = self.cos.narrow(0, 0, seq_len)?;
+        let sin = self.sin.narrow(0, 0, seq_len)?;
+        Ok((
+            candle_nn::rotary_emb::rope(&q.contiguous()?, &cos, &sin)?,
+            candle_nn::rotary_emb::rope(&k.contiguous()?, &cos, &sin)?,
+        ))
+    }
+}
+
+struct Attention {
+    wqkv: Linear,
+    wo: Linear,
+    n_heads: usize,
+    head_dim: usize,
+    kind: LayerKind,
+}
+
+impl Attention {
+    fn forward(&self, xs: &Tensor, rotary: &Rotary, mask: Option<&Tensor>) -> Result<Tensor> {
+        let (b, l, d) = xs.dims3()?;
+        let qkv = project(&self.wqkv, xs)?
+            .reshape((b, l, 3, self.n_heads, self.head_dim))?
+            .permute((2, 0, 3, 1, 4))?;
+        // Rotary in the projection dtype, as under autocast; the scores
+        // are then taken in f32 (see `scaled_attention`).
+        let q = qkv.get(0)?;
+        let k = qkv.get(1)?;
+        let v = qkv.get(2)?.contiguous()?;
+        let (q, k) = rotary.apply(&q, &k, l)?;
+        let ctx = scaled_attention(&q, &k, &v, mask, self.head_dim)?;
+        let ctx = ctx.transpose(1, 2)?.reshape((b, l, d))?;
+        Ok(ctx.apply(&self.wo)?.to_dtype(DType::F32)?)
+    }
+}
+
+/// A batch packed without padding for the flash-attention path: every
+/// row's tokens back to back, `(total, …)`. Linear layers then do no work
+/// for padding, and flash varlen keeps rows from attending to each other.
+#[cfg(feature = "flash-attn")]
+pub struct Packed {
+    /// `(b + 1,)` u32 cumulative row lengths, as flash varlen takes them.
+    pub cu_seqlens: Tensor,
+    /// Longest row.
+    pub max_len: usize,
+    /// `(total,)` u32: each token's position within its own row.
+    pub positions: Tensor,
+    /// Where each row starts in the packed order.
+    pub offsets: Vec<usize>,
+}
+
+#[cfg(feature = "flash-attn")]
+impl Packed {
+    pub fn new(lens: &[usize], device: &Device) -> Result<Self> {
+        let mut cu = Vec::with_capacity(lens.len() + 1);
+        let mut offsets = Vec::with_capacity(lens.len());
+        let mut positions = Vec::with_capacity(lens.iter().sum());
+        let mut total = 0u32;
+        cu.push(0u32);
+        for &n in lens {
+            offsets.push(total as usize);
+            positions.extend(0..n as u32);
+            total += n as u32;
+            cu.push(total);
+        }
+        let n = cu.len();
+        Ok(Self {
+            cu_seqlens: Tensor::from_vec(cu, n, device)?,
+            max_len: lens.iter().copied().max().unwrap_or(0),
+            positions: Tensor::from_vec(positions, total as usize, device)?,
+            offsets,
+        })
+    }
+}
+
+/// Bidirectional flash attention over a packed batch. `q`, `k`, `v` are
+/// `(total, h, dh)`; `half_window` limits each query to keys within that
+/// distance on either side (ModernBERT's sliding layers), `None` attends
+/// the whole row. Scores are accumulated in f32 inside the kernel.
+#[cfg(feature = "flash-attn")]
+pub(crate) fn flash_attention(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    packed: &Packed,
+    half_window: Option<usize>,
+) -> Result<Tensor> {
+    let head_dim = q.dim(2)?;
+    let scale = (head_dim as f64).powf(-0.5) as f32;
+    Ok(candle_flash_attn::flash_attn_varlen_windowed(
+        q,
+        k,
+        v,
+        &packed.cu_seqlens,
+        &packed.cu_seqlens,
+        packed.max_len,
+        packed.max_len,
+        scale,
+        half_window,
+        half_window,
+    )?)
+}
+
+/// Split a packed `(total, 3·d)` qkv projection into contiguous
+/// `(total, h, dh)` q, k and v.
+#[cfg(feature = "flash-attn")]
+pub(crate) fn split_qkv(
+    qkv: &Tensor,
+    n_heads: usize,
+    head_dim: usize,
+) -> Result<(Tensor, Tensor, Tensor)> {
+    let t = qkv.dim(0)?;
+    let qkv = qkv.reshape((t, 3, n_heads, head_dim))?;
+    let part = |i: usize| -> Result<Tensor> { Ok(qkv.narrow(1, i, 1)?.squeeze(1)?.contiguous()?) };
+    Ok((part(0)?, part(1)?, part(2)?))
+}
+
+#[cfg(feature = "flash-attn")]
+impl Rotary {
+    /// Rotate packed `(total, h, dh)` q and k by each token's position.
+    fn apply_packed(&self, q: &Tensor, k: &Tensor, positions: &Tensor) -> Result<(Tensor, Tensor)> {
+        let cos = self.cos.index_select(positions, 0)?;
+        let sin = self.sin.index_select(positions, 0)?;
+        let rot = |x: &Tensor| -> Result<Tensor> {
+            Ok(candle_nn::rotary_emb::rope_thd(&x.unsqueeze(0)?, &cos, &sin)?.squeeze(0)?)
+        };
+        Ok((rot(q)?, rot(k)?))
+    }
+}
+
+#[cfg(feature = "flash-attn")]
+impl Attention {
+    /// `xs` is the packed f32 residual stream `(total, d)`.
+    fn forward_packed(
+        &self,
+        xs: &Tensor,
+        rotary: &Rotary,
+        packed: &Packed,
+        half_window: usize,
+    ) -> Result<Tensor> {
+        let (t, d) = xs.dims2()?;
+        let (q, k, v) = split_qkv(&project(&self.wqkv, xs)?, self.n_heads, self.head_dim)?;
+        let (q, k) = rotary.apply_packed(&q, &k, &packed.positions)?;
+        let window = match self.kind {
+            LayerKind::Sliding => Some(half_window),
+            LayerKind::Global => None,
+        };
+        let ctx = flash_attention(&q, &k, &v, packed, window)?.reshape((t, d))?;
+        Ok(ctx.apply(&self.wo)?.to_dtype(DType::F32)?)
+    }
+}
+
+/// `softmax(q kᵀ / √d + mask) v`, scores and softmax in f32.
+///
+/// `q`, `k`, `v` are `(b, h, l, d)`; `mask` is an f32 additive mask
+/// broadcastable to `(b, h, l, l)`, or `None` when nothing is masked —
+/// which saves a bandwidth-bound pass over the `(b, h, l, l)` scores.
+pub(crate) fn scaled_attention(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    mask: Option<&Tensor>,
+    head_dim: usize,
+) -> Result<Tensor> {
+    let dtype = v.dtype();
+    let scale = (head_dim as f64).powf(-0.5);
+    // Scores in f32 end to end: a bf16 `q kᵀ` is rounded to 8 mantissa
+    // bits before the softmax ever sees it, which on this model's
+    // logit-sized scores is most of the drift from the reference's GPU
+    // path (whose SDPA kernels keep scores in f32).
+    // The scale goes on q, not on the `(b, h, l, l)` scores: the same
+    // product, on a tensor `l / d` times smaller.
+    let scores = (q.to_dtype(DType::F32)? * scale)?.matmul(&k.to_dtype(DType::F32)?.t()?)?;
+    let scores = match mask {
+        Some(mask) => scores.broadcast_add(mask)?,
+        None => scores,
+    };
+    let probs = candle_nn::ops::softmax_last_dim(&scores)?;
+    Ok(probs.to_dtype(dtype)?.matmul(v)?)
+}
+
+struct Mlp {
+    wi: Linear,
+    wo: Linear,
+}
+
+impl Module for Mlp {
+    /// f32 in, f32 out; the projections run in the weights' dtype.
+    fn forward(&self, xs: &Tensor) -> candle_core::Result<Tensor> {
+        let xs = project(&self.wi, xs)?;
+        let parts = xs.chunk(2, D::Minus1)?;
+        (parts[0].gelu_erf()? * &parts[1])?
+            .apply(&self.wo)?
+            .to_dtype(DType::F32)
+    }
+}
+
+struct Layer {
+    attn_norm: Option<LayerNorm>,
+    attn: Attention,
+    mlp_norm: LayerNorm,
+    mlp: Mlp,
+}
+
+/// The two additive masks one forward needs, both f32. Either is `None`
+/// when it would mask nothing: `global` for a batch with no padding (every
+/// single-question request), `sliding` additionally when no row is longer
+/// than the window reaches.
+pub struct Masks {
+    /// `(b, 1, 1, l)`: padded keys masked. Global layers and the
+    /// decision head use this one.
+    pub global: Option<Tensor>,
+    /// `(b, 1, l, l)`: padded keys, plus keys outside the window.
+    pub sliding: Option<Tensor>,
+}
+
+impl Masks {
+    /// Build both masks from per-row valid lengths. Rows are
+    /// right-padded: positions `>= lens[i]` of row `i` are padding.
+    pub fn new(
+        lens: &[usize],
+        seq_len: usize,
+        half_window: usize,
+        device: &Device,
+    ) -> Result<Self> {
+        let b = lens.len();
+        let padded = lens.iter().any(|&n| n < seq_len);
+        let windowed = seq_len > half_window + 1;
+        let global = if padded {
+            let mut pad = Vec::with_capacity(b * seq_len);
+            for &n in lens {
+                pad.extend((0..seq_len).map(|j| if j < n { 0f32 } else { MASKED }));
+            }
+            Some(Tensor::from_vec(pad, (b, 1, 1, seq_len), device)?)
+        } else {
+            None
+        };
+        if !windowed {
+            let sliding = global.clone();
+            return Ok(Self { global, sliding });
+        }
+        // Built on the device from two aranges, not on the host: at
+        // 1024 tokens a host-built window is a 4 MB upload per forward.
+        let pos = Tensor::arange(0u32, seq_len as u32, device)?.to_dtype(DType::F32)?;
+        let dist = pos
+            .reshape((seq_len, 1))?
+            .broadcast_sub(&pos.reshape((1, seq_len))?)?
+            .abs()?;
+        let outside = dist.gt(half_window as f64)?;
+        let zeros = Tensor::zeros((seq_len, seq_len), DType::F32, device)?;
+        let masked = Tensor::full(MASKED, (seq_len, seq_len), device)?;
+        let window = outside
+            .where_cond(&masked, &zeros)?
+            .reshape((1, 1, seq_len, seq_len))?;
+        let sliding = match &global {
+            Some(g) => g.broadcast_add(&window)?,
+            None => window,
+        };
+        Ok(Self {
+            global,
+            sliding: Some(sliding),
+        })
+    }
+}
+
+pub struct ModernBert {
+    embeddings: Embedding,
+    embed_norm: LayerNorm,
+    layers: Vec<Layer>,
+    final_norm: LayerNorm,
+    global_rotary: Rotary,
+    sliding_rotary: Rotary,
+    half_window: usize,
+}
+
+impl ModernBert {
+    /// Load from a VarBuilder rooted at the encoder's prefix (`encoder`
+    /// in a Laya checkpoint, `model` in a stock ModernBERT one). Linear
+    /// weights load in the VarBuilder's dtype; embeddings and norms in
+    /// f32.
+    pub fn load(vb: VarBuilder, cfg: &EncoderConfig) -> Result<Self> {
+        let d = cfg.hidden_size;
+        let f32_vb = vb.clone().set_dtype(DType::F32);
+        let embeddings = candle_nn::embedding(
+            cfg.vocab_size,
+            d,
+            f32_vb.pp("embeddings").pp("tok_embeddings"),
+        )
+        .context("encoder.embeddings.tok_embeddings")?;
+        let embed_norm = fused_layer_norm_no_bias(d, cfg.norm_eps, f32_vb.pp("embeddings.norm"))?;
+        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        for (i, kind) in cfg.layer_kinds.iter().copied().enumerate() {
+            let lvb = vb.pp(format!("layers.{i}"));
+            let norm_vb = f32_vb.pp(format!("layers.{i}"));
+            // ModernBERT builds layer 0's attn_norm as an identity and
+            // saves nothing for it; every other layer must have one.
+            let attn_norm = if lvb.contains_tensor("attn_norm.weight") {
+                Some(fused_layer_norm_no_bias(
+                    d,
+                    cfg.norm_eps,
+                    norm_vb.pp("attn_norm"),
+                )?)
+            } else {
+                ensure!(
+                    i == 0,
+                    "encoder layer {i} has no attn_norm.weight — wrong weight prefix, \
+                     or not a ModernBERT checkpoint"
+                );
+                None
+            };
+            let attn = Attention {
+                wqkv: candle_nn::linear_no_bias(d, 3 * d, lvb.pp("attn.Wqkv"))?,
+                wo: candle_nn::linear_no_bias(d, d, lvb.pp("attn.Wo"))?,
+                n_heads: cfg.num_attention_heads,
+                head_dim: cfg.head_dim(),
+                kind,
+            };
+            let mlp_norm = fused_layer_norm_no_bias(d, cfg.norm_eps, norm_vb.pp("mlp_norm"))?;
+            let mlp = Mlp {
+                wi: candle_nn::linear_no_bias(d, 2 * cfg.intermediate_size, lvb.pp("mlp.Wi"))?,
+                wo: candle_nn::linear_no_bias(cfg.intermediate_size, d, lvb.pp("mlp.Wo"))?,
+            };
+            layers.push(Layer {
+                attn_norm,
+                attn,
+                mlp_norm,
+                mlp,
+            });
+        }
+        let final_norm = fused_layer_norm_no_bias(d, cfg.norm_eps, f32_vb.pp("final_norm"))?;
+        let device = vb.device();
+        Ok(Self {
+            embeddings,
+            embed_norm,
+            layers,
+            final_norm,
+            global_rotary: Rotary::new(cfg, cfg.global_rope_theta, vb.dtype(), device)?,
+            sliding_rotary: Rotary::new(cfg, cfg.local_rope_theta, vb.dtype(), device)?,
+            half_window: cfg.local_attention / 2,
+        })
+    }
+
+    pub fn half_window(&self) -> usize {
+        self.half_window
+    }
+
+    /// `ids` is `(b, l)` u32; returns the final hidden states `(b, l, d)`
+    /// in f32.
+    pub fn forward(&self, ids: &Tensor, masks: &Masks) -> Result<Tensor> {
+        let mut xs = ids.apply(&self.embeddings)?.apply(&self.embed_norm)?;
+        for layer in &self.layers {
+            let normed = match &layer.attn_norm {
+                Some(norm) => xs.apply(norm)?,
+                None => xs.clone(),
+            };
+            let (rotary, mask) = match layer.attn.kind {
+                LayerKind::Global => (&self.global_rotary, masks.global.as_ref()),
+                LayerKind::Sliding => (&self.sliding_rotary, masks.sliding.as_ref()),
+            };
+            xs = (&xs + layer.attn.forward(&normed, rotary, mask)?)?;
+            let mlp_out = xs.apply(&layer.mlp_norm)?.apply(&layer.mlp)?;
+            xs = (xs + mlp_out)?;
+        }
+        Ok(xs.apply(&self.final_norm)?)
+    }
+
+    /// The flash path: `ids` is the packed `(total,)` u32 token stream;
+    /// returns the packed final hidden states `(total, d)` in f32.
+    #[cfg(feature = "flash-attn")]
+    pub fn forward_packed(&self, ids: &Tensor, packed: &Packed) -> Result<Tensor> {
+        let mut xs = ids.apply(&self.embeddings)?.apply(&self.embed_norm)?;
+        for layer in &self.layers {
+            let normed = match &layer.attn_norm {
+                Some(norm) => xs.apply(norm)?,
+                None => xs.clone(),
+            };
+            let rotary = match layer.attn.kind {
+                LayerKind::Global => &self.global_rotary,
+                LayerKind::Sliding => &self.sliding_rotary,
+            };
+            xs = (&xs
+                + layer
+                    .attn
+                    .forward_packed(&normed, rotary, packed, self.half_window)?)?;
+            let mlp_out = xs.apply(&layer.mlp_norm)?.apply(&layer.mlp)?;
+            xs = (xs + mlp_out)?;
+        }
+        Ok(xs.apply(&self.final_norm)?)
+    }
+}
@@ -0,0 +1,358 @@
+//! The two config files a Laya checkpoint ships, and what they mean.
+//!
+//! A checkpoint directory holds `rl_agent_config.json` (the decision
+//! model: head depth, token budgets, calibration temperatures) and
+//! `encoder/config.json` (the ModernBERT backbone, written by
+//! transformers 5). There is no top-level `config.json`.
+//!
+//! The encoder config is parsed here rather than through
+//! `candle_transformers::models::modernbert::Config` because
+//! transformers 5 moved the rotary bases into a nested
+//! `rope_parameters.{full_attention,sliding_attention}.rope_theta`
+//! block, which that struct cannot read; the older flat
+//! `global_rope_theta` / `local_rope_theta` fields are still honoured
+//! when a checkpoint carries them. mmBERT (the multilingual
+//! checkpoint's backbone) is the case that matters: both of its bases
+//! are 160,000, where the flat-field defaults would silently give the
+//! sliding layers 10,000.
+
+use anyhow::{Context, Result, bail, ensure};
+use serde::Deserialize;
+use std::collections::BTreeMap;
+use std::path::Path;
+
+/// `rl_agent_config.json`.
+#[derive(Debug, Clone, Deserialize)]
+pub struct LayaConfig {
+    /// The backbone the head was trained on, e.g.
+    /// `answerdotai/ModernBERT-large`. Informational: the weights come
+    /// from the checkpoint, the shape from `encoder/config.json`.
+    #[serde(default)]
+    pub encoder: Option<String>,
+    #[serde(default = "default_head_layers")]
+    pub head_layers: usize,
+    #[serde(default = "default_max_len")]
+    pub max_len: usize,
+    #[serde(default = "default_head_max_len")]
+    pub head_max_len: usize,
+    /// One act head output per entry, plus one for "answer". The act
+    /// head's width is `act_costs.len() + 1`.
+    #[serde(default)]
+    pub act_costs: BTreeMap<String, f64>,
+    /// Per-question-type temperatures, indexed choice / score / noul.
+    #[serde(default = "default_temperature")]
+    pub temperature: Vec<f64>,
+    /// Temperatures fitted per `"<type>:<cardinality bucket>"`.
+    #[serde(default)]
+    pub temperature_by_options: BTreeMap<String, f64>,
+    /// Autocast dtype the reference runs on GPU (`"bf16"` or `"fp16"`).
+    #[serde(default)]
+    pub amp_dtype: Option<String>,
+}
+
+fn default_head_layers() -> usize {
+    2
+}
+fn default_max_len() -> usize {
+    512
+}
+fn default_head_max_len() -> usize {
+    192
+}
+fn default_temperature() -> Vec<f64> {
+    vec![1.0, 1.0, 1.0]
+}
+
+impl LayaConfig {
+    pub fn n_act(&self) -> usize {
+        self.act_costs.len() + 1
+    }
+}
+
+/// Whether an attention layer sees the whole sequence or a window.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum LayerKind {
+    Global,
+    Sliding,
+}
+
+/// The ModernBERT backbone shape, resolved from `encoder/config.json`.
+#[derive(Debug, Clone)]
+pub struct EncoderConfig {
+    pub vocab_size: usize,
+    pub hidden_size: usize,
+    pub num_hidden_layers: usize,
+    pub num_attention_heads: usize,
+    pub intermediate_size: usize,
+    pub max_position_embeddings: usize,
+    pub norm_eps: f64,
+    pub pad_token_id: u32,
+    /// Full sliding-window width. A token attends to keys at distance
+    /// `<= local_attention / 2` on either side.
+    pub local_attention: usize,
+    pub global_rope_theta: f64,
+    pub local_rope_theta: f64,
+    pub layer_kinds: Vec<LayerKind>,
+}
+
+#[derive(Deserialize)]
+struct RawEncoderConfig {
+    vocab_size: usize,
+    hidden_size: usize,
+    num_hidden_layers: usize,
+    num_attention_heads: usize,
+    intermediate_size: usize,
+    max_position_embeddings: usize,
+    #[serde(default)]
+    norm_eps: Option<f64>,
+    #[serde(default)]
+    layer_norm_eps: Option<f64>,
+    pad_token_id: u32,
+    #[serde(default = "default_global_every")]
+    global_attn_every_n_layers: usize,
+    local_attention: usize,
+    #[serde(default)]
+    layer_types: Option<Vec<String>>,
+    #[serde(default)]
+    rope_parameters: Option<serde_json::Value>,
+    #[serde(default)]
+    global_rope_theta: Option<f64>,
+    #[serde(default)]
+    local_rope_theta: Option<f64>,
+    #[serde(default)]
+    norm_bias: bool,
+    #[serde(default)]
+    attention_bias: bool,
+    #[serde(default)]
+    mlp_bias: bool,
+    #[serde(default)]
+    hidden_activation: Option<String>,
+}
+
+fn default_global_every() -> usize {
+    3
+}
+
+/// transformers' ModernBERT defaults for checkpoints that predate
+/// `rope_parameters` and also omit the flat fields.
+const DEFAULT_GLOBAL_ROPE_THETA: f64 = 160_000.0;
+const DEFAULT_LOCAL_ROPE_THETA: f64 = 10_000.0;
+
+impl EncoderConfig {
+    pub fn from_json(text: &str) -> Result<Self> {
+        let raw: RawEncoderConfig =
+            serde_json::from_str(text).context("parse ModernBERT encoder config")?;
+
+        // Variants this port does not implement are refused, not
+        // approximated: each would load without error and answer wrong.
+        ensure!(
+            !raw.norm_bias && !raw.attention_bias && !raw.mlp_bias,
+            "ModernBERT with norm/attention/mlp biases is not supported"
+        );
+        if let Some(act) = raw.hidden_activation.as_deref() {
+            ensure!(
+                act == "gelu",
+                "ModernBERT hidden_activation {act:?} is not supported (only exact \"gelu\")"
+            );
+        }
+        ensure!(
+            raw.hidden_size.is_multiple_of(raw.num_attention_heads),
+            "hidden_size {} is not divisible by num_attention_heads {}",
+            raw.hidden_size,
+            raw.num_attention_heads
+        );
+
+        let (global_rope_theta, local_rope_theta) = rope_thetas(&raw)?;
+
+        let layer_kinds = match &raw.layer_types {
+            Some(types) => {
+                ensure!(
+                    types.len() == raw.num_hidden_layers,
+                    "layer_types has {} entries for {} layers",
+                    types.len(),
+                    raw.num_hidden_layers
+                );
+                types
+                    .iter()
+                    .map(|t| match t.as_str() {
+                        "full_attention" => Ok(LayerKind::Global),
+                        "sliding_attention" => Ok(LayerKind::Sliding),
+                        other => bail!("unknown ModernBERT layer type {other:?}"),
+                    })
+                    .collect::<Result<Vec<_>>>()?
+            }
+            None => (0..raw.num_hidden_layers)
+                .map(|i| {
+                    if i % raw.global_attn_every_n_layers == 0 {
+                        LayerKind::Global
+                    } else {
+                        LayerKind::Sliding
+                    }
+                })
+                .collect(),
+        };
+
+        Ok(Self {
+            vocab_size: raw.vocab_size,
+            hidden_size: raw.hidden_size,
+            num_hidden_layers: raw.num_hidden_layers,
+            num_attention_heads: raw.num_attention_heads,
+            intermediate_size: raw.intermediate_size,
+            max_position_embeddings: raw.max_position_embeddings,
+            norm_eps: raw.norm_eps.or(raw.layer_norm_eps).unwrap_or(1e-5),
+            pad_token_id: raw.pad_token_id,
+            local_attention: raw.local_attention,
+            global_rope_theta,
+            local_rope_theta,
+            layer_kinds,
+        })
+    }
+
+    pub fn head_dim(&self) -> usize {
+        self.hidden_size / self.num_attention_heads
+    }
+}
+
+/// Resolve the two rotary bases. `rope_parameters` (transformers 5)
+/// wins over the flat fields; within it, a per-layer-type block wins
+/// over a shared top-level `rope_theta`.
+fn rope_thetas(raw: &RawEncoderConfig) -> Result<(f64, f64)> {
+    let mut global = raw.global_rope_theta.unwrap_or(DEFAULT_GLOBAL_ROPE_THETA);
+    let mut local = raw.local_rope_theta.unwrap_or(DEFAULT_LOCAL_ROPE_THETA);
+    if let Some(rope) = &raw.rope_parameters {
+        let shared = rope.get("rope_theta").and_then(|v| v.as_f64());
+        for (key, slot) in [
+            ("full_attention", &mut global),
+            ("sliding_attention", &mut local),
+        ] {
+            let block = rope.get(key);
+            if let Some(rope_type) = block
+                .and_then(|b| b.get("rope_type"))
+                .and_then(|v| v.as_str())
+            {
+                ensure!(
+                    rope_type == "default",
+                    "rope_type {rope_type:?} for {key} is not supported"
+                );
+            }
+            let theta = block
+                .and_then(|b| b.get("rope_theta"))
+                .and_then(|v| v.as_f64())
+                .or(shared);
+            if let Some(theta) = theta {
+                *slot = theta;
+            }
+        }
+    }
+    Ok((global, local))
+}
+
+/// Both configs of one checkpoint directory.
+#[derive(Debug, Clone)]
+pub struct CheckpointConfig {
+    pub laya: LayaConfig,
+    pub encoder: EncoderConfig,
+}
+
+impl CheckpointConfig {
+    /// Read `rl_agent_config.json` and `encoder/config.json` from a
+    /// checkpoint directory (the repo root, or one of its subfolders).
+    pub fn from_dir(dir: &Path) -> Result<Self> {
+        let laya_path = dir.join("rl_agent_config.json");
+        let laya: LayaConfig = serde_json::from_str(
+            &std::fs::read_to_string(&laya_path)
+                .with_context(|| format!("read {}", laya_path.display()))?,
+        )
+        .with_context(|| format!("parse {}", laya_path.display()))?;
+        let enc_path = dir.join("encoder").join("config.json");
+        let encoder = EncoderConfig::from_json(
+            &std::fs::read_to_string(&enc_path)
+                .with_context(|| format!("read {}", enc_path.display()))?,
+        )
+        .with_context(|| format!("parse {}", enc_path.display()))?;
+        ensure!(
+            laya.temperature.len() == 3,
+            "rl_agent_config temperature must have 3 entries (choice, score, noul), got {}",
+            laya.temperature.len()
+        );
+        Ok(Self { laya, encoder })
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn base() -> serde_json::Value {
+        serde_json::json!({
+            "vocab_size": 100, "hidden_size": 64, "num_hidden_layers": 4,
+            "num_attention_heads": 4, "intermediate_size": 96,
+            "max_position_embeddings": 512, "norm_eps": 1e-5, "pad_token_id": 0,
+            "global_attn_every_n_layers": 3, "local_attention": 16,
+        })
+    }
+
+    #[test]
+    fn nested_rope_parameters_override_flat_defaults() {
+        let mut v = base();
+        v["rope_parameters"] = serde_json::json!({
+            "full_attention": {"rope_theta": 160000.0, "rope_type": "default"},
+            "sliding_attention": {"rope_theta": 160000.0, "rope_type": "default"},
+        });
+        let c = EncoderConfig::from_json(&v.to_string()).unwrap();
+        assert_eq!(c.global_rope_theta, 160_000.0);
+        // The mmBERT case: the flat default would have been 10,000.
+        assert_eq!(c.local_rope_theta, 160_000.0);
+    }
+
+    #[test]
+    fn flat_fields_are_read_without_rope_parameters() {
+        let mut v = base();
+        v["global_rope_theta"] = serde_json::json!(80000.0);
+        v["local_rope_theta"] = serde_json::json!(5000.0);
+        let c = EncoderConfig::from_json(&v.to_string()).unwrap();
+        assert_eq!((c.global_rope_theta, c.local_rope_theta), (80000.0, 5000.0));
+    }
+
+    #[test]
+    fn layer_kinds_follow_every_n_when_layer_types_absent() {
+        let c = EncoderConfig::from_json(&base().to_string()).unwrap();
+        assert_eq!(
+            c.layer_kinds,
+            vec![
+                LayerKind::Global,
+                LayerKind::Sliding,
+                LayerKind::Sliding,
+                LayerKind::Global
+            ]
+        );
+    }
+
+    #[test]
+    fn explicit_layer_types_win() {
+        let mut v = base();
+        v["layer_types"] = serde_json::json!([
+            "sliding_attention",
+            "full_attention",
+            "full_attention",
+            "sliding_attention"
+        ]);
+        let c = EncoderConfig::from_json(&v.to_string()).unwrap();
+        assert_eq!(c.layer_kinds[0], LayerKind::Sliding);
+        assert_eq!(c.layer_kinds[1], LayerKind::Global);
+    }
+
+    #[test]
+    fn unsupported_variants_are_refused() {
+        let mut v = base();
+        v["attention_bias"] = serde_json::json!(true);
+        assert!(EncoderConfig::from_json(&v.to_string()).is_err());
+        let mut v = base();
+        v["hidden_activation"] = serde_json::json!("gelu_pytorch_tanh");
+        assert!(EncoderConfig::from_json(&v.to_string()).is_err());
+        let mut v = base();
+        v["rope_parameters"] =
+            serde_json::json!({"full_attention": {"rope_type": "yarn", "rope_theta": 1.0}});
+        assert!(EncoderConfig::from_json(&v.to_string()).is_err());
+    }
+}
@@ -0,0 +1,180 @@
+//! The decision head that sits on top of the encoder.
+//!
+//! Mirrors the reference `DecisionModel` (the `laya` SDK's
+//! `common.py`):
+//!
+//! 1. a per-question-type embedding added to every position;
+//! 2. `head_layers` × PyTorch `nn.TransformerEncoderLayer` with
+//!    `norm_first=True`: pre-norm self-attention through a fused
+//!    `in_proj` with bias, then a pre-norm feed-forward whose
+//!    activation is **ReLU** — PyTorch's default, which the reference
+//!    never overrides;
+//! 3. `scorer`, applied at each option's `[MASK]` marker: LayerNorm →
+//!    Linear → exact GELU → Linear to one logit;
+//! 4. `act_head`, over the first position's hidden state plus four
+//!    summary features of the (uncalibrated) answer distribution.
+
+use anyhow::Result;
+use candle_core::{DType, Tensor};
+use candle_nn::{Embedding, LayerNorm, Linear, VarBuilder};
+
+use super::backbone::{project, scaled_attention};
+
+/// PyTorch's `LayerNorm` default.
+const TORCH_LN_EPS: f64 = 1e-5;
+
+struct HeadLayer {
+    norm1: LayerNorm,
+    in_proj: Linear,
+    out_proj: Linear,
+    norm2: LayerNorm,
+    linear1: Linear,
+    linear2: Linear,
+    n_heads: usize,
+    head_dim: usize,
+}
+
+impl HeadLayer {
+    fn load(vb: VarBuilder, d: usize) -> Result<Self> {
+        // `nn.TransformerEncoder` is built with `nhead = max(1, d // 64)`
+        // and `dim_feedforward = 4 * d`.
+        let n_heads = (d / 64).max(1);
+        let f32_vb = vb.clone().set_dtype(DType::F32);
+        Ok(Self {
+            norm1: candle_nn::layer_norm(d, TORCH_LN_EPS, f32_vb.pp("norm1"))?,
+            in_proj: linear_torch_mha_in_proj(d, vb.pp("self_attn"))?,
+            out_proj: candle_nn::linear(d, d, vb.pp("self_attn.out_proj"))?,
+            norm2: candle_nn::layer_norm(d, TORCH_LN_EPS, f32_vb.pp("norm2"))?,
+            linear1: candle_nn::linear(d, 4 * d, vb.pp("linear1"))?,
+            linear2: candle_nn::linear(4 * d, d, vb.pp("linear2"))?,
+            n_heads,
+            head_dim: d / n_heads,
+        })
+    }
+
+    /// `xs` is the f32 residual stream; projections run in the weights'
+    /// dtype and join it back in f32, as under autocast.
+    fn forward(&self, xs: &Tensor, key_mask: Option<&Tensor>) -> Result<Tensor> {
+        let (b, l, d) = xs.dims3()?;
+        let qkv = project(&self.in_proj, &xs.apply(&self.norm1)?)?
+            .reshape((b, l, 3, self.n_heads, self.head_dim))?
+            .permute((2, 0, 3, 1, 4))?;
+        let q = qkv.get(0)?.contiguous()?;
+        let k = qkv.get(1)?.contiguous()?;
+        let v = qkv.get(2)?.contiguous()?;
+        let ctx = scaled_attention(&q, &k, &v, key_mask, self.head_dim)?
+            .transpose(1, 2)?
+            .reshape((b, l, d))?;
+        let xs = (xs + ctx.apply(&self.out_proj)?.to_dtype(DType::F32)?)?;
+        let ff = project(&self.linear1, &xs.apply(&self.norm2)?)?
+            .relu()?
+            .apply(&self.linear2)?
+            .to_dtype(DType::F32)?;
+        Ok((xs + ff)?)
+    }
+
+    /// The flash path over a packed `(total, d)` f32 stream.
+    #[cfg(feature = "flash-attn")]
+    fn forward_packed(&self, xs: &Tensor, packed: &super::backbone::Packed) -> Result<Tensor> {
+        use super::backbone::{flash_attention, split_qkv};
+        let (t, d) = xs.dims2()?;
+        let qkv = project(&self.in_proj, &xs.apply(&self.norm1)?)?;
+        let (q, k, v) = split_qkv(&qkv, self.n_heads, self.head_dim)?;
+        let ctx = flash_attention(&q, &k, &v, packed, None)?.reshape((t, d))?;
+        let xs = (xs + ctx.apply(&self.out_proj)?.to_dtype(DType::F32)?)?;
+        let ff = project(&self.linear1, &xs.apply(&self.norm2)?)?
+            .relu()?
+            .apply(&self.linear2)?
+            .to_dtype(DType::F32)?;
+        Ok((xs + ff)?)
+    }
+}
+
+/// `nn.MultiheadAttention` stores q/k/v as one `in_proj_weight`
+/// `(3d, d)` and `in_proj_bias` `(3d)`, rows ordered q then k then v —
+/// exactly the layout one fused `Linear` wants.
+fn linear_torch_mha_in_proj(d: usize, vb: VarBuilder) -> Result<Linear> {
+    let w = vb.get((3 * d, d), "in_proj_weight")?;
+    let b = vb.get(3 * d, "in_proj_bias")?;
+    Ok(Linear::new(w, Some(b)))
+}
+
+pub struct DecisionHead {
+    type_emb: Embedding,
+    layers: Vec<HeadLayer>,
+    scorer_norm: LayerNorm,
+    scorer_fc: Linear,
+    scorer_out: Linear,
+    act_fc: Linear,
+    act_out: Linear,
+}
+
+impl DecisionHead {
+    /// `vb` is the checkpoint root; the head's tensors are top-level.
+    /// Linear weights load in `vb`'s dtype; the type embedding and norms
+    /// in f32.
+    pub fn load(vb: VarBuilder, d: usize, head_layers: usize, n_act: usize) -> Result<Self> {
+        let layers = (0..head_layers)
+            .map(|i| HeadLayer::load(vb.pp(format!("head.layers.{i}")), d))
+            .collect::<Result<Vec<_>>>()?;
+        let f32_vb = vb.clone().set_dtype(DType::F32);
+        Ok(Self {
+            type_emb: candle_nn::embedding(3, d, f32_vb.pp("type_emb"))?,
+            layers,
+            scorer_norm: candle_nn::layer_norm(d, TORCH_LN_EPS, f32_vb.pp("scorer.0"))?,
+            scorer_fc: candle_nn::linear(d, d, vb.pp("scorer.1"))?,
+            scorer_out: candle_nn::linear(d, 1, vb.pp("scorer.3"))?,
+            act_fc: candle_nn::linear(d + 4, 256, vb.pp("act_head.0"))?,
+            act_out: candle_nn::linear(256, n_act, vb.pp("act_head.2"))?,
+        })
+    }
+
+    /// Type embedding plus the transformer layers. `h` is the encoder
+    /// output `(b, l, d)`, `qtype` is `(b,)` u32.
+    pub fn contextualise(
+        &self,
+        h: &Tensor,
+        qtype: &Tensor,
+        key_mask: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let mut h = h.broadcast_add(&qtype.apply(&self.type_emb)?.unsqueeze(1)?)?;
+        for layer in &self.layers {
+            h = layer.forward(&h, key_mask)?;
+        }
+        Ok(h)
+    }
+
+    /// The flash path: `h` is the packed encoder output `(total, d)` and
+    /// `qtype` the per-token question type `(total,)` u32.
+    #[cfg(feature = "flash-attn")]
+    pub fn contextualise_packed(
+        &self,
+        h: &Tensor,
+        qtype: &Tensor,
+        packed: &super::backbone::Packed,
+    ) -> Result<Tensor> {
+        let mut h = (h + qtype.apply(&self.type_emb)?)?;
+        for layer in &self.layers {
+            h = layer.forward_packed(&h, packed)?;
+        }
+        Ok(h)
+    }
+
+    /// Score gathered marker states `(b, k, d)` → `(b, k)` f32 logits.
+    pub fn score(&self, markers: &Tensor) -> Result<Tensor> {
+        let x = project(&self.scorer_fc, &markers.apply(&self.scorer_norm)?)?
+            .gelu_erf()?
+            .apply(&self.scorer_out)?;
+        Ok(x.squeeze(2)?.to_dtype(DType::F32)?)
+    }
+
+    /// Act logits `(b, n_act)` f32 from the pooled first position
+    /// `(b, d)` and the four answer-distribution features `(b, 4)`.
+    pub fn act(&self, pooled: &Tensor, feats: &Tensor) -> Result<Tensor> {
+        let x = Tensor::cat(&[pooled, &feats.to_dtype(pooled.dtype())?], 1)?;
+        let x = project(&self.act_fc, &x)?
+            .gelu_erf()?
+            .apply(&self.act_out)?;
+        Ok(x.to_dtype(DType::F32)?)
+    }
+}
@@ -0,0 +1,64 @@
+//! Laya — a non-autoregressive decision model (`convaiinnovations/laya`).
+//!
+//! Not a language model: a ModernBERT encoder plus a small decision
+//! head that scores the options of a typed question in one forward
+//! pass. The caller assembles each question into one token sequence
+//! (`[CLS] <type> question: <instructions> [SEP] [MASK] opt0 [MASK]
+//! opt1 … [SEP] state [SEP]`) and says where each option's `[MASK]`
+//! marker sits; this module returns one raw score per marker plus the
+//! act head's distribution. Calibration (temperature scaling) and
+//! answer shaping happen after, CPU-side, from the checkpoint's
+//! `rl_agent_config.json`.
+//!
+//! The reference implementation is the `laya` SDK's `DecisionModel`
+//! (`laya/common.py`), not the `rl_common.py` shipped in the model
+//! repository, which has diverged from it.
+//!
+//! - [`config`] — the two config files a checkpoint ships.
+//! - [`backbone`] — ModernBERT, vendored so it runs outside f32.
+//! - [`head`] — type embedding, the head's transformer layers, the
+//!   marker scorer and the act head.
+//! - [`model`] — the composition, batch in, [`DecisionRow`]s out.
+
+pub mod backbone;
+pub mod config;
+pub mod head;
+pub mod model;
+
+pub use config::CheckpointConfig;
+pub use model::LayaModel;
+
+/// Question type, as the checkpoint's type embedding indexes it.
+pub const QTYPE_CHOICE: u8 = 0;
+pub const QTYPE_SCORE: u8 = 1;
+pub const QTYPE_NOUL: u8 = 2;
+
+/// One assembled question: its token ids, where each option's
+/// `[MASK]` marker sits, and its type.
+#[derive(Debug, Clone, PartialEq)]
+pub struct DecisionSeq {
+    pub input_ids: Vec<u32>,
+    pub markers: Vec<usize>,
+    /// [`QTYPE_CHOICE`], [`QTYPE_SCORE`] or [`QTYPE_NOUL`].
+    pub qtype: u8,
+}
+
+/// Sequences scored in one forward. One request's questions normally
+/// travel together, so the state they share is encoded in one pass.
+#[derive(Debug, Clone, Default, PartialEq)]
+pub struct DecisionBatch {
+    pub seqs: Vec<DecisionSeq>,
+}
+
+/// One sequence's result, CPU-side.
+#[derive(Debug, Clone, PartialEq)]
+pub struct DecisionRow {
+    /// One uncalibrated score per marker, in marker order.
+    pub logits: Vec<f32>,
+    /// Softmax over the act head's outputs. Index 0 is what the
+    /// reference reports as `action.act_probability`.
+    pub act: Vec<f32>,
+}
+
+#[cfg(test)]
+mod tests;
@@ -0,0 +1,260 @@
+//! A whole Laya checkpoint: encoder + decision head, batch in, raw
+//! scores out.
+
+use anyhow::{Context, Result, ensure};
+use candle_core::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use std::path::Path;
+
+use super::backbone::{Masks, ModernBert};
+use super::config::CheckpointConfig;
+use super::head::DecisionHead;
+use super::{DecisionBatch, DecisionRow};
+
+pub struct LayaModel {
+    encoder: ModernBert,
+    head: DecisionHead,
+    config: CheckpointConfig,
+    device: Device,
+    /// Precision of the linear layers (the load dtype).
+    dtype: DType,
+}
+
+/// A validated batch, flattened: what both forward paths start from.
+struct Flat {
+    lens: Vec<usize>,
+    qtypes: Vec<u32>,
+    /// Each row's marker positions, padded to `k_max` with 0.
+    marker_idx: Vec<u32>,
+    k_max: usize,
+    seq_len: usize,
+}
+
+impl LayaModel {
+    /// Load one checkpoint directory (the repo root, or one of its
+    /// subfolders): `rl_agent_config.json`, `encoder/config.json` and
+    /// `model.safetensors`.
+    pub fn load(dir: &Path, device: &Device, dtype: DType) -> Result<Self> {
+        let config = CheckpointConfig::from_dir(dir)?;
+        let weights = dir.join("model.safetensors");
+        // SAFETY: the file is memory-mapped read-only for the lifetime
+        // of the VarBuilder; nothing writes to a checkpoint in place.
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[&weights], dtype, device) }
+            .with_context(|| format!("mmap {}", weights.display()))?;
+        Self::from_var_builder(vb, config, device)
+    }
+
+    pub fn from_var_builder(
+        vb: VarBuilder,
+        config: CheckpointConfig,
+        device: &Device,
+    ) -> Result<Self> {
+        let dtype = vb.dtype();
+        let encoder = ModernBert::load(vb.pp("encoder"), &config.encoder)?;
+        let head = DecisionHead::load(
+            vb.clone(),
+            config.encoder.hidden_size,
+            config.laya.head_layers,
+            config.laya.n_act(),
+        )?;
+        Ok(Self {
+            encoder,
+            head,
+            config,
+            device: device.clone(),
+            dtype,
+        })
+    }
+
+    pub fn config(&self) -> &CheckpointConfig {
+        &self.config
+    }
+
+    /// Precision the linear layers were loaded in.
+    pub fn dtype(&self) -> DType {
+        self.dtype
+    }
+
+    /// One forward over every sequence in `batch`.
+    ///
+    /// On a CUDA device in reduced precision, in a build with the
+    /// `flash-attn` feature, the batch is packed without padding and
+    /// attention runs in flash varlen kernels. Otherwise rows are
+    /// right-padded to the longest, as the reference collates them, and
+    /// attention is eager; padding changes nothing about an unpadded
+    /// row's result.
+    pub fn forward(&self, batch: &DecisionBatch) -> Result<Vec<DecisionRow>> {
+        let flat = self.validate(batch)?;
+        #[cfg(feature = "flash-attn")]
+        if self.device.is_cuda() && self.dtype != DType::F32 && flash_enabled() {
+            return self.forward_packed(batch, &flat);
+        }
+        self.forward_padded(batch, &flat)
+    }
+
+    fn validate(&self, batch: &DecisionBatch) -> Result<Flat> {
+        let seqs = &batch.seqs;
+        ensure!(!seqs.is_empty(), "empty decision batch");
+        let max_pos = self.config.encoder.max_position_embeddings;
+        let seq_len = seqs.iter().map(|s| s.input_ids.len()).max().unwrap_or(0);
+        ensure!(seq_len > 0, "decision batch has an empty sequence");
+        ensure!(
+            seq_len <= max_pos,
+            "sequence of {seq_len} tokens exceeds max_position_embeddings {max_pos}"
+        );
+        let vocab = self.config.encoder.vocab_size as u32;
+        let k_max = seqs.iter().map(|s| s.markers.len()).max().unwrap_or(0);
+        ensure!(
+            k_max > 0,
+            "decision batch has a sequence with no option markers"
+        );
+        let mut lens = Vec::with_capacity(seqs.len());
+        let mut qtypes = Vec::with_capacity(seqs.len());
+        let mut marker_idx = Vec::with_capacity(seqs.len() * k_max);
+        for s in seqs {
+            let n = s.input_ids.len();
+            ensure!(n > 0, "decision batch has an empty sequence");
+            ensure!(!s.markers.is_empty(), "sequence has no option markers");
+            ensure!(s.qtype < 3, "question type {} out of range", s.qtype);
+            if let Some(&bad) = s.input_ids.iter().find(|&&t| t >= vocab) {
+                anyhow::bail!("token id {bad} outside vocabulary of {vocab}");
+            }
+            if let Some(&bad) = s.markers.iter().find(|&&m| m >= n) {
+                anyhow::bail!("marker position {bad} outside a {n}-token sequence");
+            }
+            lens.push(n);
+            qtypes.push(s.qtype as u32);
+            marker_idx.extend(s.markers.iter().map(|&m| m as u32));
+            // Absent slots gather position 0; their scores are discarded.
+            marker_idx.extend(std::iter::repeat_n(0u32, k_max - s.markers.len()));
+        }
+        Ok(Flat {
+            lens,
+            qtypes,
+            marker_idx,
+            k_max,
+            seq_len,
+        })
+    }
+
+    /// The eager path: rows right-padded to `(b, seq_len)`.
+    fn forward_padded(&self, batch: &DecisionBatch, flat: &Flat) -> Result<Vec<DecisionRow>> {
+        let b = batch.seqs.len();
+        let seq_len = flat.seq_len;
+        let pad = self.config.encoder.pad_token_id;
+        let mut ids = Vec::with_capacity(b * seq_len);
+        for s in &batch.seqs {
+            ids.extend_from_slice(&s.input_ids);
+            ids.extend(std::iter::repeat_n(pad, seq_len - s.input_ids.len()));
+        }
+        let ids = Tensor::from_vec(ids, (b, seq_len), &self.device)?;
+        let masks = Masks::new(
+            &flat.lens,
+            seq_len,
+            self.encoder.half_window(),
+            &self.device,
+        )?;
+        let qtype = Tensor::from_vec(flat.qtypes.clone(), b, &self.device)?;
+
+        let h = self.encoder.forward(&ids, &masks)?;
+        let h = self.head.contextualise(&h, &qtype, masks.global.as_ref())?;
+        let d = self.config.encoder.hidden_size;
+        let idx = Tensor::from_vec(flat.marker_idx.clone(), (b, flat.k_max, 1), &self.device)?
+            .broadcast_as((b, flat.k_max, d))?
+            .contiguous()?;
+        let marker_h = h.gather(&idx, 1)?;
+        let pooled = h.narrow(1, 0, 1)?.squeeze(1)?;
+        self.finish(batch, &marker_h, &pooled)
+    }
+
+    /// The flash path: tokens packed without padding, `(total, d)`.
+    #[cfg(feature = "flash-attn")]
+    fn forward_packed(&self, batch: &DecisionBatch, flat: &Flat) -> Result<Vec<DecisionRow>> {
+        use super::backbone::Packed;
+        let b = batch.seqs.len();
+        let packed = Packed::new(&flat.lens, &self.device)?;
+        let total: usize = flat.lens.iter().sum();
+        let mut ids = Vec::with_capacity(total);
+        let mut qtype_tok = Vec::with_capacity(total);
+        for (s, &q) in batch.seqs.iter().zip(&flat.qtypes) {
+            ids.extend_from_slice(&s.input_ids);
+            qtype_tok.extend(std::iter::repeat_n(q, s.input_ids.len()));
+        }
+        let ids = Tensor::from_vec(ids, total, &self.device)?;
+        let qtype_tok = Tensor::from_vec(qtype_tok, total, &self.device)?;
+
+        let h = self.encoder.forward_packed(&ids, &packed)?;
+        let h = self.head.contextualise_packed(&h, &qtype_tok, &packed)?;
+        let d = self.config.encoder.hidden_size;
+        // Marker positions, and each row's first token, in packed order.
+        let global: Vec<u32> = flat
+            .marker_idx
+            .chunks(flat.k_max)
+            .zip(&packed.offsets)
+            .flat_map(|(ms, &off)| ms.iter().map(move |&m| off as u32 + m))
+            .collect();
+        let global = Tensor::from_vec(global, b * flat.k_max, &self.device)?;
+        let marker_h = h.index_select(&global, 0)?.reshape((b, flat.k_max, d))?;
+        let firsts: Vec<u32> = packed.offsets.iter().map(|&o| o as u32).collect();
+        let pooled = h.index_select(&Tensor::from_vec(firsts, b, &self.device)?, 0)?;
+        self.finish(batch, &marker_h, &pooled)
+    }
+
+    /// Score the gathered marker states `(b, k_max, d)` and run the act
+    /// head over the pooled first positions `(b, d)`.
+    fn finish(
+        &self,
+        batch: &DecisionBatch,
+        marker_h: &Tensor,
+        pooled: &Tensor,
+    ) -> Result<Vec<DecisionRow>> {
+        let b = batch.seqs.len();
+        let logits: Vec<Vec<f32>> = self.head.score(marker_h)?.to_vec2()?;
+        let mut feats = Vec::with_capacity(b * 4);
+        let mut row_logits = Vec::with_capacity(b);
+        for (s, full) in batch.seqs.iter().zip(logits) {
+            let row: Vec<f32> = full[..s.markers.len()].to_vec();
+            feats.extend_from_slice(&act_features(&row));
+            row_logits.push(row);
+        }
+        let feats = Tensor::from_vec(feats, (b, 4), &self.device)?;
+        let act_logits = self.head.act(pooled, &feats)?;
+        let act = candle_nn::ops::softmax_last_dim(&act_logits)?.to_vec2::<f32>()?;
+        Ok(row_logits
+            .into_iter()
+            .zip(act)
+            .map(|(logits, act)| DecisionRow { logits, act })
+            .collect())
+    }
+}
+
+/// Runtime kill switch for the flash path: `NEURON_FLASH_ATTN=0` (or
+/// `false`) forces eager attention without a rebuild — the same switch
+/// the text architectures read. Read once.
+#[cfg(feature = "flash-attn")]
+fn flash_enabled() -> bool {
+    static ENABLED: std::sync::OnceLock<bool> = std::sync::OnceLock::new();
+    *ENABLED
+        .get_or_init(|| !std::env::var("NEURON_FLASH_ATTN").is_ok_and(|v| v == "0" || v == "false"))
+}
+
+/// The act head's four inputs, from one row's uncalibrated logits:
+/// top-1 probability, top-1 minus top-2, entropy normalised by
+/// `ln(max(k, 2))`, and `k / 255`.
+///
+/// The reference computes these over the batch's padded width, with
+/// absent option slots filled with a logit of -1e4. Those slots carry
+/// exactly zero probability in f32, so computing per row over only the
+/// real options gives the same result.
+pub fn act_features(logits: &[f32]) -> [f32; 4] {
+    let max = logits.iter().copied().fold(f32::NEG_INFINITY, f32::max);
+    let exps: Vec<f32> = logits.iter().map(|&z| (z - max).exp()).collect();
+    let sum: f32 = exps.iter().sum();
+    let mut p: Vec<f32> = exps.iter().map(|e| e / sum).collect();
+    let k = (logits.len().max(2)) as f32;
+    let ent = -p.iter().map(|&x| x * x.max(1e-9).ln()).sum::<f32>() / k.ln();
+    p.sort_by(|a, b| b.total_cmp(a));
+    let top1 = p[0];
+    let top2 = p.get(1).copied().unwrap_or(0.0);
+    [top1, top1 - top2, ent, k / 255.0]
+}
@@ -0,0 +1,450 @@
+//! Parity against the upstream reference.
+//!
+//! `testdata/laya/tiny/` is a toy-sized checkpoint built and scored by
+//! the SDK's own `DecisionModel` (`script/generate_laya_fixture.py`);
+//! it runs in CI. `testdata/laya/reference.json` holds the released
+//! checkpoints' outputs over a real corpus (`script/laya-reference.py`);
+//! scoring it needs the weights, so that test is ignored unless
+//! `LAYA_REFERENCE_DIR` names a snapshot of `convaiinnovations/laya`.
+
+use super::*;
+use candle_core::{DType, Device};
+use serde::Deserialize;
+use std::path::{Path, PathBuf};
+
+fn testdata() -> PathBuf {
+    Path::new(env!("CARGO_MANIFEST_DIR")).join("src/harness/testdata/laya")
+}
+
+#[derive(Deserialize)]
+struct TinyExpected {
+    seqs: Vec<TinySeq>,
+}
+
+#[derive(Deserialize)]
+struct TinySeq {
+    input_ids: Vec<u32>,
+    markers: Vec<usize>,
+    qtype: u8,
+    logits: Vec<f32>,
+    act: Vec<f32>,
+}
+
+fn load_tiny() -> (LayaModel, TinyExpected) {
+    let dir = testdata().join("tiny");
+    let model = LayaModel::load(&dir, &Device::Cpu, DType::F32).expect("load tiny checkpoint");
+    let expected: TinyExpected = serde_json::from_str(
+        &std::fs::read_to_string(dir.join("expected.json")).expect("read expected.json"),
+    )
+    .expect("parse expected.json");
+    (model, expected)
+}
+
+fn batch_of(seqs: &[TinySeq]) -> DecisionBatch {
+    DecisionBatch {
+        seqs: seqs
+            .iter()
+            .map(|s| DecisionSeq {
+                input_ids: s.input_ids.clone(),
+                markers: s.markers.clone(),
+                qtype: s.qtype,
+            })
+            .collect(),
+    }
+}
+
+/// Device and precision for the weight-backed tests: `LAYA_DEVICE`
+/// (`cpu`, or `cuda:N` in a build with the `cuda` feature) and
+/// `LAYA_DTYPE` (`f32`, `bf16` or `f16`). Defaults: CPU, f32.
+fn test_device() -> (Device, DType) {
+    let device = match std::env::var("LAYA_DEVICE").as_deref() {
+        Ok("cpu") | Err(_) => Device::Cpu,
+        Ok(s) => {
+            let ordinal = s
+                .strip_prefix("cuda:")
+                .and_then(|n| n.parse().ok())
+                .unwrap_or_else(|| panic!("LAYA_DEVICE={s}: expected cpu or cuda:N"));
+            Device::new_cuda(ordinal).unwrap_or_else(|e| panic!("LAYA_DEVICE={s}: {e}"))
+        }
+    };
+    let dtype = match std::env::var("LAYA_DTYPE").as_deref() {
+        Ok("f32") | Err(_) => DType::F32,
+        Ok("bf16") => DType::BF16,
+        Ok("f16") => DType::F16,
+        Ok(s) => panic!("LAYA_DTYPE={s}: expected f32, bf16 or f16"),
+    };
+    (device, dtype)
+}
+
+/// Logit tolerance against the f32 reference for a given precision.
+/// Reduced precision is judged mainly on argmax agreement; the bound
+/// only catches a broken kernel, not rounding.
+fn logit_tolerance(dtype: DType) -> f32 {
+    if dtype == DType::F32 { 1e-3 } else { 1.0 }
+}
+
+fn max_abs_diff(a: &[f32], b: &[f32]) -> f32 {
+    assert_eq!(a.len(), b.len(), "length mismatch");
+    a.iter()
+        .zip(b)
+        .map(|(x, y)| (x - y).abs())
+        .fold(0.0, f32::max)
+}
+
+/// The whole forward — encoder with both attention kinds and rotary
+/// bases, padding, type embedding, head layers, marker gather, scorer
+/// and act head — against the reference, in f32.
+#[test]
+fn tiny_checkpoint_matches_reference() {
+    let (model, expected) = load_tiny();
+    let rows = model.forward(&batch_of(&expected.seqs)).expect("forward");
+    assert_eq!(rows.len(), expected.seqs.len());
+    for (i, (got, want)) in rows.iter().zip(&expected.seqs).enumerate() {
+        let dl = max_abs_diff(&got.logits, &want.logits);
+        let da = max_abs_diff(&got.act, &want.act);
+        assert!(
+            dl < 1e-5 && da < 1e-5,
+            "row {i}: logit diff {dl:e}, act diff {da:e}\n got {:?} / {:?}\nwant {:?} / {:?}",
+            got.logits,
+            got.act,
+            want.logits,
+            want.act
+        );
+    }
+}
+
+/// A row's result must not depend on what it was batched with: padded
+/// keys are masked out of every attention, including the head's.
+#[test]
+fn padding_does_not_change_a_row() {
+    let (model, expected) = load_tiny();
+    let batched = model.forward(&batch_of(&expected.seqs)).expect("batched");
+    for (i, seq) in expected.seqs.iter().enumerate() {
+        let alone = model
+            .forward(&batch_of(std::slice::from_ref(seq)))
+            .expect("single");
+        let dl = max_abs_diff(&alone[0].logits, &batched[i].logits);
+        let da = max_abs_diff(&alone[0].act, &batched[i].act);
+        assert!(dl < 1e-5 && da < 1e-5, "row {i}: {dl:e} / {da:e}");
+    }
+}
+
+#[test]
+fn malformed_batches_are_refused() {
+    let (model, _) = load_tiny();
+    let seq = |ids: Vec<u32>, markers: Vec<usize>, qtype| DecisionBatch {
+        seqs: vec![DecisionSeq {
+            input_ids: ids,
+            markers,
+            qtype,
+        }],
+    };
+    assert!(model.forward(&DecisionBatch::default()).is_err());
+    assert!(model.forward(&seq(vec![1, 2, 3], vec![], 0)).is_err());
+    assert!(model.forward(&seq(vec![1, 2, 3], vec![3], 0)).is_err());
+    assert!(model.forward(&seq(vec![1, 2, 3], vec![1], 3)).is_err());
+    assert!(model.forward(&seq(vec![1, 500, 3], vec![1], 0)).is_err());
+}
+
+#[test]
+fn act_features_match_the_reference_definition() {
+    // Two equal logits: top1 = top2 = 0.5, entropy exactly 1.
+    let f = model::act_features(&[0.3, 0.3]);
+    assert!((f[0] - 0.5).abs() < 1e-6 && f[1].abs() < 1e-6 && (f[2] - 1.0).abs() < 1e-6);
+    assert!((f[3] - 2.0 / 255.0).abs() < 1e-7);
+    // One option: probability 1, no second, k clamps to 2.
+    let f = model::act_features(&[4.0]);
+    assert_eq!(f[0], 1.0);
+    assert_eq!(f[1], 1.0);
+    assert!(f[2].abs() < 1e-6);
+    assert!((f[3] - 2.0 / 255.0).abs() < 1e-7);
+}
+
+// ---------------------------------------------------------------- released checkpoints
+
+#[derive(Deserialize)]
+struct Reference {
+    cases: Vec<RefCase>,
+}
+
+#[derive(Deserialize)]
+struct RefCase {
+    name: String,
+    items: Vec<RefItem>,
+    response: serde_json::Value,
+}
+
+#[derive(Deserialize)]
+struct RefItem {
+    input_ids: Vec<u32>,
+    markers: Vec<usize>,
+    qtype: u8,
+    logits: Vec<f32>,
+    act: Vec<f32>,
+}
+
+/// Every case in `reference.json`, scored by the checkpoint the
+/// reference routed it to, on the device and precision
+/// [`test_device`] selects (f32 on the CPU by default).
+///
+/// `LAYA_REFERENCE_DIR` is a snapshot of `convaiinnovations/laya` at the
+/// revision recorded in the fixture header: the root holds the English
+/// checkpoint, `multilingual/` and `typed-decisions/` the other two.
+#[test]
+#[ignore = "needs the released weights: set LAYA_REFERENCE_DIR"]
+fn released_checkpoints_match_reference() {
+    let Ok(root) = std::env::var("LAYA_REFERENCE_DIR") else {
+        panic!("LAYA_REFERENCE_DIR is not set");
+    };
+    let root = PathBuf::from(root);
+    let (device, dtype) = test_device();
+    let tolerance = logit_tolerance(dtype);
+    let reference: Reference = serde_json::from_str(
+        &std::fs::read_to_string(testdata().join("reference.json")).expect("read reference.json"),
+    )
+    .expect("parse reference.json");
+
+    // The reference's own GPU run (bf16 autocast), for judging reduced
+    // precision against the drift upstream itself serves with.
+    let torch_bf16: Option<Reference> =
+        std::fs::read_to_string(testdata().join("reference_cuda_bf16.json"))
+            .ok()
+            .map(|t| serde_json::from_str(&t).expect("parse reference_cuda_bf16.json"));
+    // Per checkpoint: summed centred drift of ours and of the reference's
+    // bf16 run, their maxima, and the row count.
+    let mut drift: std::collections::BTreeMap<String, (f32, f32, f32, f32, usize)> =
+        Default::default();
+
+    let mut models = std::collections::BTreeMap::new();
+    let mut stats: std::collections::BTreeMap<String, (usize, f32, f32, usize)> =
+        Default::default();
+    // Forward wall time and tokens encoded, per checkpoint: a first
+    // CPU number, not a benchmark (debug builds are far slower).
+    let mut timing: std::collections::BTreeMap<String, (f64, usize)> = Default::default();
+    for case in &reference.cases {
+        let ckpt = case.response["routing"]["model"]
+            .as_str()
+            .expect("routing.model")
+            .to_string();
+        let model = models.entry(ckpt.clone()).or_insert_with(|| {
+            let dir = match ckpt.as_str() {
+                "english" => root.clone(),
+                other => root.join(other),
+            };
+            LayaModel::load(&dir, &device, dtype).unwrap_or_else(|e| panic!("load {ckpt}: {e:#}"))
+        });
+        let batch = DecisionBatch {
+            seqs: case
+                .items
+                .iter()
+                .map(|it| DecisionSeq {
+                    input_ids: it.input_ids.clone(),
+                    markers: it.markers.clone(),
+                    qtype: it.qtype,
+                })
+                .collect(),
+        };
+        let started = std::time::Instant::now();
+        let rows = model
+            .forward(&batch)
+            .unwrap_or_else(|e| panic!("{}: {e:#}", case.name));
+        let t = timing.entry(ckpt.clone()).or_default();
+        t.0 += started.elapsed().as_secs_f64() * 1e3;
+        t.1 += batch.seqs.iter().map(|s| s.input_ids.len()).sum::<usize>();
+        let torch_case = torch_bf16
+            .as_ref()
+            .and_then(|r| r.cases.iter().find(|c| c.name == case.name));
+        for (i, (got, want)) in rows.iter().zip(&case.items).enumerate() {
+            let d = drift.entry(ckpt.clone()).or_default();
+            let ours = centred_diff(&got.logits, &want.logits);
+            d.0 += ours;
+            d.2 = d.2.max(ours);
+            if let Some(t) = torch_case.and_then(|c| c.items.get(i)) {
+                let theirs = centred_diff(&t.logits, &want.logits);
+                d.1 += theirs;
+                d.3 = d.3.max(theirs);
+            }
+            d.4 += 1;
+        }
+        let entry = stats.entry(ckpt.clone()).or_default();
+        for (got, want) in rows.iter().zip(&case.items) {
+            let dl = max_abs_diff(&got.logits, &want.logits);
+            let da = max_abs_diff(&got.act, &want.act);
+            let argmax = |v: &[f32]| {
+                v.iter()
+                    .enumerate()
+                    .max_by(|a, b| a.1.total_cmp(b.1))
+                    .map(|(i, _)| i)
+            };
+            entry.0 += 1;
+            entry.1 = entry.1.max(dl);
+            entry.2 = entry.2.max(da);
+            if argmax(&got.logits) == argmax(&want.logits) {
+                entry.3 += 1;
+            }
+            if dtype != DType::F32 && std::env::var_os("LAYA_VERBOSE").is_some() {
+                eprintln!("  {:<40} {ckpt:<16} |Δlogit| {dl:.3}", case.name);
+            }
+            assert!(
+                dl < tolerance,
+                "{} ({ckpt}): logit diff {dl:e}\n got {:?}\nwant {:?}",
+                case.name,
+                got.logits,
+                want.logits
+            );
+            assert!(da < tolerance, "{} ({ckpt}): act diff {da:e}", case.name);
+        }
+    }
+    for (ckpt, (n, dl, da, agree)) in &stats {
+        let (ms, tokens) = timing[ckpt];
+        eprintln!(
+            "[{device:?} {dtype:?}] {ckpt}: {n} questions, max |Δlogit| {dl:.2e}, max |Δact| {da:.2e}, argmax {agree}/{n}; \
+             {tokens} tokens in {ms:.0} ms ({:.2} ms/token)",
+            ms / tokens as f64
+        );
+        assert_eq!(agree, n, "{ckpt}: argmax disagreement");
+    }
+    // Reduced precision: centred drift (softmax ignores a constant shift)
+    // against f32, next to the reference's own bf16 drift. Per-case drift
+    // is chaotic, so the gate is the mean: no worse than twice upstream's,
+    // plus 0.01 — about one bf16 ulp at these logits' magnitude — so a
+    // checkpoint with a single fixture case whose reference drift happens
+    // to round to nothing is not held to zero.
+    for (ckpt, (ours, theirs, ours_max, theirs_max, n)) in &drift {
+        let (ours, theirs) = (ours / *n as f32, theirs / *n as f32);
+        eprintln!(
+            "[{device:?} {dtype:?}] {ckpt}: centred drift mean {ours:.4} (reference bf16 {theirs:.4}), \
+             max {ours_max:.3} (reference bf16 {theirs_max:.3})"
+        );
+        if dtype != DType::F32 && torch_bf16.is_some() {
+            assert!(
+                ours <= 2.0 * theirs + 0.01,
+                "{ckpt}: mean drift {ours:.4} exceeds twice the reference's own bf16 drift {theirs:.4}"
+            );
+        }
+    }
+}
+
+/// Largest difference between two logit rows after removing each row's
+/// mean — the part of a difference a softmax can see.
+fn centred_diff(a: &[f32], b: &[f32]) -> f32 {
+    let mean = |v: &[f32]| v.iter().sum::<f32>() / v.len() as f32;
+    let (ma, mb) = (mean(a), mean(b));
+    a.iter()
+        .zip(b)
+        .map(|(x, y)| ((x - ma) - (y - mb)).abs())
+        .fold(0.0, f32::max)
+}
+
+/// Forward latency for the four workloads #337 benchmarks against the
+/// PyTorch reference, plus batch throughput, on the English checkpoint.
+///
+/// Sequences are real ones from `reference.json`: `choice_basic` is the
+/// short state (41 tokens), `multi_question` five questions over it, and
+/// `state_right_truncated` a full 512-token sequence (repeated for the
+/// five-question and throughput batches). The timed span is the whole
+/// `forward` call — host-to-device copy, forward, and the CPU readback
+/// that ends it — so it is directly comparable to the reference's
+/// end-to-end `predict()` minus tokenisation.
+///
+/// Run with `cargo test --release -p neuron [--features cuda] --
+/// --ignored --nocapture laya_forward_timing`, with `LAYA_REFERENCE_DIR`
+/// and optionally `LAYA_DEVICE` / `LAYA_DTYPE` set.
+#[test]
+#[ignore = "benchmark: needs the released weights (LAYA_REFERENCE_DIR)"]
+fn laya_forward_timing() {
+    let root = PathBuf::from(std::env::var("LAYA_REFERENCE_DIR").expect("LAYA_REFERENCE_DIR"));
+    let (device, dtype) = test_device();
+    let reference: Reference = serde_json::from_str(
+        &std::fs::read_to_string(testdata().join("reference.json")).expect("read reference.json"),
+    )
+    .expect("parse reference.json");
+    let seqs_of = |name: &str| -> Vec<DecisionSeq> {
+        reference
+            .cases
+            .iter()
+            .find(|c| c.name == name)
+            .unwrap_or_else(|| panic!("no case {name}"))
+            .items
+            .iter()
+            .map(|it| DecisionSeq {
+                input_ids: it.input_ids.clone(),
+                markers: it.markers.clone(),
+                qtype: it.qtype,
+            })
+            .collect()
+    };
+    let long = seqs_of("state_right_truncated").remove(0);
+    let workloads: Vec<(String, DecisionBatch)> = vec![
+        (
+            "short x1q".into(),
+            DecisionBatch {
+                seqs: seqs_of("choice_basic"),
+            },
+        ),
+        (
+            "short x5q".into(),
+            DecisionBatch {
+                seqs: seqs_of("multi_question"),
+            },
+        ),
+        (
+            "512tok x1q".into(),
+            DecisionBatch {
+                seqs: vec![long.clone()],
+            },
+        ),
+        (
+            "512tok x5q".into(),
+            DecisionBatch {
+                seqs: vec![long.clone(); 5],
+            },
+        ),
+        (
+            "512tok x16".into(),
+            DecisionBatch {
+                seqs: vec![long.clone(); 16],
+            },
+        ),
+        (
+            "512tok x64".into(),
+            DecisionBatch {
+                seqs: vec![long.clone(); 64],
+            },
+        ),
+    ];
+
+    let model = LayaModel::load(&root, &device, dtype).expect("load English checkpoint");
+    // `LAYA_TIMING_ONLY` keeps the workloads whose name contains it, and
+    // `LAYA_TIMING_ITERS` sets the timed iterations — for profiling one
+    // workload under a tracer.
+    let only = std::env::var("LAYA_TIMING_ONLY").ok();
+    let iters: usize = std::env::var("LAYA_TIMING_ITERS")
+        .ok()
+        .and_then(|v| v.parse().ok())
+        .unwrap_or(20);
+    for (name, batch) in &workloads {
+        if only.as_deref().is_some_and(|o| !name.contains(o)) {
+            continue;
+        }
+        for _ in 0..3 {
+            model.forward(batch).expect("warm-up forward");
+        }
+        let mut ms: Vec<f64> = (0..iters)
+            .map(|_| {
+                let started = std::time::Instant::now();
+                model.forward(batch).expect("forward");
+                started.elapsed().as_secs_f64() * 1e3
+            })
+            .collect();
+        ms.sort_by(f64::total_cmp);
+        let median = ms[ms.len() / 2];
+        let tokens: usize = batch.seqs.iter().map(|s| s.input_ids.len()).sum();
+        eprintln!(
+            "[{device:?} {dtype:?}] {name:>11}: median {median:7.1} ms, min {:7.1} ms, \
+             {tokens} tokens, {:.0} seq/s",
+            ms[0],
+            batch.seqs.len() as f64 / (median / 1e3)
+        );
+    }
+}
@@ -20,6 +20,7 @@
 //! TP-aware analogues live in `harness/tp/tp_<family>.rs` and follow
 //! the pattern set by `tp_qwen3.rs`.

+pub mod laya;
 pub mod phase_probe;
 pub mod qwen3_5;
 pub mod qwen4_exp;
@@ -143,6 +143,9 @@ pub enum LoadedHandle {
    Tp(Arc<TpLoadedModel>),
    /// Z-Image text-to-image pipeline (#198).
    Image(Arc<LoadedImageModel>),
+    /// Laya decision model (#336): typed questions in, calibrated
+    /// option scores out, no text generation.
+    Decision(Arc<super::decision_model::LoadedDecisionModel>),
 }

 impl LoadedHandle {
@@ -152,6 +155,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => &m.model_id,
            LoadedHandle::Image(m) => &m.model_id,
+            LoadedHandle::Decision(m) => &m.model_id,
        }
    }

@@ -162,6 +166,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => &m.spec,
            LoadedHandle::Image(m) => &m.spec,
+            LoadedHandle::Decision(m) => &m.spec,
        }
    }

@@ -171,6 +176,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.devices.clone(),
            LoadedHandle::Image(m) => m.devices.clone(),
+            LoadedHandle::Decision(m) => m.devices.clone(),
        }
    }

@@ -184,6 +190,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.poisoned.load(Ordering::Acquire),
            LoadedHandle::Image(m) => m.poisoned.load(Ordering::Acquire),
+            LoadedHandle::Decision(m) => m.poisoned.load(Ordering::Acquire),
        }
    }

@@ -195,6 +202,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => (m.admission.in_flight(), m.admission.queue_depth()),
            LoadedHandle::Image(m) => (m.admission.in_flight(), m.admission.queue_depth()),
+            LoadedHandle::Decision(m) => (m.admission.in_flight(), m.admission.queue_depth()),
        }
    }

@@ -207,6 +215,9 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => (m.admission.max_in_flight(), m.admission.max_queue_depth()),
            LoadedHandle::Image(m) => (m.admission.max_in_flight(), m.admission.max_queue_depth()),
+            LoadedHandle::Decision(m) => {
+                (m.admission.max_in_flight(), m.admission.max_queue_depth())
+            }
        }
    }

@@ -230,6 +241,10 @@ impl LoadedHandle {
                m.admission.anon_in_flight(),
                m.admission.anon_max_in_flight(),
            ),
+            LoadedHandle::Decision(m) => (
+                m.admission.anon_in_flight(),
+                m.admission.anon_max_in_flight(),
+            ),
        }
    }

@@ -240,6 +255,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.admission.close(),
            LoadedHandle::Image(m) => m.admission.close(),
+            LoadedHandle::Decision(m) => m.admission.close(),
        }
    }

@@ -251,6 +267,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.admission.rejections(),
            LoadedHandle::Image(m) => m.admission.rejections(),
+            LoadedHandle::Decision(m) => m.admission.rejections(),
        }
    }

@@ -262,6 +279,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => &m.admission,
            LoadedHandle::Image(m) => &m.admission,
+            LoadedHandle::Decision(m) => &m.admission,
        };
        (admission.kv_budget_mb(), admission.kv_available_mb())
    }
@@ -275,7 +293,7 @@ impl LoadedHandle {
            LoadedHandle::Tp(m) => &m.prefill_rate,
            // Image models have no token throughput; steps/sec lives in
            // the per-request timing instead.
-            LoadedHandle::Image(_) => return (0.0, 0.0),
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => return (0.0, 0.0),
        };
        (ema.get().unwrap_or(0.0), ema.decode().unwrap_or(0.0))
    }
@@ -287,12 +305,15 @@ impl LoadedHandle {
        if matches!(self, LoadedHandle::Image(_)) {
            return vec!["image".to_string()];
        }
+        if matches!(self, LoadedHandle::Decision(_)) {
+            return vec!["decision".to_string()];
+        }
        let mut caps = vec!["text".to_string()];
        let has_vision = match self {
            LoadedHandle::Single(m) => m.has_vision,
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.has_vision,
-            LoadedHandle::Image(_) => unreachable!("handled above"),
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => unreachable!("handled above"),
        };
        if has_vision {
            caps.push("vision".to_string());
@@ -338,7 +359,7 @@ impl LoadedHandle {
            LoadedHandle::Tp(m) => (m.last_free_mb.load(Ordering::Acquire), m.context_profile),
            // Image generation has its own VRAM path and no prefill
            // floor, so this check would say nothing useful about it.
-            LoadedHandle::Image(_) => return None,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => return None,
        };
        // `0` is the CPU-load and missing-worker sentinel — the same one
        // `check_vram` skips on. Nothing to assert either way.
@@ -419,7 +440,7 @@ impl LoadedHandle {
            LoadedHandle::Single(m) => m.tool_call_tokens.is_some(),
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.tool_call_tokens.is_some(),
-            LoadedHandle::Image(_) => false,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => false,
        }
    }

@@ -430,7 +451,7 @@ impl LoadedHandle {
            LoadedHandle::Single(m) => m.reasoning_tokens.is_some(),
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.reasoning_tokens.is_some(),
-            LoadedHandle::Image(_) => false,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => false,
        }
    }

@@ -441,7 +462,7 @@ impl LoadedHandle {
            LoadedHandle::Single(m) => m.preserve_thinking,
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.preserve_thinking,
-            LoadedHandle::Image(_) => None,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => None,
        }
    }

@@ -451,7 +472,7 @@ impl LoadedHandle {
            LoadedHandle::Single(m) => m.reasoning_efforts.clone(),
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.reasoning_efforts.clone(),
-            LoadedHandle::Image(_) => Default::default(),
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => Default::default(),
        }
    }

@@ -477,7 +498,7 @@ impl LoadedHandle {
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.query_vram_tightest_free_mb().await,
            // No context-limit derivation for image models; nothing to cache.
-            LoadedHandle::Image(_) => return,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => return,
        };
        // Don't clobber a good cached value with a transient `0`
        // (worker gone/poisoned sentinel).
@@ -486,7 +507,7 @@ impl LoadedHandle {
                LoadedHandle::Single(m) => m.last_free_mb.store(free, Ordering::Release),
                #[cfg(feature = "cuda")]
                LoadedHandle::Tp(m) => m.last_free_mb.store(free, Ordering::Release),
-                LoadedHandle::Image(_) => {}
+                LoadedHandle::Image(_) | LoadedHandle::Decision(_) => {}
            }
        }
    }
@@ -515,7 +536,7 @@ impl LoadedHandle {
                m.prefill_rate.get(),
            ),
            // Token limits are meaningless for image generation.
-            LoadedHandle::Image(_) => return None,
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => return None,
        };
        let rate = rate.unwrap_or(cfg.bootstrap_prefill_tok_per_sec);
        // The KV budget admission will actually grant. Advertising past
@@ -525,7 +546,9 @@ impl LoadedHandle {
            LoadedHandle::Single(m) => m.admission.kv_budget_mb(),
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(m) => m.admission.kv_budget_mb(),
-            LoadedHandle::Image(_) => unreachable!("derived_limit returned above"),
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => {
+                unreachable!("derived_limit returned above")
+            }
        };
        let limit = super::context_limit::derive_limit(
            &profile,
@@ -544,7 +567,9 @@ impl LoadedHandle {
                LoadedHandle::Single(m) => &m.derived_input_cap,
                #[cfg(feature = "cuda")]
                LoadedHandle::Tp(m) => &m.derived_input_cap,
-                LoadedHandle::Image(_) => unreachable!("derived_limit returned above"),
+                LoadedHandle::Image(_) | LoadedHandle::Decision(_) => {
+                    unreachable!("derived_limit returned above")
+                }
            };
            cap.store(input, Ordering::Release);
        }
@@ -3282,7 +3307,7 @@ impl CandleHarness {
            LoadedHandle::Tp(m) => {
                return self.inference_tp_stream(m, request, principal, wire).await;
            }
-            LoadedHandle::Image(_) => {
+            LoadedHandle::Image(_) | LoadedHandle::Decision(_) => {
                return Err(InferenceError::WrongModality {
                    model_id: request.model.clone(),
                });
@@ -4001,6 +4026,11 @@ impl Harness for CandleHarness {
        if matches!(plan.format, super::preflight::SourceFormat::Diffusers) {
            return self.load_image_model(spec, &source_id).await;
        }
+        // Laya decision models (#336) take the decision path: one
+        // device, unquantized (preflight rejected tp>1 and quant).
+        if matches!(plan.format, super::preflight::SourceFormat::DecisionModel) {
+            return self.load_decision_model(spec, &source_id).await;
+        }

        let tp_size = spec.tensor_parallel.unwrap_or(1);
        if tp_size > 1 {
@@ -4367,6 +4397,11 @@ impl Harness for CandleHarness {
                    );
                }
            }
+            LoadedHandle::Decision(dm) => {
+                // Each checkpoint's weights drop on the thread that owns
+                // them (the device worker, for a CUDA load).
+                dm.release().await;
+            }
            #[cfg(feature = "cuda")]
            LoadedHandle::Tp(tp) => {
                // Try to recover the inner TpLoadedModel so we can move
@@ -4541,6 +4576,132 @@ impl CandleHarness {
        Ok(())
    }

+    /// Load a Laya decision model (#336): resolve the checkpoints' files,
+    /// build them on the device worker (or the CPU), and register the
+    /// model with `capabilities: ["decision"]`.
+    ///
+    /// The repo root is required. A released family also carries
+    /// `multilingual/` and `typed-decisions/` beside it (#339); each one
+    /// the repo has is loaded too, so the `/v1/systemone` handler can
+    /// route non-English states to the multilingual checkpoint and honour
+    /// a request that pins one. A repo without them (a single fine-tune)
+    /// serves its root alone.
+    async fn load_decision_model(
+        &self,
+        spec: &ModelSpec,
+        source_id: &cortex_core::source::ModelSourceId,
+    ) -> Result<()> {
+        let devices = spec.devices.clone().unwrap_or_else(|| vec![0]);
+        let device = Self::pick_device(&devices)?;
+        #[cfg(feature = "cuda")]
+        let worker = if matches!(device, Device::Cuda(_)) {
+            Some(self.ensure_device_worker(devices[0]).await?)
+        } else {
+            None
+        };
+        // CPU build: always the CPU backend. Correct, but at hundreds of
+        // milliseconds per question it is a fallback, not a serving tier.
+        #[cfg(not(feature = "cuda"))]
+        let worker = {
+            let _ = &device;
+            None
+        };
+
+        let files = self.resolve_decision_files(source_id, None).await?;
+        let root = super::decision_model::DecisionCheckpoint::load(
+            super::decision_model::ROOT_CHECKPOINT.to_string(),
+            files,
+            &spec.model_id,
+            worker.clone(),
+        )
+        .await?;
+        let dtype = root.dtype;
+        let mut checkpoints = vec![root];
+        for sub in super::decision::route::FAMILY
+            .iter()
+            .filter(|c| **c != super::decision_model::ROOT_CHECKPOINT)
+        {
+            let files = match self.resolve_decision_files(source_id, Some(sub)).await {
+                Ok(files) => files,
+                Err(e) => {
+                    tracing::info!(
+                        model = %spec.model_id,
+                        checkpoint = sub,
+                        error = %format!("{e:#}"),
+                        "decision checkpoint not available; serving without it"
+                    );
+                    continue;
+                }
+            };
+            checkpoints.push(
+                super::decision_model::DecisionCheckpoint::load(
+                    sub.to_string(),
+                    files,
+                    &spec.model_id,
+                    worker.clone(),
+                )
+                .await?,
+            );
+        }
+        let names: Vec<&str> = checkpoints.iter().map(|c| c.name.as_str()).collect();
+        tracing::info!(model = %spec.model_id, checkpoints = ?names, "decision checkpoints loaded");
+
+        let loaded = super::decision_model::LoadedDecisionModel {
+            model_id: spec.model_id.clone(),
+            spec: spec.clone(),
+            devices,
+            checkpoints,
+            poisoned: Arc::new(AtomicBool::new(false)),
+            admission: crate::harness::admission::AdmissionController::new(&self.admission_cfg),
+        };
+        let mut models = self.models.write().await;
+        models.insert(
+            spec.model_id.clone(),
+            LoadedHandle::Decision(Arc::new(loaded)),
+        );
+        tracing::info!(model = %spec.model_id, dtype = ?dtype, "decision model loaded");
+        Ok(())
+    }
+
+    /// Fetch one Laya checkpoint's files through hf-hub: the repo root
+    /// for `subfolder = None`, else that subfolder. Only the files the
+    /// checkpoint needs are fetched, not the whole repo.
+    async fn resolve_decision_files(
+        &self,
+        source_id: &cortex_core::source::ModelSourceId,
+        subfolder: Option<&str>,
+    ) -> Result<super::decision_model::CheckpointFiles> {
+        let api = self.hf_api_for(&source_id.scheme)?;
+        let repo = api.model(source_id.repo_path());
+        let display_id = source_id.to_string();
+        let prefix = subfolder.map(|s| format!("{s}/")).unwrap_or_default();
+        let mut fetched = Vec::new();
+        for file in [
+            "rl_agent_config.json",
+            "encoder/config.json",
+            "model.safetensors",
+            "tokenizer/tokenizer.json",
+            "tokenizer/tokenizer_config.json",
+        ] {
+            let path = format!("{prefix}{file}");
+            fetched.push(
+                repo.get(&path)
+                    .await
+                    .with_context(|| format!("fetch {path} from {display_id}"))?,
+            );
+        }
+        // hf-hub materialises a snapshot as a directory tree mirroring
+        // the repo, so the checkpoint directory is where its config sits.
+        let dir = fetched[0]
+            .parent()
+            .context("checkpoint config has no parent directory")?
+            .to_path_buf();
+        Ok(super::decision_model::CheckpointFiles {
+            dir,
+            tokenizer: fetched[3].clone(),
+        })
+    }
+
    /// Resolve a diffusers-layout Z-Image repo into local component
    /// paths, downloading through hf-hub as needed. Verifies the
    /// pipeline class before fetching multi-GB weight shards.
@@ -4654,6 +4815,212 @@ impl CandleHarness {
        }
    }

+    /// The loaded decision model `model_id` (#336), for the
+    /// `/v1/systemone` handler: it takes the admission permit, scores
+    /// through [`super::decision_model::DecisionCheckpoint::decide`],
+    /// and shapes the answers. A model of another kind is a
+    /// `WrongModality`, as for images.
+    pub async fn decision_model(
+        &self,
+        model_id: &str,
+    ) -> Result<Arc<super::decision_model::LoadedDecisionModel>, InferenceError> {
+        let handle = {
+            let models = self.models.read().await;
+            models.get(model_id).cloned()
+        };
+        match handle {
+            Some(LoadedHandle::Decision(m)) => Ok(m),
+            Some(_) => Err(InferenceError::WrongModality {
+                model_id: model_id.to_string(),
+            }),
+            None => Err(InferenceError::ModelNotLoaded(model_id.to_string())),
+        }
+    }
+
+    /// Answer one `/v1/systemone` request (#338): pick the served decision
+    /// model and the checkpoint of it that answers, assemble the question
+    /// sequences, score them in one forward pass, and shape the answers.
+    ///
+    /// The served model is `served` when the gateway routed the request
+    /// (the `x-helexa-decision-model` header), else the model the body's
+    /// `model` names if one is loaded, else the one decision model this
+    /// node serves. The body's `model` is then read a second time, as the
+    /// reference server reads it: as an optional pin to a checkpoint of
+    /// the family (`multilingual`, `typed-decisions`, …).
+    ///
+    /// Questions are validated and tokenised before admission, so a
+    /// malformed request never takes a slot.
+    pub async fn systemone(
+        &self,
+        served: Option<&str>,
+        request: cortex_core::decisions::SystemOneRequest,
+        principal: Option<&str>,
+    ) -> Result<cortex_core::decisions::SystemOneResponse, SystemOneError> {
+        use super::decision::{self, route};
+
+        let model = match served {
+            Some(id) => self.decision_model(id).await?,
+            None => {
+                self.default_decision_model(request.model.as_deref())
+                    .await?
+            }
+        };
+        if model.poisoned.load(Ordering::Acquire) {
+            return Err(InferenceError::Other(anyhow::anyhow!(
+                "decision model '{}' is poisoned; unload and reload it",
+                model.model_id
+            ))
+            .into());
+        }
+
+        let pin = route::resolve_pin(request.model.as_deref());
+        let mut routing = route::route(&model.model_id, &request.state, &request.questions, pin);
+        let checkpoint = match model.checkpoint(Some(&routing.model)) {
+            Some(c) => c,
+            None if pin.is_some() => {
+                return Err(decision::DecisionRequestError::new(
+                    404,
+                    format!(
+                        "checkpoint '{}' is not served by '{}' on this node",
+                        routing.model, model.model_id
+                    ),
+                )
+                .into());
+            }
+            // Routed (not pinned) to a checkpoint this repo does not
+            // carry: answer with the default one, and say so.
+            None => {
+                let fallback = model.checkpoint(None).ok_or_else(|| {
+                    InferenceError::Other(anyhow::anyhow!(
+                        "decision model '{}' has no checkpoints",
+                        model.model_id
+                    ))
+                })?;
+                routing.reason = format!(
+                    "{}; the {} checkpoint is not loaded, so {} answered",
+                    routing.reason, routing.model, fallback.name
+                );
+                routing.model = fallback.name.clone();
+                routing.repo = route::repo_of(&model.model_id, &fallback.name);
+                fallback
+            }
+        };
+        let lang = route::detected_language(&routing).map(str::to_string);
+
+        let prepared = decision::prepare(
+            &request.state,
+            &request.questions,
+            &checkpoint.calibration,
+            &checkpoint.tokenizer,
+            decision::Budget {
+                max_len: request.max_len,
+                head_max_len: request.head_max_len,
+            },
+        )?;
+
+        let _permit = model.admission.enter(principal).await?;
+        let outputs: Vec<decision::RowOutput> = if prepared.rows.is_empty() {
+            Vec::new()
+        } else {
+            let batch = super::arch::laya::DecisionBatch {
+                seqs: prepared
+                    .rows
+                    .iter()
+                    .map(|r| super::arch::laya::DecisionSeq {
+                        input_ids: r.input_ids.clone(),
+                        markers: r.markers.clone(),
+                        qtype: r.qtype as u8,
+                    })
+                    .collect(),
+            };
+            checkpoint
+                .decide(batch)
+                .await
+                .map_err(|e| InferenceError::Other(anyhow::anyhow!("decision forward: {e:#}")))?
+                .into_iter()
+                .map(|row| decision::RowOutput {
+                    act_probability: row.act.first().copied().unwrap_or(0.0),
+                    logits: row.logits,
+                })
+                .collect()
+        };
+        let (answers, usage) = decision::shape(
+            &prepared,
+            &outputs,
+            &checkpoint.calibration,
+            lang.as_deref(),
+        );
+        Ok(decision::shape::response(answers, usage, Some(routing)))
+    }
+
+    /// Register an already-built decision model, bypassing the hub fetch
+    /// `load_model` does — for tests that serve local checkpoints.
+    #[cfg(test)]
+    pub(crate) async fn register_decision_model(
+        &self,
+        model: super::decision_model::LoadedDecisionModel,
+    ) {
+        self.models.write().await.insert(
+            model.model_id.clone(),
+            LoadedHandle::Decision(Arc::new(model)),
+        );
+    }
+
+    /// The decision model to serve a request no gateway routed: the one
+    /// `requested` names if it is loaded, else the only (first) loaded
+    /// decision model.
+    async fn default_decision_model(
+        &self,
+        requested: Option<&str>,
+    ) -> Result<Arc<super::decision_model::LoadedDecisionModel>, InferenceError> {
+        let models = self.models.read().await;
+        if let Some(id) = requested
+            && let Some(handle) = models.get(id)
+        {
+            return match handle {
+                LoadedHandle::Decision(m) => Ok(Arc::clone(m)),
+                _ => Err(InferenceError::WrongModality {
+                    model_id: id.to_string(),
+                }),
+            };
+        }
+        let mut decision: Vec<&Arc<super::decision_model::LoadedDecisionModel>> = models
+            .values()
+            .filter_map(|h| match h {
+                LoadedHandle::Decision(m) => Some(m),
+                _ => None,
+            })
+            .collect();
+        decision.sort_by(|a, b| a.model_id.cmp(&b.model_id));
+        decision
+            .first()
+            .map(|m| Arc::clone(m))
+            .ok_or_else(|| InferenceError::ModelNotLoaded("no decision model is loaded".into()))
+    }
+
+    /// Render one audio generation (#382). The cortex job manager sends one
+    /// of these per job and waits for the whole render; the reply is the
+    /// WAV bytes plus the result metadata cortex records on the job.
+    ///
+    /// No audio architecture is loadable yet, so every model reachable
+    /// here is the wrong modality or not loaded. The ACE-Step engine
+    /// (#383) adds a `LoadedHandle::Audio` arm that validates against the
+    /// model's limits, takes an admission permit and runs the device job.
+    pub async fn audio_generation(
+        &self,
+        request: &cortex_core::audio::AudioGenerationRequest,
+        _principal: Option<&str>,
+    ) -> Result<(Vec<u8>, cortex_core::audio::NeuronAudioResult), InferenceError> {
+        let loaded = self.models.read().await.contains_key(&request.model);
+        if loaded {
+            Err(InferenceError::WrongModality {
+                model_id: request.model.clone(),
+            })
+        } else {
+            Err(InferenceError::ModelNotLoaded(request.model.clone()))
+        }
+    }
+
    /// Run one text-to-image generation (#198). This is the internal
    /// path the `/v1/images/generations` handler (#200) fronts;
    /// admission, validation, tokenization, and PNG encoding happen
@@ -6278,6 +6645,33 @@ pub enum InferenceError {
    Other(#[from] anyhow::Error),
 }

+/// Why a `/v1/systemone` request was not answered (#338): a request the
+/// reference server would also reject (its status and `detail` text), or
+/// a serving failure shared with the other endpoints.
+#[derive(Debug)]
+pub enum SystemOneError {
+    Request(super::decision::DecisionRequestError),
+    Inference(InferenceError),
+}
+
+impl From<super::decision::DecisionRequestError> for SystemOneError {
+    fn from(e: super::decision::DecisionRequestError) -> Self {
+        SystemOneError::Request(e)
+    }
+}
+
+impl From<InferenceError> for SystemOneError {
+    fn from(e: InferenceError) -> Self {
+        SystemOneError::Inference(e)
+    }
+}
+
+impl From<super::admission::AdmissionRejection> for SystemOneError {
+    fn from(e: super::admission::AdmissionRejection) -> Self {
+        SystemOneError::Inference(e.into())
+    }
+}
+
 impl From<super::admission::AdmissionRejection> for InferenceError {
    fn from(rejection: super::admission::AdmissionRejection) -> Self {
        use super::admission::AdmissionRejection;
@@ -57,6 +57,9 @@ pub fn classify_files(
 ) -> Vec<String> {
    match preflight::classify(filenames) {
        SourceFormat::Diffusers => vec!["image".to_string()],
+        // A decision model answers typed questions (`/v1/systemone`);
+        // it generates no text, so it must not claim "text".
+        SourceFormat::DecisionModel => vec!["decision".to_string()],
        // A repo with no recognised weights (tokenizer-only, or an empty
        // entry) is not servable, so claiming "text" would be a lie of
        // the same kind this module exists to remove.
@@ -101,6 +104,22 @@ mod tests {
        );
    }

+    #[test]
+    fn laya_repo_is_a_decision_model() {
+        let files = [
+            "rl_agent_config.json",
+            "encoder/config.json",
+            "model.safetensors",
+            "tokenizer/tokenizer.json",
+        ];
+        assert_eq!(
+            classify_files(&files, || panic!(
+                "config must not be read for a decision model"
+            )),
+            vec!["decision".to_string()]
+        );
+    }
+
    #[test]
    fn dense_repo_without_vision_config_is_text_only() {
        let files = ["config.json", "tokenizer.json", "model.safetensors"];
@@ -0,0 +1,233 @@
+//! Per-checkpoint decision settings, read from `rl_agent_config.json`.
+//!
+//! Parsed with [`Json`], not `serde_json`: the fitted temperatures are
+//! written with 17 significant digits, and `serde_json`'s default float
+//! parser is not correctly rounded (it reads `1.7601518630981445` as
+//! `…443`).
+
+use cortex_core::decisions::Json;
+use std::collections::HashMap;
+
+/// Default token budgets when a checkpoint's config omits them.
+pub const DEFAULT_MAX_LEN: usize = 512;
+pub const DEFAULT_HEAD_MAX_LEN: usize = 192;
+
+/// Bounds on a usable calibration temperature. A fitted value below 1
+/// sharpens instead of softening; the shipped `choice:11+` bucket is 0.1006,
+/// which would publish a coin flip as a certainty, so the reference refuses
+/// to apply anything outside this range.
+pub const TEMP_MIN: f64 = 0.5;
+pub const TEMP_MAX: f64 = 5.0;
+
+/// A temperature confined to [`TEMP_MIN`, `TEMP_MAX`]; a non-finite or
+/// non-numeric value falls back to 1.0.
+pub fn clamp_temperature(t: Option<f64>) -> f64 {
+    match t {
+        Some(t) if t.is_finite() => t.clamp(TEMP_MIN, TEMP_MAX),
+        _ => 1.0,
+    }
+}
+
+/// Python's `float(v)` for a JSON value: numbers, bools, and numeric strings.
+fn py_float(v: &Json) -> Option<f64> {
+    match v {
+        Json::Int(d) => d.parse().ok(),
+        Json::Float(x) => Some(*x),
+        Json::Bool(b) => Some(if *b { 1.0 } else { 0.0 }),
+        Json::String(s) => s.trim().parse().ok(),
+        _ => None,
+    }
+}
+
+/// One set of calibration temperatures: per question type, overridden per
+/// `(type, option-count bucket)` (see [`temp_bucket`]). Already clamped.
+#[derive(Debug, Clone, PartialEq)]
+pub struct Temperatures {
+    /// Indexed by [`super::QType`] (`choice`, `score`, `noul`).
+    pub by_type: [f64; 3],
+    /// `"choice:3-5"` → temperature, and so on.
+    pub by_options: HashMap<String, f64>,
+}
+
+impl Temperatures {
+    /// The temperature for a question of type index `qtype` with `k` options.
+    pub fn for_question(&self, qtype: usize, k: usize) -> f64 {
+        self.by_options
+            .get(&temp_bucket(qtype, k))
+            .copied()
+            .unwrap_or(self.by_type[qtype])
+    }
+
+    fn from_json(
+        temperature: Option<&Json>,
+        by_options: Option<&Json>,
+        fallback: Option<[f64; 3]>,
+    ) -> Result<Self, String> {
+        let raw = match temperature {
+            None => fallback.unwrap_or([1.0; 3]).map(Some),
+            Some(Json::Array(a)) if a.len() == 3 => {
+                [py_float(&a[0]), py_float(&a[1]), py_float(&a[2])]
+            }
+            Some(other) => {
+                return Err(format!(
+                    "temperature must be a list of 3 floats, got {}",
+                    other.py_repr()
+                ));
+            }
+        };
+        let by_options = match by_options {
+            Some(Json::Object(m)) => m
+                .iter()
+                .map(|(k, v)| (k.clone(), clamp_temperature(py_float(v))))
+                .collect(),
+            _ => HashMap::new(),
+        };
+        Ok(Self {
+            by_type: raw.map(clamp_temperature),
+            by_options,
+        })
+    }
+}
+
+/// The calibration bucket of a question: its type and option count.
+pub fn temp_bucket(qtype: usize, k: usize) -> String {
+    let size = match k {
+        0..=2 => "2",
+        3..=5 => "3-5",
+        6..=10 => "6-10",
+        _ => "11+",
+    };
+    format!("{}:{size}", super::QType::NAMES[qtype])
+}
+
+/// What the sequence builder and the calibration need from a checkpoint.
+#[derive(Debug, Clone, PartialEq)]
+pub struct DecisionConfig {
+    /// Total sequence budget.
+    pub max_len: usize,
+    /// Budget for the question text and every option together.
+    pub head_max_len: usize,
+    pub temperatures: Temperatures,
+    /// Per-language overrides, keyed by the lower-cased primary subtag
+    /// (`"de"` for `de-AT`). Empty for the shipped checkpoints; an operator
+    /// may fit them per language.
+    pub lang_temperatures: HashMap<String, Temperatures>,
+}
+
+impl DecisionConfig {
+    /// Parse a checkpoint's `rl_agent_config.json`.
+    pub fn from_json(text: &str) -> Result<Self, String> {
+        let v = Json::parse(text.as_bytes()).map_err(|e| format!("rl_agent_config.json: {e}"))?;
+        let usize_or = |key: &str, default: usize| match v.get(key) {
+            Some(Json::Int(d)) => d.parse().unwrap_or(default),
+            _ => default,
+        };
+        Ok(Self {
+            max_len: usize_or("max_len", DEFAULT_MAX_LEN),
+            head_max_len: usize_or("head_max_len", DEFAULT_HEAD_MAX_LEN),
+            temperatures: Temperatures::from_json(
+                v.get("temperature"),
+                v.get("temperature_by_options"),
+                None,
+            )?,
+            lang_temperatures: HashMap::new(),
+        })
+    }
+
+    /// Add per-language temperatures, the shape the reference takes them in:
+    /// `{"de": {"temperature": [..3], "temperature_by_options": {..}}}`. A
+    /// language that omits `temperature` inherits the checkpoint's raw
+    /// per-type values.
+    pub fn with_lang_temperatures(mut self, spec: &Json) -> Result<Self, String> {
+        let Json::Object(m) = spec else {
+            return Err("lang_temperatures must be an object".into());
+        };
+        for (lang, cfg) in m {
+            let t = Temperatures::from_json(
+                cfg.get("temperature"),
+                cfg.get("temperature_by_options"),
+                Some(self.temperatures.by_type),
+            )
+            .map_err(|e| format!("language override {lang:?}: {e}"))?;
+            let primary = lang.split('-').next().unwrap_or(lang).to_lowercase();
+            self.lang_temperatures.insert(primary, t);
+        }
+        Ok(self)
+    }
+
+    /// The temperatures to apply for a request detected (or declared) as
+    /// `lang`, which may carry a region (`de-AT`).
+    pub fn temperatures_for(&self, lang: Option<&str>) -> &Temperatures {
+        lang.and_then(|l| {
+            let primary = l.split('-').next().unwrap_or(l).to_lowercase();
+            self.lang_temperatures.get(&primary)
+        })
+        .unwrap_or(&self.temperatures)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    const ENGLISH: &str = r#"{"encoder": "answerdotai/ModernBERT-large", "head_layers": 2,
+        "max_len": 512, "head_max_len": 192,
+        "temperature": [1.6369030475616455, 1.2514300346374512, 1.983399510383606],
+        "temperature_by_options": {"choice:3-5": 1.7601518630981445, "choice:11+": 0.10058280825614929}}"#;
+
+    #[test]
+    fn shipped_choice_11_bucket_is_clamped() {
+        let c = DecisionConfig::from_json(ENGLISH).unwrap();
+        assert_eq!(c.temperatures.for_question(0, 12), TEMP_MIN);
+        assert_eq!(c.temperatures.for_question(0, 4), 1.7601518630981445);
+        // No bucket: the per-type value.
+        assert_eq!(c.temperatures.for_question(1, 7), 1.2514300346374512);
+    }
+
+    #[test]
+    fn buckets_split_where_the_reference_splits() {
+        let b: Vec<_> = [1, 2, 3, 5, 6, 10, 11]
+            .iter()
+            .map(|&k| temp_bucket(2, k))
+            .collect();
+        assert_eq!(
+            b,
+            [
+                "noul:2",
+                "noul:2",
+                "noul:3-5",
+                "noul:3-5",
+                "noul:6-10",
+                "noul:6-10",
+                "noul:11+"
+            ]
+        );
+    }
+
+    #[test]
+    fn language_overrides_match_on_primary_subtag() {
+        let c = DecisionConfig::from_json(ENGLISH)
+            .unwrap()
+            .with_lang_temperatures(
+                &Json::parse(br#"{"DE": {"temperature": [1.0, 1.4, 9.0]}}"#).unwrap(),
+            )
+            .unwrap();
+        assert_eq!(
+            c.temperatures_for(Some("de-AT")).by_type,
+            [1.0, 1.4, TEMP_MAX]
+        );
+        assert_eq!(c.temperatures_for(Some("fr")), &c.temperatures);
+        assert_eq!(c.temperatures_for(None), &c.temperatures);
+    }
+
+    #[test]
+    fn bad_temperatures_are_refused_or_neutralised() {
+        assert!(DecisionConfig::from_json(r#"{"temperature": [1.0]}"#).is_err());
+        let c = DecisionConfig::from_json(r#"{"temperature": [" 2.5 ", "x", null]}"#).unwrap();
+        assert_eq!(c.temperatures.by_type, [2.5, 1.0, 1.0]);
+        assert_eq!(
+            (c.max_len, c.head_max_len),
+            (DEFAULT_MAX_LEN, DEFAULT_HEAD_MAX_LEN)
+        );
+    }
+}
@@ -0,0 +1,249 @@
+//! `CandleHarness::systemone` end to end: request in, routed, validated,
+//! scored and shaped response out (#338).
+//!
+//! The tiny checkpoint (`testdata/laya/tiny/`) exercises the plumbing in
+//! CI. The released-weights test replays every reference case through
+//! the same entry point the HTTP handler calls and compares the whole
+//! response — answers, usage and routing — with what the reference
+//! server returned; it needs `LAYA_REFERENCE_DIR`.
+
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::sync::atomic::AtomicBool;
+
+use cortex_core::decisions::{Json, SystemOneRequest, limits};
+
+use crate::harness::candle::{CandleHarness, SystemOneError};
+use crate::harness::decision_model::{CheckpointFiles, DecisionCheckpoint, LoadedDecisionModel};
+
+fn testdata() -> PathBuf {
+    Path::new(env!("CARGO_MANIFEST_DIR")).join("src/harness/testdata/laya")
+}
+
+fn harness() -> Arc<CandleHarness> {
+    CandleHarness::new(
+        "http://localhost:13131".into(),
+        &crate::config::CandleHarnessConfig::default(),
+    )
+}
+
+/// A decision model made of the given `(routing name, directory)`
+/// checkpoints, loaded on the CPU in f32.
+async fn decision_model(model_id: &str, checkpoints: &[(&str, PathBuf)]) -> LoadedDecisionModel {
+    let mut loaded = Vec::new();
+    for (name, dir) in checkpoints {
+        let files = CheckpointFiles {
+            tokenizer: if dir.join("tokenizer/tokenizer.json").exists() {
+                dir.join("tokenizer/tokenizer.json")
+            } else {
+                dir.join("tokenizer.json")
+            },
+            dir: dir.clone(),
+        };
+        loaded.push(
+            DecisionCheckpoint::load(name.to_string(), files, model_id, None)
+                .await
+                .unwrap_or_else(|e| panic!("load {name}: {e:#}")),
+        );
+    }
+    LoadedDecisionModel {
+        model_id: model_id.to_string(),
+        spec: serde_json::from_value(serde_json::json!({
+            "model_id": model_id,
+            "harness": "candle",
+        }))
+        .expect("model spec"),
+        devices: vec![0],
+        checkpoints: loaded,
+        poisoned: Arc::new(AtomicBool::new(false)),
+        admission: crate::harness::admission::AdmissionController::new(
+            &crate::config::AdmissionConfig::default(),
+        ),
+    }
+}
+
+fn request(body: &str) -> SystemOneRequest {
+    SystemOneRequest::parse(body.as_bytes(), limits::DEFAULT_MAX_TOKEN_BUDGET)
+        .expect("valid request")
+}
+
+async fn tiny_harness() -> Arc<CandleHarness> {
+    let h = harness();
+    h.register_decision_model(
+        decision_model("test/tiny", &[("english", testdata().join("tiny"))]).await,
+    )
+    .await;
+    h
+}
+
+#[tokio::test]
+async fn answers_every_question_in_order_with_exact_usage() {
+    let h = tiny_harness().await;
+    let req = request(
+        r#"{"state": "a b a", "questions": {
+            "z_last_alphabetically": {"type": "noul", "instructions": "a?"},
+            "a_first": {"type": "choice", "instructions": "b?", "criteria": ["a", "b", "a b"]}
+        }}"#,
+    );
+    let resp = h.systemone(None, req, None).await.expect("answered");
+    let json = serde_json::to_value(&resp).unwrap();
+    // Answers come back in request order, not sorted.
+    let keys: Vec<&str> = resp.answers.0.iter().map(|(k, _)| k.as_str()).collect();
+    assert_eq!(keys, ["z_last_alphabetically", "a_first"]);
+    assert_eq!(json["answers"]["z_last_alphabetically"]["type"], "noul");
+    let probs = json["answers"]["a_first"]["probabilities"]
+        .as_object()
+        .unwrap();
+    let total: f64 = probs.values().map(|p| p.as_f64().unwrap()).sum();
+    assert!((total - 1.0).abs() < 1e-3, "{probs:?}");
+    // Usage is the exact number of tokens encoded, output tokens zero.
+    assert!(json["usage"]["input_tokens"].as_u64().unwrap() > 0);
+    assert_eq!(json["usage"]["output_tokens"], 0);
+    assert_eq!(json["routing"]["model"], "english");
+    assert_eq!(json["routing"]["repo"], "test/tiny");
+}
+
+#[tokio::test]
+async fn pinning_a_checkpoint_the_model_lacks_is_404() {
+    let h = tiny_harness().await;
+    let req = request(
+        r#"{"state": "a", "model": "multilingual",
+            "questions": {"q": {"type": "noul", "instructions": "a?"}}}"#,
+    );
+    match h.systemone(None, req, None).await {
+        Err(SystemOneError::Request(e)) => assert_eq!(e.status, 404, "{}", e.detail),
+        other => panic!("expected a 404, got {other:?}"),
+    }
+}
+
+#[tokio::test]
+async fn routing_to_a_missing_checkpoint_falls_back_and_says_so() {
+    // Devanagari routes to `multilingual`, which this model lacks.
+    let h = tiny_harness().await;
+    let req = request(
+        r#"{"state": "मुझे पैसे वापस चाहिए",
+            "questions": {"q": {"type": "noul", "instructions": "a?"}}}"#,
+    );
+    let resp = h.systemone(None, req, None).await.expect("answered");
+    let routing = resp.routing.expect("routing");
+    assert_eq!(routing.model, "english");
+    assert!(
+        routing
+            .reason
+            .contains("multilingual checkpoint is not loaded"),
+        "{}",
+        routing.reason
+    );
+}
+
+#[tokio::test]
+async fn invalid_questions_are_rejected_before_admission() {
+    let h = tiny_harness().await;
+    let req =
+        request(r#"{"state": "a", "questions": {"q": {"type": "maybe", "instructions": "a?"}}}"#);
+    match h.systemone(None, req, None).await {
+        Err(SystemOneError::Request(e)) => assert_eq!(e.status, 422, "{}", e.detail),
+        other => panic!("expected a 422, got {other:?}"),
+    }
+}
+
+#[tokio::test]
+async fn a_gateway_routed_model_that_is_not_a_decision_model_is_refused() {
+    let h = tiny_harness().await;
+    let req = request(r#"{"state": "a", "questions": {}}"#);
+    match h.systemone(Some("not/loaded"), req, None).await {
+        Err(SystemOneError::Inference(crate::harness::candle::InferenceError::ModelNotLoaded(
+            id,
+        ))) => {
+            assert_eq!(id, "not/loaded")
+        }
+        other => panic!("expected ModelNotLoaded, got {other:?}"),
+    }
+}
+
+/// Every reference case through `systemone`, compared with the reference
+/// server's whole response. Routing and usage must match exactly;
+/// probabilities to within the engine's measured f32 distance from the
+/// reference (a last-digit wobble after 4-decimal rounding); the chosen
+/// answer exactly.
+#[tokio::test]
+#[ignore = "needs the released weights: set LAYA_REFERENCE_DIR"]
+async fn released_family_answers_like_the_reference_server() {
+    let root = PathBuf::from(std::env::var("LAYA_REFERENCE_DIR").expect("LAYA_REFERENCE_DIR"));
+    let h = harness();
+    h.register_decision_model(
+        decision_model(
+            "convaiinnovations/laya",
+            &[
+                ("english", root.clone()),
+                ("multilingual", root.join("multilingual")),
+                ("typed-decisions", root.join("typed-decisions")),
+            ],
+        )
+        .await,
+    )
+    .await;
+
+    let text = std::fs::read_to_string(testdata().join("reference.json")).unwrap();
+    let reference = Json::parse(text.as_bytes()).unwrap();
+    let Some(Json::Array(cases)) = reference.get("cases") else {
+        panic!("no cases")
+    };
+    let mut max_dp = 0.0f64;
+    for case in cases {
+        let name = case.get("name").and_then(Json::as_str).unwrap();
+        let body = serde_json::to_vec(case.get("request").unwrap()).unwrap();
+        let req = SystemOneRequest::parse(&body, limits::DEFAULT_MAX_TOKEN_BUDGET).unwrap();
+        let resp = h
+            .systemone(Some("convaiinnovations/laya"), req, None)
+            .await
+            .unwrap_or_else(|e| panic!("{name}: {e:?}"));
+        let want_json = case.get("response").unwrap();
+        // Routing and usage compared as serialized text through `Json`:
+        // exact, key order included. serde_json would re-round the
+        // reference's floats on the way in.
+        for key in ["routing", "usage", "model"] {
+            let got = match key {
+                "routing" => serde_json::to_string(&resp.routing).unwrap(),
+                "usage" => serde_json::to_string(&resp.usage).unwrap(),
+                _ => serde_json::to_string(&resp.model).unwrap(),
+            };
+            let want = serde_json::to_string(want_json.get(key).unwrap()).unwrap();
+            assert_eq!(got, want, "{name}: {key}");
+        }
+        let got = serde_json::to_value(&resp).unwrap();
+        let want: serde_json::Value =
+            serde_json::from_str(&serde_json::to_string(want_json).unwrap()).unwrap();
+        let (ga, wa) = (
+            got["answers"].as_object().unwrap(),
+            want["answers"].as_object().unwrap(),
+        );
+        assert_eq!(
+            ga.keys().collect::<Vec<_>>(),
+            wa.keys().collect::<Vec<_>>(),
+            "{name}: answer ids"
+        );
+        for (qid, w) in wa {
+            let g = &ga[qid];
+            for key in ["choice", "type", "legend"] {
+                assert_eq!(g.get(key), w.get(key), "{name}/{qid}: {key}");
+            }
+            if let Some(wp) = w["probabilities"].as_object() {
+                for (opt, p) in wp {
+                    let d = (g["probabilities"][opt].as_f64().unwrap() - p.as_f64().unwrap()).abs();
+                    max_dp = max_dp.max(d);
+                }
+            }
+            for key in ["noul", "score", "confidence", "answer_confidence"] {
+                if let (Some(gv), Some(wv)) = (
+                    g.get(key).and_then(|v| v.as_f64()),
+                    w.get(key).and_then(|v| v.as_f64()),
+                ) {
+                    max_dp = max_dp.max((gv - wv).abs());
+                }
+            }
+        }
+    }
+    eprintln!("released family: max |Δ| over probabilities and confidences {max_dp:.1e}");
+    assert!(max_dp <= 5e-4, "max |Δ| {max_dp:e}");
+}
@@ -0,0 +1,59 @@
+//! Laya decision models: everything around the forward pass (#335).
+//!
+//! A Laya checkpoint answers typed questions over a *state* (text, a JSON
+//! document, a conversation) in one encoder forward pass: each question
+//! becomes one sequence,
+//!
+//! ```text
+//! [CLS] "<type> question: <instructions>" [SEP] [MASK] opt0 [MASK] opt1 … [SEP] state [SEP]
+//! ```
+//!
+//! and the decision head scores the hidden state at each option's `[MASK]`
+//! marker. This module is the part that touches no tensor:
+//!
+//! - [`question`] — validate and normalise the question definitions, and
+//!   render each option's text;
+//! - [`sequence`] — assemble the token sequences under the model's token
+//!   budgets ([`prepare`]);
+//! - [`shape`] — turn per-option logits into calibrated, typed answers
+//!   ([`shape()`]);
+//! - [`config`] / [`tokenizer`] — the per-checkpoint pieces those need.
+//!
+//! The reference is the `laya` Python SDK (`laya/common.py`,
+//! `laya/agent.py`, `laya/serve.py`), whose behaviour this reproduces token
+//! for token: the parity tests replay fixtures recorded from it
+//! (`script/laya-reference.py`). The calibration arithmetic follows numpy's
+//! float32 evaluation order, so rounded probabilities match to the last
+//! published digit.
+//!
+//! The wire types (`/v1/systemone`) live in [`cortex_core::decisions`].
+
+pub mod config;
+pub mod question;
+pub mod route;
+pub mod sequence;
+pub mod shape;
+pub mod tokenizer;
+
+#[cfg(test)]
+mod handler_tests;
+
+pub use config::DecisionConfig;
+pub use cortex_core::decisions::DecisionRequestError;
+pub use question::{QType, Question};
+pub use sequence::{Budget, OptionStats, Prepared, Row, prepare};
+pub use shape::{RowOutput, shape};
+pub use tokenizer::{DecisionTokenizer, SpecialTokens, TextEncoder};
+
+/// The constant `model` field of every response: the decision head's name.
+/// Which checkpoint answered is reported in `routing`.
+pub const DECISION_MODEL_NAME: &str = "laya-rl-agent";
+
+/// A request error in the reference's `ValueError` class: the server
+/// answers it with 422 and the message as `detail`.
+pub(crate) fn unprocessable(detail: impl Into<String>) -> DecisionRequestError {
+    DecisionRequestError::new(422, detail)
+}
+
+#[cfg(test)]
+mod parity_tests;
@@ -0,0 +1,209 @@
+//! Replays the reference fixtures (`testdata/laya/reference.json`, recorded
+//! by `script/laya-reference.py`) through [`prepare`] and [`shape`].
+//!
+//! Token ids and marker positions must match exactly. The answers and usage
+//! must serialize to exactly the same JSON — same keys, same order, same
+//! rounded values — when fed the reference's own raw logits.
+//!
+//! The English tokenizer is committed. The multilingual one is 34 MB, so the
+//! multilingual cases run only when `LAYA_ML_TOKENIZER_DIR` names a copy of
+//! the checkpoint's `multilingual/tokenizer/` directory.
+
+use super::*;
+use cortex_core::decisions::{Json, SystemOneRequest, limits};
+use std::path::{Path, PathBuf};
+
+fn testdata() -> PathBuf {
+    Path::new(env!("CARGO_MANIFEST_DIR")).join("src/harness/testdata/laya")
+}
+
+fn reference() -> Json {
+    Json::parse(&std::fs::read(testdata().join("reference.json")).expect("fixtures")).expect("json")
+}
+
+fn to_value(j: &Json) -> serde_json::Value {
+    serde_json::to_value(j).expect("serializable")
+}
+
+fn tokenizer_for(checkpoint: &str) -> Option<DecisionTokenizer> {
+    let dir = match checkpoint {
+        // typed-decisions ships the English tokenizer, byte for byte.
+        "english" | "typed-decisions" => testdata().join("tokenizer"),
+        "multilingual" => PathBuf::from(std::env::var_os("LAYA_ML_TOKENIZER_DIR")?),
+        other => panic!("unknown checkpoint {other}"),
+    };
+    Some(DecisionTokenizer::from_dir(&dir).expect("tokenizer"))
+}
+
+fn config_for(reference: &Json, checkpoint: &str) -> DecisionConfig {
+    let cfg = reference
+        .get("header")
+        .and_then(|h| h.get("checkpoints"))
+        .and_then(|c| c.get(checkpoint))
+        .and_then(|c| c.get("cfg"))
+        .expect("checkpoint cfg");
+    DecisionConfig::from_json(&serde_json::to_string(cfg).unwrap()).unwrap()
+}
+
+fn array(j: &Json) -> &[Json] {
+    match j {
+        Json::Array(a) => a,
+        other => panic!("expected a list, got {other:?}"),
+    }
+}
+
+fn num(j: &Json) -> f64 {
+    match j {
+        Json::Int(d) => d.parse().unwrap(),
+        Json::Float(x) => *x,
+        other => panic!("expected a number, got {other:?}"),
+    }
+}
+
+/// Replay every case whose tokenizer is available; return how many ran per
+/// checkpoint.
+fn replay(filter: impl Fn(&str) -> bool) -> Vec<(String, usize)> {
+    let reference = reference();
+    let mut ran: Vec<(String, usize)> = Vec::new();
+    for case in array(reference.get("cases").unwrap()) {
+        let name = case.get("name").and_then(Json::as_str).unwrap();
+        if !filter(name) {
+            continue;
+        }
+        let response = case.get("response").unwrap();
+        let routing = response.get("routing").unwrap();
+        let checkpoint = routing.get("model").and_then(Json::as_str).unwrap();
+        let Some(tok) = tokenizer_for(checkpoint) else {
+            continue;
+        };
+        let cfg = config_for(&reference, checkpoint);
+
+        let body = serde_json::to_vec(case.get("request").unwrap()).unwrap();
+        let req = SystemOneRequest::parse(&body, limits::DEFAULT_MAX_TOKEN_BUDGET)
+            .unwrap_or_else(|e| panic!("{name}: {e}"));
+        let budget = Budget {
+            max_len: req.max_len,
+            head_max_len: req.head_max_len,
+        };
+        let prepared = prepare(&req.state, &req.questions, &cfg, &tok, budget)
+            .unwrap_or_else(|e| panic!("{name}: {e}"));
+
+        let items = array(case.get("items").unwrap());
+        assert_eq!(prepared.rows.len(), items.len(), "{name}: row count");
+        let mut outputs = Vec::new();
+        for (i, (row, item)) in prepared.rows.iter().zip(items).enumerate() {
+            let ids: Vec<u32> = array(item.get("input_ids").unwrap())
+                .iter()
+                .map(|v| num(v) as u32)
+                .collect();
+            let markers: Vec<usize> = array(item.get("markers").unwrap())
+                .iter()
+                .map(|v| num(v) as usize)
+                .collect();
+            assert_eq!(row.input_ids, ids, "{name}: row {i} input_ids");
+            assert_eq!(row.markers, markers, "{name}: row {i} markers");
+            assert_eq!(row.qtype as usize, num(item.get("qtype").unwrap()) as usize);
+            let opts = item.get("options").unwrap();
+            let s = prepared.stats[i];
+            assert_eq!(
+                (s.options, s.options_distinct, s.tokens_per_option),
+                (
+                    num(opts.get("options").unwrap()) as usize,
+                    num(opts.get("options_distinct").unwrap()) as usize,
+                    match opts.get("tokens_per_option").unwrap() {
+                        Json::Null => None,
+                        v => Some(num(v) as usize),
+                    }
+                ),
+                "{name}: row {i} option stats"
+            );
+            outputs.push(RowOutput {
+                logits: array(item.get("logits").unwrap())
+                    .iter()
+                    .map(|v| num(v) as f32)
+                    .collect(),
+                act_probability: num(&array(item.get("act").unwrap())[0]) as f32,
+            });
+        }
+
+        let lang = routing
+            .get("detection")
+            .and_then(|d| d.get("language"))
+            .and_then(Json::as_str);
+        let (answers, usage) = shape(&prepared, &outputs, &cfg, lang);
+        // Serialized strings, not values: key order is part of the contract.
+        assert_eq!(
+            serde_json::to_string(&answers).unwrap(),
+            serde_json::to_string(response.get("answers").unwrap()).unwrap(),
+            "{name}: answers"
+        );
+        assert_eq!(
+            serde_json::to_value(&usage).unwrap(),
+            to_value(response.get("usage").unwrap()),
+            "{name}: usage"
+        );
+        match ran.iter_mut().find(|(c, _)| c == checkpoint) {
+            Some((_, n)) => *n += 1,
+            None => ran.push((checkpoint.to_string(), 1)),
+        }
+    }
+    ran
+}
+
+#[test]
+fn every_english_fixture_matches_the_reference() {
+    let ran = replay(|_| true);
+    let count = |c: &str| ran.iter().find(|(n, _)| n == c).map_or(0, |(_, n)| *n);
+    // The corpus routes 19 cases to English and 1 to typed-decisions; a
+    // drop means a case silently stopped running.
+    assert_eq!(count("english"), 19, "{ran:?}");
+    assert_eq!(count("typed-decisions"), 1, "{ran:?}");
+    if std::env::var_os("LAYA_ML_TOKENIZER_DIR").is_some() {
+        assert_eq!(count("multilingual"), 8, "{ran:?}");
+    }
+}
+
+#[test]
+fn empty_questions_need_no_forward() {
+    let reference = reference();
+    let tok = tokenizer_for("english").unwrap();
+    let cfg = config_for(&reference, "english");
+    let prepared = prepare(
+        &Json::String("x".into()),
+        &[],
+        &cfg,
+        &tok,
+        Budget::default(),
+    )
+    .unwrap();
+    assert!(prepared.rows.is_empty());
+    let (answers, usage) = shape(&prepared, &[], &cfg, None);
+    assert_eq!(serde_json::to_string(&answers).unwrap(), "{}");
+    assert_eq!(
+        serde_json::to_string(&usage).unwrap(),
+        r#"{"input_tokens":0,"output_tokens":0}"#
+    );
+}
+
+#[test]
+fn options_that_cannot_all_fit_are_refused() {
+    let reference = reference();
+    let tok = tokenizer_for("english").unwrap();
+    let cfg = config_for(&reference, "english");
+    let questions = Json::parse(
+        br#"{"q": {"type": "choice", "instructions": "i", "criteria": ["a", "b", "c", "d"]}}"#,
+    )
+    .unwrap();
+    let Json::Object(questions) = questions else {
+        unreachable!()
+    };
+    let budget = Budget {
+        max_len: Some(10),
+        head_max_len: None,
+    };
+    let err = prepare(&Json::String("x".into()), &questions, &cfg, &tok, budget).unwrap_err();
+    assert_eq!(
+        (err.status, err.detail.as_str()),
+        (422, "question 'q' options exceed head_max_len=192")
+    );
+}
@@ -0,0 +1,458 @@
+//! Question validation, normalisation and option rendering.
+//!
+//! Every rejection here is a 422 whose message names the question and what
+//! to fix — the same text the reference returns, so a client written
+//! against it sees the same errors.
+
+use super::{DecisionRequestError, unprocessable};
+use cortex_core::decisions::{HashKey, Json};
+use std::collections::HashMap;
+
+/// The three decision primitives. The discriminant is the index the model's
+/// type embedding and the calibration tables use.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum QType {
+    /// Pick one of the caller's labels.
+    Choice = 0,
+    /// An ordinal level on the caller's rubric.
+    Score = 1,
+    /// Yes/no: the probability that a statement holds.
+    Noul = 2,
+}
+
+impl QType {
+    pub const NAMES: [&'static str; 3] = ["choice", "score", "noul"];
+
+    pub fn name(self) -> &'static str {
+        Self::NAMES[self as usize]
+    }
+
+    fn parse(s: &str) -> Option<Self> {
+        match s {
+            "choice" => Some(Self::Choice),
+            "score" => Some(Self::Score),
+            "noul" => Some(Self::Noul),
+            _ => None,
+        }
+    }
+}
+
+/// A validated question, normalised the way the reference normalises it.
+#[derive(Debug, Clone, PartialEq)]
+pub struct Question {
+    pub id: String,
+    pub kind: QType,
+    /// The instruction text: the caller's string, or the JSON rendering of
+    /// a structured value.
+    pub instructions: String,
+    /// Each option's rendered text, in answer-index order. For `noul` this
+    /// is always `[false, true]`, so index 1 is P(true).
+    pub options: Vec<String>,
+    /// `choice`: each option's label as the caller wrote it (the answer key).
+    /// `score`: each level's description (the legend). `noul`: empty.
+    pub labels: Vec<Json>,
+}
+
+impl Question {
+    /// Validate one question definition and normalise it.
+    pub fn from_definition(id: &str, def: &Json) -> Result<Self, DecisionRequestError> {
+        check_question(id, def)?;
+        Ok(to_internal(id, def))
+    }
+}
+
+/// Validate and normalise every question, in request order. The first
+/// invalid question is reported; nothing is normalised until all pass.
+pub fn questions_from_request(
+    questions: &[(String, Json)],
+) -> Result<Vec<Question>, DecisionRequestError> {
+    for (id, def) in questions {
+        check_question(id, def)?;
+    }
+    Ok(questions
+        .iter()
+        .map(|(id, def)| to_internal(id, def))
+        .collect())
+}
+
+fn q(id: &str) -> String {
+    Json::String(id.to_string()).py_repr()
+}
+
+fn check_question(id: &str, def: &Json) -> Result<(), DecisionRequestError> {
+    let qid = q(id);
+    if !matches!(def, Json::Object(_)) {
+        return Err(unprocessable(format!(
+            "question {qid}: definition must be a dict, got {}",
+            def.py_type_name()
+        )));
+    }
+    let t = def.get("type").unwrap_or(&Json::Null);
+    let Some(kind) = t.as_str().and_then(QType::parse) else {
+        return Err(unprocessable(format!(
+            "question {qid}: unknown type {}; use one of ['choice', 'noul', 'score']",
+            t.py_repr()
+        )));
+    };
+    if def.get("instructions").is_none() {
+        return Err(unprocessable(format!(
+            "question {qid}: no 'instructions'; add the text the model should answer"
+        )));
+    }
+    let crit = def.get("criteria");
+    match kind {
+        QType::Choice => check_choice(&qid, crit)?,
+        QType::Score => {
+            let Some(Json::Array(levels)) = crit else {
+                return Err(unprocessable(format!(
+                    "question {qid}: a score question takes 'criteria' as a list of level \
+                     descriptions, index 0 first"
+                )));
+            };
+            if levels.is_empty() {
+                return Err(unprocessable(format!(
+                    "question {qid}: a score question needs at least one level"
+                )));
+            }
+            if let Some(i) = levels.iter().position(|l| *l == Json::Null) {
+                return Err(unprocessable(format!(
+                    "question {qid}: score level {i} is null; give every level a description, \
+                     index 0 first"
+                )));
+            }
+        }
+        QType::Noul => match crit {
+            None | Some(Json::Null) => {}
+            Some(Json::Object(m)) => {
+                let mut keys: Vec<String> = m.iter().map(|(k, _)| k.to_lowercase()).collect();
+                keys.sort();
+                keys.dedup();
+                if keys.iter().any(|k| k != "true" && k != "false") {
+                    let listed = Json::Array(keys.into_iter().map(Json::String).collect());
+                    return Err(unprocessable(format!(
+                        "question {qid}: a noul question takes 'criteria' keyed only \
+                         'true'/'false' (either or both, and omitted is fine), got {}. Those \
+                         keys are the option texts the model reads; any other key was silently \
+                         dropped and replaced with the defaults. If you want the answer worded \
+                         differently, keep 'criteria' keyed 'true'/'false' and set 'labels' \
+                         instead.",
+                        listed.py_repr()
+                    )));
+                }
+            }
+            Some(_) => {
+                return Err(unprocessable(format!(
+                    "question {qid}: a noul question takes 'criteria' as a dict with optional \
+                     'true'/'false' descriptions, or omits it"
+                )));
+            }
+        },
+    }
+    if let Some(labels) = def.get("labels") {
+        if kind != QType::Noul {
+            return Err(unprocessable(format!(
+                "question {qid}: 'labels' is only supported for noul questions"
+            )));
+        }
+        resolve_noul_labels(labels).map_err(|e| unprocessable(format!("question {qid}: {e}")))?;
+    }
+    Ok(())
+}
+
+fn check_choice(qid: &str, crit: Option<&Json>) -> Result<(), DecisionRequestError> {
+    let labels: Vec<Json> = match crit {
+        Some(Json::Object(m)) => m.iter().map(|(k, _)| Json::String(k.clone())).collect(),
+        Some(Json::Array(a)) => a.clone(),
+        _ => {
+            return Err(unprocessable(format!(
+                "question {qid}: a choice question takes 'criteria' as a dict of label -> \
+                 description, or a list of labels"
+            )));
+        }
+    };
+    if labels.is_empty() {
+        return Err(unprocessable(format!(
+            "question {qid}: a choice question needs at least one criterion"
+        )));
+    }
+    for (i, label) in labels.iter().enumerate() {
+        match label {
+            Json::Array(_) | Json::Object(_) => {
+                return Err(unprocessable(format!(
+                    "question {qid}: choice label {i} is a {}; a label is rendered as option \
+                     text and used as the answer key, so it must be a scalar (a string, number \
+                     or bool), got {}",
+                    label.py_type_name(),
+                    label.py_repr()
+                )));
+            }
+            Json::Null => {
+                return Err(unprocessable(format!(
+                    "question {qid}: choice label {i} is null; a label is rendered as option \
+                     text and used as the answer key, so it must be a string, number or bool \
+                     -- a null label renders as the text \"None\" while its answer key is \
+                     \"null\""
+                )));
+            }
+            _ => {}
+        }
+    }
+    // Only the list form can repeat a key: an object's keys are unique.
+    if matches!(crit, Some(Json::Array(_))) {
+        let mut seen: HashMap<HashKey, usize> = HashMap::new();
+        for (i, label) in labels.iter().enumerate() {
+            let key = label.hash_key().expect("scalars are hashable");
+            if key == HashKey::Unique {
+                continue;
+            }
+            if let Some(first) = seen.get(&key) {
+                return Err(unprocessable(format!(
+                    "question {qid}: choice label {i} ({}) repeats label {first}; the labels are \
+                     the answer keys, so every option needs its own (1, 1.0 and True are one key)",
+                    label.py_repr()
+                )));
+            }
+            seen.insert(key, i);
+        }
+    }
+    Ok(())
+}
+
+const DEFAULT_NOUL_FALSE: &str = "no, the statement does not hold";
+const DEFAULT_NOUL_TRUE: &str = "yes, the statement holds";
+
+/// The words a noul question shows the model for its two outcomes.
+fn resolve_noul_labels(labels: &Json) -> Result<(String, String), &'static str> {
+    const BAD: &str =
+        "noul labels must map exactly 'false' and 'true' to distinct non-empty strings";
+    let Json::Object(m) = labels else {
+        return if *labels == Json::Null {
+            Ok(("false".into(), "true".into()))
+        } else {
+            Err(BAD)
+        };
+    };
+    let mut keys: Vec<&str> = m.iter().map(|(k, _)| k.as_str()).collect();
+    keys.sort_unstable();
+    if keys != ["false", "true"] {
+        return Err(BAD);
+    }
+    let (Some(Json::String(f)), Some(Json::String(t))) = (labels.get("false"), labels.get("true"))
+    else {
+        return Err(BAD);
+    };
+    let (f, t) = (py_strip(f), py_strip(t));
+    if f.is_empty() || t.is_empty() || f == t {
+        return Err(BAD);
+    }
+    Ok((f.to_string(), t.to_string()))
+}
+
+/// Python's `str.strip()` with no argument: Unicode whitespace.
+fn py_strip(s: &str) -> &str {
+    s.trim_matches(|c: char| c.is_whitespace() || ('\u{1c}'..='\u{1f}').contains(&c))
+}
+
+/// `render_criterion`: a string as itself, anything else as JSON.
+fn render_criterion(v: &Json) -> String {
+    match v {
+        Json::String(s) => s.clone(),
+        other => other.dumps(),
+    }
+}
+
+/// Only `None` and `""` mean "no description"; `0` and `false` are values.
+fn is_blank(v: &Json) -> bool {
+    matches!(v, Json::Null) || matches!(v, Json::String(s) if s.is_empty())
+}
+
+fn to_internal(id: &str, def: &Json) -> Question {
+    let kind = def
+        .get("type")
+        .and_then(Json::as_str)
+        .and_then(QType::parse)
+        .expect("validated");
+    let instructions = match def.get("instructions").expect("validated") {
+        Json::String(s) => s.clone(),
+        other => other.dumps(),
+    };
+    let crit = def.get("criteria");
+    let (options, labels) = match kind {
+        QType::Choice => {
+            let pairs: Vec<(Json, Json)> = match crit {
+                Some(Json::Object(m)) => m
+                    .iter()
+                    .map(|(k, v)| (Json::String(k.clone()), v.clone()))
+                    .collect(),
+                Some(Json::Array(a)) => a.iter().map(|l| (l.clone(), Json::Null)).collect(),
+                _ => unreachable!("validated"),
+            };
+            let options = pairs
+                .iter()
+                .map(|(k, v)| {
+                    if is_blank(v) {
+                        k.py_str()
+                    } else {
+                        format!("{}: {}", k.py_str(), render_criterion(v))
+                    }
+                })
+                .collect();
+            (options, pairs.into_iter().map(|(k, _)| k).collect())
+        }
+        QType::Score => {
+            let Some(Json::Array(levels)) = crit else {
+                unreachable!("validated")
+            };
+            let options = levels
+                .iter()
+                .enumerate()
+                .map(|(i, c)| format!("level {i}: {}", render_criterion(c)))
+                .collect();
+            (options, levels.clone())
+        }
+        QType::Noul => {
+            // Keys are matched case-insensitively; a repeat after
+            // lower-casing keeps the later value, as a dict would.
+            let mut desc: HashMap<String, Json> = HashMap::new();
+            if let Some(Json::Object(m)) = crit {
+                for (k, v) in m {
+                    desc.insert(k.to_lowercase(), v.clone());
+                }
+            }
+            let (fl, tl) =
+                resolve_noul_labels(def.get("labels").unwrap_or(&Json::Null)).expect("validated");
+            let render = |key: &str, default: &str| match desc.get(key) {
+                Some(v) if !is_blank(v) => render_criterion(v),
+                _ => default.to_string(),
+            };
+            (
+                vec![
+                    format!("{fl}: {}", render("false", DEFAULT_NOUL_FALSE)),
+                    format!("{tl}: {}", render("true", DEFAULT_NOUL_TRUE)),
+                ],
+                Vec::new(),
+            )
+        }
+    };
+    Question {
+        id: id.to_string(),
+        kind,
+        instructions,
+        options,
+        labels,
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn def(s: &str) -> Json {
+        Json::parse(s.as_bytes()).unwrap()
+    }
+
+    fn err(s: &str) -> String {
+        Question::from_definition("q", &def(s)).unwrap_err().detail
+    }
+
+    #[test]
+    fn options_render_as_the_reference_renders_them() {
+        let c = Question::from_definition(
+            "q",
+            &def(r#"{"type": "choice", "instructions": {"a": 1},
+                     "criteria": {"x": "", "y": null, "z": 0, "w": {"d": [1, 2.0]}, "v": "text"}}"#),
+        )
+        .unwrap();
+        assert_eq!(c.instructions, r#"{"a": 1}"#);
+        assert_eq!(
+            c.options,
+            ["x", "y", "z: 0", r#"w: {"d": [1, 2.0]}"#, "v: text"]
+        );
+
+        let l = Question::from_definition(
+            "q",
+            &def(r#"{"type": "choice", "instructions": "i", "criteria": [1, 2.5, false, "s"]}"#),
+        )
+        .unwrap();
+        assert_eq!(l.options, ["1", "2.5", "False", "s"]);
+
+        let s = Question::from_definition(
+            "q",
+            &def(r#"{"type": "score", "instructions": "i", "criteria": ["calm", {"k": false}]}"#),
+        )
+        .unwrap();
+        assert_eq!(s.options, ["level 0: calm", r#"level 1: {"k": false}"#]);
+
+        let n = Question::from_definition(
+            "q",
+            &def(
+                r#"{"type": "noul", "instructions": "i", "criteria": {"TRUE": "held", "false": ""},
+                     "labels": {"false": " no ", "true": "yes"}}"#,
+            ),
+        )
+        .unwrap();
+        assert_eq!(
+            n.options,
+            ["no: no, the statement does not hold", "yes: held"]
+        );
+    }
+
+    #[test]
+    fn invalid_questions_name_the_question_and_the_fix() {
+        assert_eq!(
+            err("[]"),
+            "question 'q': definition must be a dict, got list"
+        );
+        assert_eq!(
+            err(r#"{"type": "pick"}"#),
+            "question 'q': unknown type 'pick'; use one of ['choice', 'noul', 'score']"
+        );
+        assert_eq!(
+            err(r#"{"type": "noul"}"#),
+            "question 'q': no 'instructions'; add the text the model should answer"
+        );
+        assert!(
+            err(r#"{"type": "choice", "instructions": "i", "criteria": []}"#)
+                .ends_with("needs at least one criterion")
+        );
+        assert!(
+            err(r#"{"type": "choice", "instructions": "i", "criteria": [[1]]}"#)
+                .starts_with("question 'q': choice label 0 is a list;")
+        );
+        assert!(
+            err(r#"{"type": "choice", "instructions": "i", "criteria": ["a", null]}"#)
+                .starts_with("question 'q': choice label 1 is null;")
+        );
+        assert!(
+            err(r#"{"type": "choice", "instructions": "i", "criteria": [1, "x", true]}"#)
+                .starts_with("question 'q': choice label 2 (True) repeats label 0;")
+        );
+        assert_eq!(
+            err(r#"{"type": "score", "instructions": "i", "criteria": ["a", null]}"#),
+            "question 'q': score level 1 is null; give every level a description, index 0 first"
+        );
+        assert!(
+            err(r#"{"type": "noul", "instructions": "i", "criteria": {"yes": "x"}}"#)
+                .contains("got ['yes'].")
+        );
+        assert_eq!(
+            err(r#"{"type": "score", "instructions": "i", "criteria": ["a"], "labels": {}}"#),
+            "question 'q': 'labels' is only supported for noul questions"
+        );
+        assert_eq!(
+            err(r#"{"type": "noul", "instructions": "i", "labels": {"false": "x", "true": " x"}}"#),
+            "question 'q': noul labels must map exactly 'false' and 'true' to distinct non-empty strings"
+        );
+    }
+
+    #[test]
+    fn nan_labels_never_collide() {
+        assert!(
+            Question::from_definition(
+                "q",
+                &def(r#"{"type": "choice", "instructions": "i", "criteria": [NaN, NaN]}"#),
+            )
+            .is_ok()
+        );
+    }
+}
@@ -0,0 +1,301 @@
+//! Choosing which checkpoint of a Laya family answers a request (#339).
+//!
+//! A released Laya repo carries three checkpoints: the English one at
+//! its root, `multilingual/` and `typed-decisions/`. The English
+//! checkpoint cannot read other scripts — upstream measured Khmer at
+//! 0.000 accuracy with 0.952 confidence — so an unpinned request is
+//! routed by the language of its state. This follows the reference's
+//! `Router._route` (`laya/router.py`) and its HTTP server's
+//! `_resolve_model` (`laya/serve.py`), including the `reason` strings.
+//!
+//! Precedence, as upstream: a checkpoint the caller names in `model`,
+//! then the detected script and language, then the default (English).
+//! Upstream's opt-in typed-decisions workflow routing and caller
+//! language hints are not exposed over HTTP by the reference, so they
+//! are not here either; the matched workflow is still reported.
+
+use cortex_core::decisions::{DecisionRouting, Json};
+
+use crate::harness::lang_detect;
+
+/// The root checkpoint's routing name.
+pub const ENGLISH: &str = "english";
+pub const MULTILINGUAL: &str = "multilingual";
+pub const TYPED_DECISIONS: &str = "typed-decisions";
+
+/// Where an unpinned request with no usable language signal goes.
+pub const DEFAULT_CHECKPOINT: &str = ENGLISH;
+
+/// Every checkpoint a released family carries, root first.
+pub const FAMILY: [&str; 3] = [ENGLISH, MULTILINGUAL, TYPED_DECISIONS];
+
+/// The reference's `_ALIASES`: names people are likely to type.
+const ALIASES: [(&str, &str); 10] = [
+    ("en", ENGLISH),
+    ("laya", ENGLISH),
+    ("default", ENGLISH),
+    ("multi", MULTILINGUAL),
+    ("ml", MULTILINGUAL),
+    ("laya-multilingual", MULTILINGUAL),
+    ("typed", TYPED_DECISIONS),
+    ("typed_decisions", TYPED_DECISIONS),
+    ("laya-typed-decisions", TYPED_DECISIONS),
+    ("decisions", TYPED_DECISIONS),
+];
+
+/// The reference server's `_PUBLISHED_MODEL_IDS`: the Hugging Face ids
+/// the non-root checkpoints are published under.
+const PUBLISHED: [(&str, &str); 2] = [
+    ("convaiinnovations/laya-multilingual", MULTILINGUAL),
+    ("convaiinnovations/laya-typed-decisions", TYPED_DECISIONS),
+];
+
+/// Question-id signatures of the four typed-decisions workflows.
+const WORKFLOWS: [(&str, [&str; 5]); 4] = [
+    (
+        "agent_trace_observability",
+        ["action", "needs_review", "outcome", "risk", "urgency"],
+    ),
+    (
+        "customer_service",
+        ["action", "category", "churn_risk", "needs_human", "urgency"],
+    ),
+    (
+        "invoice_processing",
+        [
+            "discrepancy_severity",
+            "disposition",
+            "duplicate",
+            "matches_order",
+            "urgency",
+        ],
+    ),
+    (
+        "security_incidents",
+        [
+            "credential_compromise",
+            "disposition",
+            "severity",
+            "true_positive",
+            "urgency",
+        ],
+    ),
+];
+
+/// The checkpoint a request's `model` field pins, if it names one.
+///
+/// Anything else — a Jev service name like `jev-1`, the family's own
+/// repo id, an empty string — pins nothing and the request is routed.
+pub fn resolve_pin(model: Option<&str>) -> Option<&'static str> {
+    let model = model?;
+    if model.is_empty() {
+        return None;
+    }
+    let key = py_strip(model).to_lowercase();
+    if let Some((_, ckpt)) = PUBLISHED.iter().find(|(id, _)| *id == key) {
+        return Some(ckpt);
+    }
+    let key = ALIASES
+        .iter()
+        .find(|(alias, _)| *alias == key)
+        .map(|(_, ckpt)| *ckpt)
+        .unwrap_or(key.as_str());
+    FAMILY.iter().copied().find(|c| *c == key)
+}
+
+/// The typed-decisions workflow whose question ids these are exactly.
+pub fn match_workflow(questions: &[(String, Json)]) -> Option<&'static str> {
+    let ids: std::collections::BTreeSet<&str> = questions.iter().map(|(q, _)| q.as_str()).collect();
+    WORKFLOWS.iter().find_map(|(name, sig)| {
+        let sig: std::collections::BTreeSet<&str> = sig.iter().copied().collect();
+        (ids == sig).then_some(*name)
+    })
+}
+
+/// Decide which checkpoint answers, and say why.
+///
+/// `model_id` is the served family's repo id, used for the `repo` field.
+/// The result names a checkpoint whether or not this node has it
+/// loaded; the caller decides what to do about one it lacks.
+pub fn route(
+    model_id: &str,
+    state: &Json,
+    questions: &[(String, Json)],
+    pin: Option<&'static str>,
+) -> DecisionRouting {
+    if let Some(key) = pin {
+        return DecisionRouting {
+            model: key.to_string(),
+            repo: repo_of(model_id, key),
+            reason: format!("explicit model={}", py_repr(key)),
+            detection: None,
+            workflow: None,
+        };
+    }
+    let workflow = match_workflow(questions).map(str::to_string);
+    let det = lang_detect::analyse(state);
+    let (key, reason) = if det.script == "unknown" {
+        (
+            DEFAULT_CHECKPOINT,
+            format!("no letters detected in state; using default ({DEFAULT_CHECKPOINT})"),
+        )
+    } else if det.script != "latin" {
+        (
+            MULTILINGUAL,
+            format!(
+                "non-Latin script ({}, {:.0}% of letters); the English checkpoint cannot read it",
+                det.script,
+                100.0 * det.non_latin_fraction
+            ),
+        )
+    } else if !det.is_english {
+        let reason = if let Some(segment) = &det.mixed_segment {
+            format!(
+                "Latin script, mostly English, but a line or field reads as {} ({}); \
+                 the English checkpoint cannot read it",
+                det.language.map(py_repr).unwrap_or_else(|| "None".into()),
+                py_repr(&segment.chars().take(60).collect::<String>()),
+            )
+        } else if let Some(lang) = det.language {
+            format!(
+                "Latin script but language looks like {}, not English",
+                py_repr(lang)
+            )
+        } else {
+            format!(
+                "Latin script, language not identified but {:.0}% non-English letters; \
+                 not safe for the English checkpoint",
+                100.0 * det.diacritic_rate
+            )
+        };
+        (MULTILINGUAL, reason)
+    } else if det.language_undecided {
+        (
+            DEFAULT_CHECKPOINT,
+            format!(
+                "Latin script, language not identified and no non-English letters; \
+                 using default ({DEFAULT_CHECKPOINT})"
+            ),
+        )
+    } else {
+        (ENGLISH, "English Latin text".to_string())
+    };
+    DecisionRouting {
+        model: key.to_string(),
+        repo: repo_of(model_id, key),
+        reason,
+        detection: Some(analysis_json(&det)),
+        workflow,
+    }
+}
+
+/// The language per-language calibration keys on: the detected one.
+pub fn detected_language(routing: &DecisionRouting) -> Option<&str> {
+    routing
+        .detection
+        .as_ref()
+        .and_then(|d| d.get("language"))
+        .and_then(Json::as_str)
+}
+
+/// `<repo>` for the root checkpoint, `<repo>/<subfolder>` otherwise.
+pub fn repo_of(model_id: &str, checkpoint: &str) -> String {
+    if checkpoint == ENGLISH {
+        model_id.to_string()
+    } else {
+        format!("{model_id}/{checkpoint}")
+    }
+}
+
+/// The analysis as an order-preserving [`Json`], so the response keeps
+/// the reference's field order.
+fn analysis_json(det: &lang_detect::Analysis) -> Json {
+    let text = serde_json::to_string(det).unwrap_or_else(|_| "null".into());
+    Json::parse(text.as_bytes()).unwrap_or(Json::Null)
+}
+
+fn py_repr(s: &str) -> String {
+    Json::String(s.to_string()).py_repr()
+}
+
+/// Python's `str.strip()`: Unicode whitespace, which includes the
+/// U+001C–U+001F separators Rust's `trim` leaves alone.
+fn py_strip(s: &str) -> &str {
+    s.trim_matches(|c: char| c.is_whitespace() || ('\u{1c}'..='\u{1f}').contains(&c))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn pins_follow_the_reference_server() {
+        assert_eq!(resolve_pin(Some("multilingual")), Some(MULTILINGUAL));
+        assert_eq!(resolve_pin(Some(" ML ")), Some(MULTILINGUAL));
+        assert_eq!(
+            resolve_pin(Some("convaiinnovations/laya-typed-decisions")),
+            Some(TYPED_DECISIONS)
+        );
+        assert_eq!(resolve_pin(Some("laya")), Some(ENGLISH));
+        // A Jev service name, the family's own id, and nothing: no pin.
+        assert_eq!(resolve_pin(Some("jev-1")), None);
+        assert_eq!(resolve_pin(Some("convaiinnovations/laya")), None);
+        assert_eq!(resolve_pin(Some("")), None);
+        assert_eq!(resolve_pin(None), None);
+    }
+
+    #[test]
+    fn workflow_needs_the_exact_id_set() {
+        let qs = |ids: &[&str]| -> Vec<(String, Json)> {
+            ids.iter().map(|i| (i.to_string(), Json::Null)).collect()
+        };
+        assert_eq!(
+            match_workflow(&qs(&[
+                "urgency",
+                "action",
+                "category",
+                "churn_risk",
+                "needs_human"
+            ])),
+            Some("customer_service")
+        );
+        assert_eq!(match_workflow(&qs(&["urgency"])), None);
+    }
+
+    /// Every case in the reference fixtures routes to the checkpoint, with
+    /// the reason and detection, the reference reported.
+    #[test]
+    fn routes_every_reference_case_like_the_reference() {
+        let text = std::fs::read_to_string(
+            std::path::Path::new(env!("CARGO_MANIFEST_DIR"))
+                .join("src/harness/testdata/laya/reference.json"),
+        )
+        .expect("read reference.json");
+        let reference = Json::parse(text.as_bytes()).expect("parse reference.json");
+        let Some(Json::Array(cases)) = reference.get("cases") else {
+            panic!("no cases");
+        };
+        for case in cases {
+            let name = case.get("name").and_then(Json::as_str).unwrap();
+            let request = case.get("request").unwrap();
+            let body = serde_json::to_vec(request).unwrap();
+            let req = cortex_core::decisions::SystemOneRequest::parse(
+                &body,
+                cortex_core::decisions::limits::DEFAULT_MAX_TOKEN_BUDGET,
+            )
+            .unwrap();
+            let routing = route(
+                "convaiinnovations/laya",
+                &req.state,
+                &req.questions,
+                resolve_pin(req.model.as_deref()),
+            );
+            let want = case.get("response").unwrap().get("routing").unwrap();
+            assert_eq!(
+                serde_json::to_string(&routing).unwrap(),
+                serde_json::to_string(want).unwrap(),
+                "{name}"
+            );
+        }
+    }
+}
@@ -0,0 +1,316 @@
+//! Sequence assembly: one token sequence per question, under the model's
+//! token budgets.
+//!
+//! ```text
+//! [CLS] "<type> question: <instructions>" [SEP] [MASK] opt0 [MASK] opt1 … [SEP] state [SEP]
+//! ```
+//!
+//! The budget rules, in the order they apply:
+//!
+//! 1. Each option is `[MASK]` plus at most 48 tokens of its text.
+//! 2. `head_max_len` bounds the instruction text and all options together.
+//!    If fewer than 16 tokens would remain for the instructions, every
+//!    option is cut to the same `max(4, (head_max_len - 16) / options)`
+//!    tokens (marker included).
+//! 3. The instructions get what is left of `head_max_len`, but never fewer
+//!    than 8 tokens.
+//! 4. The state fills the rest of `max_len`. A conversation (a JSON list)
+//!    keeps its newest turns, so it is cut from the left; anything else is
+//!    cut from the right.
+//! 5. The whole sequence is cut to `max_len`, dropping any marker that no
+//!    longer fits — a question whose markers do not all fit is refused.
+
+use super::question::{QType, Question, questions_from_request};
+use super::tokenizer::TextEncoder;
+use super::{DecisionConfig, DecisionRequestError, unprocessable};
+use cortex_core::decisions::Json;
+
+/// Option text tokens kept before the head budget is considered.
+pub const MAX_OPTION_TOKENS: usize = 48;
+/// Head tokens the instructions are guaranteed before options are shrunk.
+const MIN_INSTRUCTION_ROOM: usize = 16;
+/// Per-option floor (marker included) when options are shrunk.
+const MIN_TOKENS_PER_OPTION: usize = 4;
+/// Instruction tokens kept however many the options take.
+const MIN_INSTRUCTION_TOKENS: usize = 8;
+
+/// Per-request overrides of the checkpoint's token budgets.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Budget {
+    pub max_len: Option<usize>,
+    pub head_max_len: Option<usize>,
+}
+
+/// One model input row: one question over the state.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct Row {
+    pub input_ids: Vec<u32>,
+    /// Position of each option's `[MASK]` marker, in option order.
+    pub markers: Vec<usize>,
+    pub qtype: QType,
+}
+
+/// What the head budget did to a question's options.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct OptionStats {
+    pub options: usize,
+    /// Options whose token span is still unique after the cut. Fewer than
+    /// `options` means the model can no longer tell some of them apart.
+    pub options_distinct: usize,
+    /// The per-option cap applied, when the options had to be shrunk.
+    pub tokens_per_option: Option<usize>,
+}
+
+/// A request turned into model input: one [`Row`] per question, in request
+/// order. With no questions there are no rows and nothing to run.
+#[derive(Debug, Clone, PartialEq)]
+pub struct Prepared {
+    pub questions: Vec<Question>,
+    pub rows: Vec<Row>,
+    pub stats: Vec<OptionStats>,
+    /// Every token the forward pass encodes: the metered input.
+    pub input_tokens: usize,
+    /// The id batched rows are padded with.
+    pub pad_id: u32,
+}
+
+/// The text a state is tokenized from: a string as-is, anything else as
+/// `json.dumps(state, ensure_ascii=False)`.
+pub fn serialize_state(state: &Json) -> String {
+    match state {
+        Json::String(s) => s.clone(),
+        other => other.dumps(),
+    }
+}
+
+/// Validate a request's questions and build their model input.
+///
+/// `questions` are the request's definitions in request order; `cfg` and
+/// `tok` belong to the checkpoint the request was routed to.
+pub fn prepare(
+    state: &Json,
+    questions: &[(String, Json)],
+    cfg: &DecisionConfig,
+    tok: &dyn TextEncoder,
+    budget: Budget,
+) -> Result<Prepared, DecisionRequestError> {
+    let special = tok.special();
+    if questions.is_empty() {
+        return Ok(Prepared {
+            questions: Vec::new(),
+            rows: Vec::new(),
+            stats: Vec::new(),
+            input_tokens: 0,
+            pad_id: special.pad_id,
+        });
+    }
+    let questions = questions_from_request(questions)?;
+    let max_len = budget.max_len.unwrap_or(cfg.max_len);
+    let head_max_len = budget.head_max_len.unwrap_or(cfg.head_max_len);
+    let encode = |text: &str| {
+        tok.encode(text)
+            .map_err(|e| DecisionRequestError::new(500, format!("tokenizer failed: {e}")))
+    };
+    // Tokenized once and sliced per question.
+    let state_ids = encode(&serialize_state(state).replace(&special.mask_token, " "))?;
+    let truncate_left = matches!(state, Json::Array(_));
+    let frame = Frame {
+        cls: special.cls_id,
+        sep: special.sep_id,
+        max_len,
+        head_max_len,
+    };
+
+    let mut rows = Vec::with_capacity(questions.len());
+    let mut stats = Vec::with_capacity(questions.len());
+    for question in &questions {
+        let instructions = question.instructions.replace(&special.mask_token, " ");
+        let head_ids = encode(&format!(
+            "{} question: {instructions}",
+            question.kind.name()
+        ))?;
+        let mut option_ids = Vec::with_capacity(question.options.len());
+        for option in &question.options {
+            let mut ids = vec![special.mask_id];
+            let text = encode(&format!(" {}", option.replace(&special.mask_token, " ")))?;
+            ids.extend(text.into_iter().take(MAX_OPTION_TOKENS));
+            option_ids.push(ids);
+        }
+        let (row, s) = frame.assemble(head_ids, option_ids, &state_ids, truncate_left);
+        if row.markers.len() != question.options.len() {
+            return Err(unprocessable(format!(
+                "question {} options exceed head_max_len={head_max_len}",
+                Json::String(question.id.clone()).py_repr()
+            )));
+        }
+        rows.push(Row {
+            qtype: question.kind,
+            ..row
+        });
+        stats.push(s);
+    }
+    let input_tokens = rows.iter().map(|r| r.input_ids.len()).sum();
+    Ok(Prepared {
+        questions,
+        rows,
+        stats,
+        input_tokens,
+        pad_id: special.pad_id,
+    })
+}
+
+/// The fixed parts of every sequence for one request.
+struct Frame {
+    cls: u32,
+    sep: u32,
+    max_len: usize,
+    head_max_len: usize,
+}
+
+impl Frame {
+    /// The pure budget arithmetic of one sequence, over already-tokenized parts.
+    fn assemble(
+        &self,
+        mut head_ids: Vec<u32>,
+        mut option_ids: Vec<Vec<u32>>,
+        state_ids: &[u32],
+        truncate_left: bool,
+    ) -> (Row, OptionStats) {
+        let Frame {
+            cls,
+            sep,
+            max_len,
+            head_max_len,
+        } = *self;
+        let used = |o: &[Vec<u32>]| o.iter().map(Vec::len).sum::<usize>();
+        // Signed: options can overrun the head budget.
+        let mut opt_budget = head_max_len as isize - used(&option_ids) as isize;
+        let mut tokens_per_option = None;
+        if opt_budget < MIN_INSTRUCTION_ROOM as isize {
+            let per = MIN_TOKENS_PER_OPTION
+                .max(head_max_len.saturating_sub(MIN_INSTRUCTION_ROOM) / option_ids.len().max(1));
+            tokens_per_option = Some(per);
+            for o in &mut option_ids {
+                o.truncate(per);
+            }
+            opt_budget = head_max_len as isize - used(&option_ids) as isize;
+        }
+        head_ids.truncate(opt_budget.max(MIN_INSTRUCTION_TOKENS as isize) as usize);
+
+        let mut ids = Vec::with_capacity(max_len);
+        ids.push(cls);
+        ids.extend(head_ids);
+        ids.push(sep);
+        let mut markers = Vec::with_capacity(option_ids.len());
+        for o in &option_ids {
+            markers.push(ids.len());
+            ids.extend_from_slice(o);
+        }
+        ids.push(sep);
+        let room = max_len.saturating_sub(ids.len() + 1);
+        let state = if truncate_left {
+            &state_ids[state_ids.len().saturating_sub(room)..]
+        } else {
+            &state_ids[..room.min(state_ids.len())]
+        };
+        ids.extend_from_slice(state);
+        ids.push(sep);
+        ids.truncate(max_len);
+        markers.retain(|&m| m < max_len);
+
+        let mut distinct = option_ids.clone();
+        distinct.sort();
+        distinct.dedup();
+        (
+            Row {
+                input_ids: ids,
+                markers,
+                qtype: QType::Choice,
+            },
+            OptionStats {
+                options: option_ids.len(),
+                options_distinct: distinct.len(),
+                tokens_per_option,
+            },
+        )
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    const CLS: u32 = 1;
+    const SEP: u32 = 2;
+    const M: u32 = 9;
+
+    fn frame(max_len: usize, head_max_len: usize) -> Frame {
+        Frame {
+            cls: CLS,
+            sep: SEP,
+            max_len,
+            head_max_len,
+        }
+    }
+
+    fn opts(lens: &[usize]) -> Vec<Vec<u32>> {
+        lens.iter()
+            .enumerate()
+            .map(|(i, &n)| {
+                let mut o = vec![M];
+                o.extend((0..n - 1).map(|j| 100 + (i * 50 + j) as u32));
+                o
+            })
+            .collect()
+    }
+
+    #[test]
+    fn roomy_budget_keeps_everything() {
+        let (row, s) = frame(64, 32).assemble(vec![10, 11], opts(&[2, 3]), &[50, 51], false);
+        assert_eq!(
+            row.input_ids,
+            [CLS, 10, 11, SEP, M, 100, M, 150, 151, SEP, 50, 51, SEP]
+        );
+        assert_eq!(row.markers, [4, 6]);
+        assert_eq!(
+            (s.options, s.options_distinct, s.tokens_per_option),
+            (2, 2, None)
+        );
+    }
+
+    #[test]
+    fn crowded_head_shrinks_options_evenly_and_keeps_eight_instruction_tokens() {
+        // 3 options of 10 = 30 tokens against a 40-token head: 10 left < 16.
+        let head: Vec<u32> = (10..40).collect();
+        let (row, s) = frame(128, 40).assemble(head, opts(&[10, 10, 10]), &[], false);
+        // per = max(4, (40 - 16) / 3) = 8; 40 - 24 = 16 instruction tokens.
+        assert_eq!(s.tokens_per_option, Some(8));
+        assert_eq!(row.markers, [18, 26, 34]);
+        let (row, s) = frame(256, 40).assemble((10..40).collect(), opts(&[10; 12]), &[], false);
+        // per = max(4, 24 / 12 = 2) = 4; the budget goes negative, 8 tokens survive.
+        assert_eq!(s.tokens_per_option, Some(4));
+        assert_eq!(row.markers[0], 1 + 8 + 1);
+    }
+
+    #[test]
+    fn state_is_cut_from_the_side_the_state_shape_asks_for() {
+        let state: Vec<u32> = (50..60).collect();
+        let (right, _) = frame(8, 32).assemble(vec![], opts(&[1]), &state, false);
+        let (left, _) = frame(8, 32).assemble(vec![], opts(&[1]), &state, true);
+        // [CLS] [SEP] [MASK] [SEP] = 4 tokens, 1 reserved for the last [SEP]: room 3.
+        assert_eq!(right.input_ids, [CLS, SEP, M, SEP, 50, 51, 52, SEP]);
+        assert_eq!(left.input_ids, [CLS, SEP, M, SEP, 57, 58, 59, SEP]);
+        // No room at all: none of the state, not all of it.
+        let (none, _) = frame(5, 32).assemble(vec![], opts(&[1]), &state, true);
+        assert_eq!(none.input_ids, [CLS, SEP, M, SEP, SEP]);
+    }
+
+    #[test]
+    fn markers_past_max_len_are_dropped_and_collisions_counted() {
+        let same = vec![vec![M, 7, 7], vec![M, 7, 7]];
+        let (row, s) = frame(5, 64).assemble(vec![], same, &[], false);
+        // [CLS] [SEP] then markers at 2 and 5; only the first fits in 5.
+        assert_eq!(row.markers, [2]);
+        assert_eq!((s.options, s.options_distinct), (2, 1));
+    }
+}
@@ -0,0 +1,318 @@
+//! Per-option logits → calibrated, typed answers.
+//!
+//! The reference computes this in numpy on the float32 logits the model
+//! returns, and publishes each probability rounded to 4 decimal places.
+//! Reproducing the rounded values exactly means reproducing the float32
+//! arithmetic, in numpy's order: division by a float32 temperature, a
+//! max-subtracted `exp`, and numpy's *pairwise* summation (not a running
+//! sum) for every reduction. The expected score is the exception: numpy
+//! promotes `arange(k) * p` to float64.
+
+use super::question::QType;
+use super::sequence::Prepared;
+use super::{DECISION_MODEL_NAME, DecisionConfig};
+use cortex_core::decisions::{
+    CollapsedOptions, DecisionAction, DecisionAnswer, DecisionUsage, OrderedMap, SystemOneResponse,
+};
+
+/// The model's output for one [`super::Row`].
+#[derive(Debug, Clone, PartialEq)]
+pub struct RowOutput {
+    /// The scorer's raw logit at each option marker, before calibration.
+    /// Entries past the row's marker count (batch padding) are ignored.
+    pub logits: Vec<f32>,
+    /// The act head's probability of acting: `softmax(act_logits)[0]`.
+    pub act_probability: f32,
+}
+
+/// Build the answers and usage for a prepared request from the model's
+/// per-row outputs (`outputs[i]` belongs to `prepared.rows[i]`).
+///
+/// `lang` selects per-language calibration where the checkpoint has it:
+/// the language the request was declared or detected as.
+pub fn shape(
+    prepared: &Prepared,
+    outputs: &[RowOutput],
+    cfg: &DecisionConfig,
+    lang: Option<&str>,
+) -> (OrderedMap<DecisionAnswer>, DecisionUsage) {
+    assert_eq!(
+        outputs.len(),
+        prepared.rows.len(),
+        "one model output per prepared row"
+    );
+    let temps = cfg.temperatures_for(lang);
+    let mut answers = Vec::with_capacity(outputs.len());
+    let mut collapsed = Vec::new();
+    for ((question, row), (out, stats)) in prepared
+        .questions
+        .iter()
+        .zip(&prepared.rows)
+        .zip(outputs.iter().zip(&prepared.stats))
+    {
+        let k = row.markers.len();
+        let p = calibrate(
+            &out.logits[..k],
+            temps.for_question(question.kind as usize, k),
+        );
+        let answer_confidence = round4(
+            p.iter()
+                .copied()
+                .fold(f32::NEG_INFINITY, f32::max)
+                .clamp(0.0, 1.0) as f64,
+        );
+        let action = DecisionAction {
+            act_probability: round4(out.act_probability as f64),
+        };
+        let probabilities = |key: &dyn Fn(usize) -> String| {
+            OrderedMap(
+                p.iter()
+                    .enumerate()
+                    .map(|(i, &v)| (key(i), round4(v as f64)))
+                    .collect(),
+            )
+        };
+        let answer = match question.kind {
+            QType::Choice => DecisionAnswer::Choice {
+                choice: question.labels[argmax(&p)].clone(),
+                probabilities: probabilities(&|i| {
+                    question.labels[i]
+                        .json_key()
+                        .expect("choice labels are scalars")
+                }),
+                confidence: round4(entropy_confidence(&p) as f64),
+                answer_confidence,
+                action,
+            },
+            QType::Score => {
+                // int64 * float32 promotes to float64 in numpy.
+                let expected: Vec<f64> = p
+                    .iter()
+                    .enumerate()
+                    .map(|(i, &v)| i as f64 * v as f64)
+                    .collect();
+                DecisionAnswer::Score {
+                    score: round4(pairwise_sum(&expected)),
+                    legend: OrderedMap(
+                        question
+                            .labels
+                            .iter()
+                            .enumerate()
+                            .map(|(i, c)| (i.to_string(), c.clone()))
+                            .collect(),
+                    ),
+                    probabilities: probabilities(&|i| i.to_string()),
+                    confidence: round4(entropy_confidence(&p) as f64),
+                    answer_confidence,
+                    action,
+                }
+            }
+            QType::Noul => {
+                let yes = p[1] as f64;
+                DecisionAnswer::Noul {
+                    noul: round4(yes),
+                    confidence: round4(yes.max(1.0 - yes)),
+                    answer_confidence,
+                    action,
+                }
+            }
+        };
+        answers.push((question.id.clone(), answer));
+        if stats.options_distinct < stats.options {
+            collapsed.push((
+                question.id.clone(),
+                CollapsedOptions {
+                    total: stats.options,
+                    distinct: stats.options_distinct,
+                    tokens_per_option: stats.tokens_per_option,
+                },
+            ));
+        }
+    }
+    let usage = DecisionUsage {
+        input_tokens: prepared.input_tokens,
+        output_tokens: 0,
+        options: (!collapsed.is_empty()).then_some(OrderedMap(collapsed)),
+    };
+    (OrderedMap(answers), usage)
+}
+
+/// Assemble the full response once routing is known.
+pub fn response(
+    answers: OrderedMap<DecisionAnswer>,
+    usage: DecisionUsage,
+    routing: Option<cortex_core::decisions::DecisionRouting>,
+) -> SystemOneResponse {
+    SystemOneResponse {
+        model: DECISION_MODEL_NAME.to_string(),
+        answers,
+        usage,
+        routing,
+    }
+}
+
+/// Temperature-scaled softmax, as the reference evaluates it in numpy:
+/// `z = logits / t`, `e = exp(z - z.max())`, `p = e / e.sum()`.
+///
+/// numpy's SIMD `expf` and libm's disagree by one ulp on about a third of
+/// inputs. That is invisible after the 4-decimal rounding on every
+/// reference fixture, and far below the engine's own distance from the
+/// reference's logits, so it is not reproduced; the steps either side of
+/// the `exp` are.
+fn calibrate(logits: &[f32], t: f64) -> Vec<f32> {
+    let z = scale(logits, t);
+    let max = z.iter().copied().fold(f32::NEG_INFINITY, f32::max);
+    let e: Vec<f32> = z.iter().map(|&v| (v - max).exp()).collect();
+    normalize(&e)
+}
+
+/// `logits / t` in float32: numpy casts the Python-float temperature to the
+/// array's dtype rather than promoting the array.
+fn scale(logits: &[f32], t: f64) -> Vec<f32> {
+    let t = t as f32;
+    logits.iter().map(|&l| l / t).collect()
+}
+
+/// `e / e.sum()` with numpy's pairwise float32 sum.
+fn normalize(e: &[f32]) -> Vec<f32> {
+    let sum = pairwise_sum(e);
+    e.iter().map(|&v| v / sum).collect()
+}
+
+/// `1 - H(p) / log(k)`, clipped to [0, 1]: how concentrated the whole
+/// distribution is. Not calibrated; see `answer_confidence` for that.
+fn entropy_confidence(p: &[f32]) -> f32 {
+    let k = p.len();
+    if k < 2 {
+        return 1.0;
+    }
+    let terms: Vec<f32> = p.iter().map(|&v| v * v.clamp(1e-12, 1.0).ln()).collect();
+    let ent = -pairwise_sum(&terms);
+    (1.0 - ent / (k as f64).ln() as f32).clamp(0.0, 1.0)
+}
+
+/// The first index of the largest value, as `argmax` picks it.
+fn argmax(p: &[f32]) -> usize {
+    let mut best = 0;
+    for (i, &v) in p.iter().enumerate() {
+        if v > p[best] {
+            best = i;
+        }
+    }
+    best
+}
+
+/// Python's `round(x, 4)`: correctly rounded, ties to even on the exact
+/// binary value. Rust's fixed-precision formatting rounds the same way.
+fn round4(x: f64) -> f64 {
+    format!("{x:.4}").parse().expect("formatted float parses")
+}
+
+/// numpy's pairwise summation over a contiguous array, which the rounded
+/// outputs depend on: a plain loop below 8 elements, eight interleaved
+/// accumulators up to 128, and recursive halving (at a multiple of 8)
+/// beyond.
+fn pairwise_sum<T>(a: &[T]) -> T
+where
+    T: Copy + std::ops::Add<Output = T> + Default,
+{
+    const BLOCK: usize = 128;
+    let n = a.len();
+    if n < 8 {
+        let mut res = T::default();
+        for &v in a {
+            res = res + v;
+        }
+        res
+    } else if n <= BLOCK {
+        let mut r = [a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7]];
+        let mut i = 8;
+        while i < n - n % 8 {
+            for j in 0..8 {
+                r[j] = r[j] + a[i + j];
+            }
+            i += 8;
+        }
+        let mut res = ((r[0] + r[1]) + (r[2] + r[3])) + ((r[4] + r[5]) + (r[6] + r[7]));
+        while i < n {
+            res = res + a[i];
+            i += 1;
+        }
+        res
+    } else {
+        let mut n2 = n / 2;
+        n2 -= n2 % 8;
+        pairwise_sum(&a[..n2]) + pairwise_sum(&a[n2..])
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn pairwise_sum_differs_from_a_running_sum_where_numpy_does() {
+        // 1e8 swamps every 1.0 added to it one at a time in f32; pairwise
+        // grouping keeps them. `int(np.float32 array.sum())` of this array is 100000008.
+        let mut a = vec![1.0f32; 16];
+        a[0] = 1e8;
+        let running = a.iter().fold(0.0f32, |s, &v| s + v);
+        assert_eq!(running, 1e8);
+        assert_eq!(pairwise_sum(&a), 100_000_008.0);
+    }
+
+    /// Rows of `testdata/laya/numpy_softmax.txt`: the reference's
+    /// calibration run in numpy on random float32 logits, with the bits of
+    /// every intermediate. Bit equality pins the evaluation order — a
+    /// running sum instead of numpy's pairwise one, or dividing by the
+    /// temperature in float64, changes low bits that the 4-decimal rounding
+    /// usually hides.
+    fn numpy_rows() -> Vec<(f64, [Vec<f32>; 4])> {
+        include_str!("../testdata/laya/numpy_softmax.txt")
+            .lines()
+            .filter(|l| !l.starts_with('#') && !l.trim().is_empty())
+            .map(|l| {
+                let mut f = l.split('|');
+                let t: f64 = f.next().unwrap().parse().unwrap();
+                let bits = |s: &str| -> Vec<f32> {
+                    s.split(',')
+                        .map(|h| f32::from_bits(u32::from_str_radix(&h[2..], 16).unwrap()))
+                        .collect()
+                };
+                let v: Vec<Vec<f32>> = f.map(bits).collect();
+                (t, [v[0].clone(), v[1].clone(), v[2].clone(), v[3].clone()])
+            })
+            .collect()
+    }
+
+    fn bits(v: &[f32]) -> Vec<u32> {
+        v.iter().map(|x| x.to_bits()).collect()
+    }
+
+    #[test]
+    fn calibration_matches_numpy_either_side_of_exp() {
+        let rows = numpy_rows();
+        assert_eq!(rows.len(), 4);
+        for (t, [logits, z, e, p]) in rows {
+            assert_eq!(
+                bits(&scale(&logits, t)),
+                bits(&z),
+                "k={} t={t}: z",
+                logits.len()
+            );
+            assert_eq!(
+                bits(&normalize(&e)),
+                bits(&p),
+                "k={} t={t}: p",
+                logits.len()
+            );
+        }
+    }
+
+    #[test]
+    fn round4_ties_to_even_on_the_binary_value() {
+        assert_eq!(round4(0.03125), 0.0312);
+        assert_eq!(round4(1.00005), 1.0001); // 1.00005 is just above the tie in binary
+        assert_eq!(round4(0.99995), 1.0);
+    }
+}
@@ -0,0 +1,81 @@
+//! The tokenizer a decision checkpoint ships, and its special tokens.
+
+use std::path::Path;
+
+/// The special tokens the sequence layout uses, resolved to ids.
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct SpecialTokens {
+    /// The option marker's text (`[MASK]`, `<mask>` for mmBERT). Literal
+    /// occurrences in caller text are replaced with a space before
+    /// tokenizing, so no caller can forge a marker.
+    pub mask_token: String,
+    pub cls_id: u32,
+    pub sep_id: u32,
+    pub mask_id: u32,
+    /// Padding for batched rows; not part of any sequence.
+    pub pad_id: u32,
+}
+
+/// Text → token ids, with no special tokens added.
+pub trait TextEncoder {
+    fn encode(&self, text: &str) -> Result<Vec<u32>, String>;
+    fn special(&self) -> &SpecialTokens;
+}
+
+/// A checkpoint's `tokenizer/` directory: `tokenizer.json` plus the special
+/// token names in `tokenizer_config.json`.
+pub struct DecisionTokenizer {
+    tok: tokenizers::Tokenizer,
+    special: SpecialTokens,
+}
+
+impl DecisionTokenizer {
+    pub fn from_dir(dir: &Path) -> anyhow::Result<Self> {
+        let mut tok = tokenizers::Tokenizer::from_file(dir.join("tokenizer.json"))
+            .map_err(|e| anyhow::anyhow!("{}: {e}", dir.join("tokenizer.json").display()))?;
+        // The Python tokenizer call the reference makes pads and truncates
+        // nothing unless asked; a tokenizer.json that carries either setting
+        // would otherwise apply it here.
+        tok.with_truncation(None)
+            .map_err(|e| anyhow::anyhow!("disabling truncation: {e}"))?;
+        tok.with_padding(None);
+        let cfg_path = dir.join("tokenizer_config.json");
+        let cfg: serde_json::Value = serde_json::from_str(&std::fs::read_to_string(&cfg_path)?)
+            .map_err(|e| anyhow::anyhow!("{}: {e}", cfg_path.display()))?;
+        let name = |key: &str| -> anyhow::Result<String> {
+            cfg.get(key)
+                .and_then(|v| {
+                    v.as_str()
+                        .or_else(|| v.get("content").and_then(|c| c.as_str()))
+                })
+                .map(str::to_owned)
+                .ok_or_else(|| anyhow::anyhow!("{}: no {key}", cfg_path.display()))
+        };
+        let id = |token: &str| -> anyhow::Result<u32> {
+            tok.token_to_id(token)
+                .ok_or_else(|| anyhow::anyhow!("special token {token:?} is not in the vocabulary"))
+        };
+        let mask_token = name("mask_token")?;
+        let special = SpecialTokens {
+            cls_id: id(&name("cls_token")?)?,
+            sep_id: id(&name("sep_token")?)?,
+            mask_id: id(&mask_token)?,
+            pad_id: id(&name("pad_token")?)?,
+            mask_token,
+        };
+        Ok(Self { tok, special })
+    }
+}
+
+impl TextEncoder for DecisionTokenizer {
+    fn encode(&self, text: &str) -> Result<Vec<u32>, String> {
+        self.tok
+            .encode(text, false)
+            .map(|e| e.get_ids().to_vec())
+            .map_err(|e| e.to_string())
+    }
+
+    fn special(&self) -> &SpecialTokens {
+        &self.special
+    }
+}
@@ -0,0 +1,252 @@
+//! A loaded Laya decision model, as the rest of neuron sees it (#336).
+//!
+//! The weights live in a device worker's decision slab (CUDA) or in an
+//! `Arc` scored on the blocking pool (CPU); either way the caller holds
+//! only this struct and asks it to [`DecisionCheckpoint::decide`] a
+//! batch of already-assembled questions. Turning a `/v1/systemone`
+//! request into that batch — rendering options, budgets, tokenisation —
+//! and turning the scores back into calibrated answers happen outside
+//! this module.
+//!
+//! One Hugging Face repo can carry several checkpoints (the released
+//! `convaiinnovations/laya` has the English one at its root and
+//! `multilingual/` and `typed-decisions/` beside it), so a loaded model
+//! is a list of checkpoints, each named by its subfolder.
+
+use std::path::PathBuf;
+use std::sync::Arc;
+use std::sync::atomic::AtomicBool;
+
+use anyhow::{Context, Result};
+use candle_core::DType;
+
+use super::arch::laya::{CheckpointConfig, DecisionBatch, DecisionRow, LayaModel};
+use super::decision::{DecisionConfig, DecisionTokenizer};
+use super::device_worker::{DecisionHandle, DeviceWorkerHandle};
+use cortex_core::harness::ModelSpec;
+
+/// Routing name of the checkpoint at the repo root. A released Laya
+/// family's root is its English checkpoint, and upstream routes to it
+/// under that name.
+pub const ROOT_CHECKPOINT: &str = super::decision::route::ENGLISH;
+
+/// Where one checkpoint's weights live and how to reach them.
+enum Backend {
+    /// Owned by a CUDA device worker; scored through its channel.
+    Worker {
+        worker: Arc<DeviceWorkerHandle>,
+        handle: DecisionHandle,
+    },
+    /// CPU fallback. `LayaModel::forward` takes `&self`, so concurrent
+    /// requests share it without a lock.
+    Cpu(Arc<LayaModel>),
+}
+
+/// The files one checkpoint needs, already on local disk.
+#[derive(Debug, Clone)]
+pub struct CheckpointFiles {
+    /// The checkpoint directory: holds `rl_agent_config.json`,
+    /// `encoder/config.json` and `model.safetensors`.
+    pub dir: PathBuf,
+    pub tokenizer: PathBuf,
+}
+
+pub struct DecisionCheckpoint {
+    /// The checkpoint's routing name: [`ROOT_CHECKPOINT`] for the repo
+    /// root, else its subfolder (`multilingual`, `typed-decisions`).
+    pub name: String,
+    pub config: CheckpointConfig,
+    /// Budgets and fitted temperatures from `rl_agent_config.json`, as
+    /// the prompt builder and calibration read them.
+    pub calibration: DecisionConfig,
+    pub tokenizer: DecisionTokenizer,
+    /// Precision the weights were loaded in.
+    pub dtype: DType,
+    backend: Backend,
+}
+
+impl DecisionCheckpoint {
+    /// Load one checkpoint onto `worker`'s device, or onto the CPU when
+    /// `worker` is `None`.
+    pub async fn load(
+        name: String,
+        files: CheckpointFiles,
+        model_id: &str,
+        worker: Option<Arc<DeviceWorkerHandle>>,
+    ) -> Result<Self> {
+        let config = CheckpointConfig::from_dir(&files.dir)?;
+        let calibration_path = files.dir.join("rl_agent_config.json");
+        let calibration = DecisionConfig::from_json(
+            &std::fs::read_to_string(&calibration_path)
+                .with_context(|| format!("read {}", calibration_path.display()))?,
+        )
+        .map_err(|e| anyhow::anyhow!("{e}"))?;
+        let tokenizer_dir = files
+            .tokenizer
+            .parent()
+            .context("tokenizer.json has no parent directory")?;
+        let tokenizer = DecisionTokenizer::from_dir(tokenizer_dir)?;
+        let (dtype, backend) = match worker {
+            Some(worker) => {
+                let wanted = device_dtype(config.laya.amp_dtype.as_deref());
+                let (handle, dtype) = worker
+                    .load_decision(files.dir.clone(), model_id.to_string(), wanted)
+                    .await
+                    .map_err(|e| anyhow::anyhow!("worker load_decision: {e:#}"))?;
+                (dtype, Backend::Worker { worker, handle })
+            }
+            None => {
+                let dir = files.dir.clone();
+                let model = tokio::task::spawn_blocking(move || {
+                    LayaModel::load(&dir, &candle_core::Device::Cpu, DType::F32)
+                })
+                .await
+                .context("decision load task")??;
+                (DType::F32, Backend::Cpu(Arc::new(model)))
+            }
+        };
+        Ok(Self {
+            name,
+            config,
+            calibration,
+            tokenizer,
+            dtype,
+            backend,
+        })
+    }
+
+    /// Score one batch: one [`DecisionRow`] per sequence, in order.
+    pub async fn decide(&self, batch: DecisionBatch) -> Result<Vec<DecisionRow>> {
+        match &self.backend {
+            Backend::Worker { worker, handle } => worker
+                .decide(*handle, batch)
+                .await
+                .map_err(|e| anyhow::anyhow!("{e:#}")),
+            Backend::Cpu(model) => {
+                let model = Arc::clone(model);
+                tokio::task::spawn_blocking(move || model.forward(&batch))
+                    .await
+                    .context("decision forward task")?
+            }
+        }
+    }
+
+    /// Release the weights. On a worker this drops them on the thread
+    /// that owns the device context.
+    pub async fn release(&self) {
+        if let Backend::Worker { worker, handle } = &self.backend
+            && let Err(e) = worker.drop_decision(*handle).await
+        {
+            tracing::warn!(
+                checkpoint = %self.name,
+                error = %e,
+                "decision unload: DropDecision RPC failed (weights may leak in worker slab)"
+            );
+        }
+    }
+}
+
+/// The precision to serve in on a GPU: the autocast dtype the reference
+/// itself runs there (`amp_dtype` in `rl_agent_config.json`), else bf16.
+fn device_dtype(amp_dtype: Option<&str>) -> DType {
+    match amp_dtype {
+        Some("fp16") | Some("float16") => DType::F16,
+        Some("fp32") | Some("float32") => DType::F32,
+        _ => DType::BF16,
+    }
+}
+
+pub struct LoadedDecisionModel {
+    pub model_id: String,
+    pub spec: ModelSpec,
+    pub devices: Vec<u32>,
+    /// At least one; the first is the default.
+    pub checkpoints: Vec<DecisionCheckpoint>,
+    /// Set when a forward returns a driver-level error, mirroring the
+    /// other model kinds: `/models` reports it and requests fast-reject.
+    pub poisoned: Arc<AtomicBool>,
+    pub admission: super::admission::AdmissionController,
+}
+
+impl LoadedDecisionModel {
+    /// The checkpoint called `name`, or the default one for `None`.
+    pub fn checkpoint(&self, name: Option<&str>) -> Option<&DecisionCheckpoint> {
+        match name {
+            None => self.checkpoints.first(),
+            Some(n) => self.checkpoints.iter().find(|c| c.name == n),
+        }
+    }
+
+    pub async fn release(&self) {
+        for c in &self.checkpoints {
+            c.release().await;
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn tiny() -> CheckpointFiles {
+        let dir =
+            std::path::Path::new(env!("CARGO_MANIFEST_DIR")).join("src/harness/testdata/laya/tiny");
+        CheckpointFiles {
+            tokenizer: dir.join("tokenizer.json"),
+            dir,
+        }
+    }
+
+    #[test]
+    fn gpu_dtype_follows_the_reference_autocast() {
+        assert_eq!(device_dtype(Some("bf16")), DType::BF16);
+        assert_eq!(device_dtype(Some("fp16")), DType::F16);
+        assert_eq!(device_dtype(None), DType::BF16);
+    }
+
+    /// The whole async path on the CPU backend, and the device-worker
+    /// path on a CPU build (the worker runs, with a CPU device), must
+    /// give the same scores as the model called directly.
+    #[tokio::test]
+    async fn both_backends_score_like_the_model() {
+        let files = tiny();
+        let direct = LayaModel::load(&files.dir, &candle_core::Device::Cpu, DType::F32).unwrap();
+        let batch = DecisionBatch {
+            seqs: vec![super::super::arch::laya::DecisionSeq {
+                input_ids: vec![5, 9, 11, 3, 7, 2],
+                markers: vec![1, 3],
+                qtype: 0,
+            }],
+        };
+        let want = direct.forward(&batch).unwrap();
+
+        let cpu = DecisionCheckpoint::load("root".into(), files.clone(), "tiny", None)
+            .await
+            .unwrap();
+        assert_eq!(cpu.decide(batch.clone()).await.unwrap(), want);
+
+        // A CPU build's worker holds a CPU device, so this exercises the
+        // job round-trip (load, decide, drop) without a GPU — and the
+        // worker must fall back to f32 there, reporting that it did.
+        let worker = DeviceWorkerHandle::spawn(0).unwrap();
+        let on_worker =
+            DecisionCheckpoint::load("root".into(), files, "tiny", Some(worker.clone()))
+                .await
+                .unwrap();
+        #[cfg(not(feature = "cuda"))]
+        {
+            assert_eq!(on_worker.dtype, DType::F32);
+            assert_eq!(on_worker.decide(batch).await.unwrap(), want);
+        }
+        #[cfg(feature = "cuda")]
+        {
+            let got = on_worker.decide(batch).await.unwrap();
+            for (g, w) in got[0].logits.iter().zip(&want[0].logits) {
+                assert!((g - w).abs() < 5e-2, "{g} vs {w}");
+            }
+        }
+        on_worker.release().await;
+        assert!(on_worker.decide(DecisionBatch::default()).await.is_err());
+        worker.shutdown().unwrap();
+    }
+}
@@ -13,12 +13,13 @@
 //! ARCH model state in this state slab will gain a companion
 //! `tp_models: HashMap<TpHandle, Box<TpLeaderModel>>`.

+use crate::harness::arch::laya::LayaModel;
 use crate::harness::arch::snapshot::KvCacheSnapshot;
 use crate::harness::candle::ModelArch;
 #[cfg(feature = "cuda")]
 use crate::harness::device_worker::jobs::TpHandle;
 use crate::harness::device_worker::jobs::{
-    ArchHandle, DenseLoad, ImageHandle, ImageInput, Job, KvSnapshotId,
+    ArchHandle, DecisionHandle, DenseLoad, ImageHandle, ImageInput, Job, KvSnapshotId,
 };
 use crate::harness::image::ZImagePipeline;
 #[cfg(feature = "cuda")]
@@ -65,6 +66,11 @@ struct DeviceWorkerState {
    image_models: HashMap<ImageHandle, Box<ZImagePipeline>>,
    /// Counter for minting fresh `ImageHandle`s.
    next_image_handle: u64,
+    /// Laya decision-model slab (#336). Same lifecycle discipline as
+    /// `image_models`, in its own namespace.
+    decision_models: HashMap<DecisionHandle, Box<LayaModel>>,
+    /// Counter for minting fresh `DecisionHandle`s.
+    next_decision_handle: u64,
    /// Leader's NCCL state. Populated by `Job::NcclInit`; the
    /// underlying `Comm`'s libnccl handle lives bound to this thread
    /// for its entire lifetime. Subprocess workers maintain their own
@@ -751,6 +757,62 @@ pub(crate) fn run(device_index: u32, rx: Receiver<Job>, poisoned: Arc<AtomicBool
                trim_device_pool(&state);
                let _ = reply.send(result);
            }
+            Job::LoadDecision {
+                dir,
+                model_id,
+                dtype,
+                reply,
+            } => {
+                let dtype = if state.device.is_cpu() {
+                    candle_core::DType::F32
+                } else {
+                    dtype
+                };
+                let result = LayaModel::load(&dir, &state.device, dtype)
+                    .with_context(|| format!("load laya checkpoint {}", dir.display()))
+                    .map(|model| {
+                        let handle = DecisionHandle(state.next_decision_handle);
+                        state.next_decision_handle = state.next_decision_handle.wrapping_add(1);
+                        state.decision_models.insert(handle, Box::new(model));
+                        tracing::info!(
+                            device_index,
+                            model_id,
+                            checkpoint = %dir.display(),
+                            handle = handle.0,
+                            "device worker: decision model loaded"
+                        );
+                        (handle, dtype)
+                    });
+                trim_device_pool(&state);
+                let _ = reply.send(result);
+            }
+            Job::DropDecision { handle, reply } => {
+                let removed = state.decision_models.remove(&handle);
+                let was_present = removed.is_some();
+                // Explicit drop on this thread, as for DropArch/DropImage.
+                drop(removed);
+                if was_present {
+                    trim_device_pool(&state);
+                }
+                tracing::debug!(
+                    device_index,
+                    handle = handle.0,
+                    was_present,
+                    "device worker: decision model dropped"
+                );
+                let _ = reply.send(());
+            }
+            Job::Decide {
+                handle,
+                batch,
+                reply,
+            } => {
+                let result = match state.decision_models.get(&handle) {
+                    Some(model) => model.forward(&batch),
+                    None => Err(anyhow::anyhow!("no decision model for handle {}", handle.0)),
+                };
+                let _ = reply.send(result);
+            }
            Job::Shutdown => unreachable!("Shutdown should break above"),
        }
    }
@@ -823,6 +885,8 @@ fn init_state(device_index: u32) -> DeviceWorkerState {
            next_kv_snapshot_id: 1,
            image_models: HashMap::new(),
            next_image_handle: 1,
+            decision_models: HashMap::new(),
+            next_decision_handle: 1,
            nccl: NcclState::new(),
            tp_models: HashMap::new(),
            next_tp_handle: 1,
@@ -841,6 +905,8 @@ fn init_state(device_index: u32) -> DeviceWorkerState {
            next_kv_snapshot_id: 1,
            image_models: HashMap::new(),
            next_image_handle: 1,
+            decision_models: HashMap::new(),
+            next_decision_handle: 1,
            nccl: NcclState::new(),
        }
    }
@@ -1969,6 +2035,15 @@ fn drain_poisoned(job: Job, device_index: u32) {
        Job::GenerateImage { reply, .. } => {
            let _ = reply.send(Err(err()));
        }
+        Job::LoadDecision { reply, .. } => {
+            let _ = reply.send(Err(err()));
+        }
+        Job::DropDecision { reply, .. } => {
+            let _ = reply.send(());
+        }
+        Job::Decide { reply, .. } => {
+            let _ = reply.send(Err(err()));
+        }
        Job::ForwardLogitsWithImages { reply, .. } => {
            let _ = reply.send(Err(err()));
        }
@@ -59,6 +59,15 @@ pub struct TpHandle(pub u64);
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub struct ImageHandle(pub u64);

+/// Opaque handle to a Laya decision model ([`LayaModel`]) stored in the
+/// worker thread's decision slab (#336). Same discipline as
+/// [`ImageHandle`]: construction, every forward and `Drop` happen on the
+/// worker thread, and `Job::DropDecision` is the only way to release it.
+///
+/// [`LayaModel`]: crate::harness::arch::laya::LayaModel
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
+pub struct DecisionHandle(pub u64);
+
 /// Opaque handle to a prefix-cache snapshot (#11) stored worker-side
 /// next to the model slab. Scoped to the `ArchHandle` it was captured
 /// from — `Job::DropArch` drops every snapshot under its handle. The
@@ -549,6 +558,33 @@ pub enum Job {
        max_dim: usize,
        reply: oneshot::Sender<Result<crate::harness::image::ImageGenResult>>,
    },
+    /// Load one Laya checkpoint directory (#336) — the repo root or one
+    /// of its subfolders, already on local disk — into the decision
+    /// slab. File resolution (hf-hub) happens on the async side.
+    ///
+    /// `dtype` is the precision wanted on a GPU; a worker whose device
+    /// is the CPU loads in f32 instead (the CPU backend has no bf16
+    /// matmul). The reply carries the dtype actually used.
+    LoadDecision {
+        dir: PathBuf,
+        model_id: String,
+        dtype: candle_core::DType,
+        reply: oneshot::Sender<Result<(DecisionHandle, candle_core::DType)>>,
+    },
+    /// Remove a decision model from the slab and drop it on the worker
+    /// thread. Mirrors `DropArch` / `DropImage`.
+    DropDecision {
+        handle: DecisionHandle,
+        reply: oneshot::Sender<()>,
+    },
+    /// Score one batch of assembled questions (#336). One job per
+    /// request: the whole batch is a single encoder forward, and the
+    /// reply is CPU-side scores — no device tensor crosses back.
+    Decide {
+        handle: DecisionHandle,
+        batch: crate::harness::arch::laya::DecisionBatch,
+        reply: oneshot::Sender<Result<Vec<crate::harness::arch::laya::DecisionRow>>>,
+    },
    /// Tell the worker to break its dispatch loop and exit. Any jobs
    /// queued after this in the channel reply `Err` to their oneshot
    /// senders (the senders are dropped on the worker's exit, which
@@ -51,7 +51,7 @@ use tokio::sync::oneshot;

 #[cfg(feature = "cuda")]
 pub use jobs::TpHandle;
-pub use jobs::{ArchHandle, DenseLoad, ImageHandle, Job, KvSnapshotId};
+pub use jobs::{ArchHandle, DecisionHandle, DenseLoad, ImageHandle, Job, KvSnapshotId};

 /// Errors returned by `DeviceWorkerHandle` submit methods.
 #[derive(Debug, thiserror::Error)]
@@ -337,6 +337,85 @@ impl DeviceWorkerHandle {
        }
    }

+    /// Load one Laya checkpoint directory (#336) on the worker thread.
+    /// Returns the handle and the dtype the weights were loaded in.
+    pub async fn load_decision(
+        &self,
+        dir: std::path::PathBuf,
+        model_id: String,
+        dtype: candle_core::DType,
+    ) -> Result<(DecisionHandle, candle_core::DType), WorkerError> {
+        if self.poisoned.load(Ordering::Acquire) {
+            return Err(WorkerError::Poisoned {
+                device_index: self.device_index,
+            });
+        }
+        let (reply_tx, reply_rx) = oneshot::channel();
+        self.tx
+            .send(Job::LoadDecision {
+                dir,
+                model_id,
+                dtype,
+                reply: reply_tx,
+            })
+            .map_err(|_| WorkerError::Gone {
+                device_index: self.device_index,
+            })?;
+        match reply_rx.await {
+            Ok(result) => result.map_err(WorkerError::from),
+            Err(_) => Err(WorkerError::Gone {
+                device_index: self.device_index,
+            }),
+        }
+    }
+
+    /// Drop the decision model for `handle` on the worker thread. Same
+    /// poison-tolerant contract as [`Self::drop_arch`].
+    pub async fn drop_decision(&self, handle: DecisionHandle) -> Result<(), WorkerError> {
+        let (reply_tx, reply_rx) = oneshot::channel();
+        self.tx
+            .send(Job::DropDecision {
+                handle,
+                reply: reply_tx,
+            })
+            .map_err(|_| WorkerError::Gone {
+                device_index: self.device_index,
+            })?;
+        reply_rx.await.map_err(|_| WorkerError::Gone {
+            device_index: self.device_index,
+        })
+    }
+
+    /// Score one batch of assembled questions (#336). The reply is
+    /// CPU-side scores; no device tensor crosses back.
+    pub async fn decide(
+        &self,
+        handle: DecisionHandle,
+        batch: crate::harness::arch::laya::DecisionBatch,
+    ) -> Result<Vec<crate::harness::arch::laya::DecisionRow>, WorkerError> {
+        if self.poisoned.load(Ordering::Acquire) {
+            return Err(WorkerError::Poisoned {
+                device_index: self.device_index,
+            });
+        }
+        let (reply_tx, reply_rx) = oneshot::channel();
+        self.tx
+            .send(Job::Decide {
+                handle,
+                batch,
+                reply: reply_tx,
+            })
+            .map_err(|_| WorkerError::Gone {
+                device_index: self.device_index,
+            })?;
+        match reply_rx.await {
+            Ok(result) => result.map_err(WorkerError::from),
+            Err(_) => Err(WorkerError::Gone {
+                device_index: self.device_index,
+            }),
+        }
+    }
+
    /// Tell the worker to drop the `ModelArch` for `handle` on the
    /// worker thread (so CUDA tensors release on the right context).
    /// Returns `Ok(())` even if the handle wasn't in the slab — Drop
@@ -6,10 +6,13 @@ pub mod candle;
 pub mod capability;
 pub mod chat_template;
 pub mod context_limit;
+pub mod decision;
+pub mod decision_model;
 pub mod device_worker;
 pub mod engine;
 pub mod image;
 pub mod isq_cache;
+pub mod lang_detect;
 pub mod prefix_cache;
 pub mod preflight;
 pub mod preprocess;
@@ -54,6 +54,11 @@ pub enum SourceFormat {
    /// Takes precedence over the safetensors sniff because diffusers
    /// repos also contain `.safetensors` shards (in subdirs).
    Diffusers,
+    /// A Laya decision-model checkpoint (#336): `rl_agent_config.json`
+    /// plus `encoder/config.json` at the root, no top-level
+    /// `config.json`. Served by the decision path — single device,
+    /// unquantized. Takes precedence over the safetensors sniff.
+    DecisionModel,
 }

 /// Output of `preflight` for a load that can proceed. Carries the
@@ -115,6 +120,12 @@ pub enum PreflightError {
        available: Vec<String>,
        nearest: Option<String>,
    },
+
+    /// The repo's format cannot be served with an option the spec
+    /// asked for (e.g. `tensor_parallel` or `quant` on a decision
+    /// model). `reason` says which and what to set instead.
+    #[error("cannot load '{model_id}' as requested: {reason}")]
+    Unsupported { model_id: String, reason: String },
 }

 /// Run the placement check.
@@ -230,6 +241,24 @@ pub async fn preflight(
        // The architecture compatibility check stays where it is —
        // `check_dense_config_supported` runs once `config.json` is
        // on disk, since it needs the parsed JSON.
+        // Decision models (#336) load unquantized on one device.
+        (SourceFormat::DecisionModel, tp, _) if tp > 1 => Err(PreflightError::Unsupported {
+            model_id: source_id.to_string(),
+            reason: format!(
+                "decision models load on a single device; tensor_parallel={tp} is not supported"
+            ),
+        }),
+        (SourceFormat::DecisionModel, _, Some(q)) => Err(PreflightError::Unsupported {
+            model_id: source_id.to_string(),
+            reason: format!("decision models load unquantized; remove quant={q:?}"),
+        }),
+        (SourceFormat::DecisionModel, _, None) => Ok(PlacementPlan {
+            model_id: source_id.to_string(),
+            format: format.clone(),
+            tp_size,
+            picked_quant_file: None,
+        }),
+
        (SourceFormat::DenseSafetensors { .. } | SourceFormat::Mixed { .. }, _, _) => {
            Ok(PlacementPlan {
                model_id: source_id.to_string(),
@@ -295,6 +324,9 @@ pub fn classify(filenames: &[&str]) -> SourceFormat {
    if crate::harness::image::is_diffusers_layout(filenames) {
        return SourceFormat::Diffusers;
    }
+    if is_decision_layout(filenames) {
+        return SourceFormat::DecisionModel;
+    }
    let mut gguf_quants: Vec<String> = filenames
        .iter()
        .filter(|f| f.to_lowercase().ends_with(".gguf"))
@@ -318,6 +350,16 @@ pub fn classify(filenames: &[&str]) -> SourceFormat {
    }
 }

+/// A Laya decision checkpoint at the repo root: its decision config and
+/// its encoder config, with no top-level `config.json` (a causal LM
+/// that happened to ship a file with the same name still classifies as
+/// a text model).
+pub fn is_decision_layout(filenames: &[&str]) -> bool {
+    filenames.contains(&"rl_agent_config.json")
+        && filenames.contains(&"encoder/config.json")
+        && !filenames.contains(&"config.json")
+}
+
 /// Mirror of the quant-matching logic in `candle.rs::resolve_files` so
 /// preflight picks the same file the downstream loader would. Empty
 /// quant returns the first `.gguf` (any quant). Lowercased substring
@@ -651,6 +693,20 @@ mod tests {
                tp_size,
                picked_quant_file: None,
            }),
+            (SourceFormat::DecisionModel, tp, _) if tp > 1 => Err(PreflightError::Unsupported {
+                model_id: source_id.to_string(),
+                reason: format!("decision models load on a single device; tensor_parallel={tp}"),
+            }),
+            (SourceFormat::DecisionModel, _, Some(q)) => Err(PreflightError::Unsupported {
+                model_id: source_id.to_string(),
+                reason: format!("decision models load unquantized; remove quant={q:?}"),
+            }),
+            (SourceFormat::DecisionModel, _, None) => Ok(PlacementPlan {
+                model_id: source_id.to_string(),
+                format: format.clone(),
+                tp_size,
+                picked_quant_file: None,
+            }),
            (SourceFormat::DenseSafetensors { .. } | SourceFormat::Mixed { .. }, _, _) => {
                Ok(PlacementPlan {
                    model_id: source_id.to_string(),
@@ -674,6 +730,66 @@ mod tests {
        assert_eq!(classify(&files), SourceFormat::Diffusers);
    }

+    /// The released `convaiinnovations/laya` listing: the English
+    /// checkpoint at the root, two more in subfolders, and no root
+    /// `config.json`.
+    const LAYA_FILES: [&str; 12] = [
+        "README.md",
+        "rl_agent_config.json",
+        "encoder/config.json",
+        "model.safetensors",
+        "tokenizer/tokenizer.json",
+        "tokenizer/tokenizer_config.json",
+        "multilingual/rl_agent_config.json",
+        "multilingual/encoder/config.json",
+        "multilingual/model.safetensors",
+        "typed-decisions/rl_agent_config.json",
+        "typed-decisions/encoder/config.json",
+        "typed-decisions/model.safetensors",
+    ];
+
+    #[test]
+    fn classify_laya_layout() {
+        assert_eq!(classify(&LAYA_FILES), SourceFormat::DecisionModel);
+        // A causal LM that also ships an rl config is still a text model.
+        let lm = [
+            "config.json",
+            "rl_agent_config.json",
+            "encoder/config.json",
+            "model.safetensors",
+        ];
+        assert!(matches!(
+            classify(&lm),
+            SourceFormat::DenseSafetensors { .. }
+        ));
+    }
+
+    #[test]
+    fn feasibility_decision_model() {
+        let fmt = classify(&LAYA_FILES);
+        let plan = decide(
+            &spec("convaiinnovations/laya", None, None),
+            &fmt,
+            &LAYA_FILES,
+        )
+        .expect("single-device unquantized decision load should pass");
+        assert_eq!(plan.format, SourceFormat::DecisionModel);
+        let err = decide(
+            &spec("convaiinnovations/laya", Some(2), None),
+            &fmt,
+            &LAYA_FILES,
+        )
+        .expect_err("tp>1 must be refused");
+        assert!(matches!(err, PreflightError::Unsupported { .. }));
+        let err = decide(
+            &spec("convaiinnovations/laya", None, Some("q8_0")),
+            &fmt,
+            &LAYA_FILES,
+        )
+        .expect_err("quant must be refused");
+        assert!(matches!(err, PreflightError::Unsupported { .. }));
+    }
+
    #[test]
    fn feasibility_diffusers_single_gpu_ok() {
        let files = ["model_index.json", "transformer/config.json"];
@@ -0,0 +1,52 @@
+{
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 1,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 2,
+  "global_attn_every_n_layers": 3,
+  "hidden_activation": "gelu",
+  "hidden_size": 32,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.3,
+  "intermediate_size": 48,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 8,
+  "max_position_embeddings": 128,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 2,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 1000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 300.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 2,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.17.0",
+  "vocab_size": 97
+}
@@ -0,0 +1,182 @@
+{
+ "seed": 7,
+ "seqs": [
+  {
+   "input_ids": [
+    1,
+    93,
+    82,
+    87,
+    51,
+    46,
+    89,
+    6,
+    68,
+    94,
+    86,
+    43,
+    23,
+    70,
+    42,
+    60,
+    35,
+    3,
+    86,
+    7,
+    20,
+    18,
+    90,
+    84,
+    62,
+    30,
+    42,
+    65,
+    34,
+    37,
+    30,
+    67,
+    2,
+    34,
+    77,
+    96,
+    24,
+    89,
+    19,
+    33
+   ],
+   "markers": [
+    3,
+    9,
+    17
+   ],
+   "qtype": 0,
+   "logits": [
+    -0.4152399003505707,
+    -0.5505563020706177,
+    -0.600245475769043
+   ],
+   "act": [
+    0.4727683663368225,
+    0.5272316336631775
+   ]
+  },
+  {
+   "input_ids": [
+    4,
+    5,
+    43,
+    95,
+    77,
+    35,
+    61,
+    13,
+    42,
+    24,
+    21,
+    7,
+    11,
+    38,
+    80,
+    54,
+    4,
+    29,
+    5,
+    80,
+    54,
+    78,
+    46
+   ],
+   "markers": [
+    4,
+    11
+   ],
+   "qtype": 1,
+   "logits": [
+    -0.1313231736421585,
+    -0.3265465199947357
+   ],
+   "act": [
+    0.40568628907203674,
+    0.5943136811256409
+   ]
+  },
+  {
+   "input_ids": [
+    67,
+    40,
+    83,
+    41,
+    77,
+    96,
+    58,
+    39,
+    76
+   ],
+   "markers": [
+    2
+   ],
+   "qtype": 2,
+   "logits": [
+    0.534652590751648
+   ],
+   "act": [
+    0.4226877987384796,
+    0.5773122310638428
+   ]
+  },
+  {
+   "input_ids": [
+    96,
+    32,
+    60,
+    44,
+    35,
+    47,
+    45,
+    51,
+    60,
+    51,
+    73,
+    50,
+    25,
+    79,
+    74,
+    24,
+    83,
+    7,
+    9,
+    91,
+    21,
+    87,
+    29,
+    25,
+    74,
+    18,
+    31,
+    46,
+    46,
+    30,
+    52
+   ],
+   "markers": [
+    5,
+    7,
+    12,
+    20,
+    26
+   ],
+   "qtype": 0,
+   "logits": [
+    -0.4001395106315613,
+    -0.5266168117523193,
+    -0.44161877036094666,
+    -0.43973028659820557,
+    -0.5682165622711182
+   ],
+   "act": [
+    0.46282079815864563,
+    0.537179172039032
+   ]
+  }
+ ]
+}
@@ -0,0 +1,16 @@
+{
+ "encoder": "tiny-modernbert",
+ "head_layers": 2,
+ "max_len": 64,
+ "head_max_len": 24,
+ "act_costs": {
+  "escalate": 0.5
+ },
+ "amp_dtype": "bf16",
+ "temperature": [
+  1.0,
+  1.0,
+  1.0
+ ],
+ "temperature_by_options": {}
+}
@@ -0,0 +1 @@
+{"version": "1.0", "truncation": null, "padding": null, "added_tokens": [], "normalizer": null, "pre_tokenizer": {"type": "Whitespace"}, "post_processor": null, "decoder": null, "model": {"type": "WordLevel", "vocab": {"[PAD]": 0, "[UNK]": 1, "[CLS]": 2, "[SEP]": 3, "[MASK]": 4, "a": 5, "b": 6}, "unk_token": "[UNK]"}}
@@ -0,0 +1,7 @@
+{
+ "cls_token": "[CLS]",
+ "sep_token": "[SEP]",
+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
+ "unk_token": "[UNK]"
+}
@@ -0,0 +1,14 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]"
+}
@@ -158,6 +158,7 @@ fn preflight_kind(err: &PreflightError) -> &'static str {
        PreflightError::EmptyRepo { .. } => "empty_repo",
        PreflightError::TpRequiresSafetensors { .. } => "tp_requires_safetensors",
        PreflightError::QuantNotFound { .. } => "quant_not_found",
+        PreflightError::Unsupported { .. } => "unsupported_load_option",
    }
 }

@@ -820,3 +820,55 @@ async fn null_assistant_content_is_not_a_body_rejection() {
    assert_ne!(resp.status(), 422, "null content must not be a body error");
    assert_ne!(resp.status(), 400, "null content must not be a body error");
 }
+
+// ── /v1/audio/generations (#382) ─────────────────────────────────
+//
+// The neuron half of the async audio job API: one synchronous render per
+// call. The envelope and error mapping are tested here; rendering needs an
+// audio engine (#383) and is validated on the fleet.
+
+#[tokio::test]
+async fn test_audio_no_candle_harness_503() {
+    let url = spawn_neuron(fake_discovery()).await;
+    let resp = reqwest::Client::new()
+        .post(format!("{url}/v1/audio/generations"))
+        .json(&json!({"model": "m", "prompt": "lofi"}))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 503);
+}
+
+#[tokio::test]
+async fn test_audio_model_not_loaded_404() {
+    let url = spawn_neuron_with_candle(fake_discovery()).await;
+    let resp = reqwest::Client::new()
+        .post(format!("{url}/v1/audio/generations"))
+        .json(&json!({"model": "ACE-Step/Ace-Step1.5", "prompt": "lofi"}))
+        .send()
+        .await
+        .unwrap();
+    assert_eq!(resp.status(), 404);
+    let body: serde_json::Value = resp.json().await.unwrap();
+    assert_eq!(body["error"]["code"], "model_not_found");
+}
+
+#[tokio::test]
+async fn test_audio_invalid_params_400() {
+    let url = spawn_neuron_with_candle(fake_discovery()).await;
+    for bad in [
+        json!({"model": "m", "prompt": ""}),
+        json!({"model": "m", "prompt": "lofi", "duration": -3}),
+        json!({"model": "m", "prompt": "lofi", "response_format": "ogg"}),
+    ] {
+        let resp = reqwest::Client::new()
+            .post(format!("{url}/v1/audio/generations"))
+            .json(&bad)
+            .send()
+            .await
+            .unwrap();
+        assert_eq!(resp.status(), 400, "{bad}");
+        let body: serde_json::Value = resp.json().await.unwrap();
+        assert_eq!(body["error"]["code"], "invalid_audio_params", "{bad}");
+    }
+}
@@ -10,6 +10,9 @@ Restart=on-failure
 RestartSec=5
 User=cortex
 Group=cortex
+# /var/lib/cortex, owned by the service user: async audio jobs (#382)
+# keep their records and rendered audio here.
+StateDirectory=cortex

 [Install]
 WantedBy=multi-user.target
@@ -118,12 +118,10 @@ directly useful for P2 (#11 prefix KV caching, #23 chunked prefill,
 ## Misc operational facts

 - Fleet: beast 2×5090 (27B Q6K TP=2), benjy 4090 (8B), quadbrat 3060
-  (1.7B); gateway on hanzalova:31313; all reachable by ssh as the
-  operator (passwordless sudo) — used for sudoers sync, live probes,
-  and killing wedged processes during P1.
- The tea CLI token (`~/.config/tea/config.yml`) authenticates against
-  git.lair.cafe for everything the gitea-mcp tools don't cover
-  (milestones, labels, pin, merge).
+  (1.7B); gateway on hanzalova:31313. Operator access to the hosts
+  (used for sudoers sync, live probes, and killing wedged processes
+  during P1) is described in `doc/infra/operator-access.md` in the
+  private helexa/security repo.
 - Anthropic streaming is now production-real: Claude Code can be
  pointed at the gateway. Real-traffic feedback from that is useful
  input for P2 priorities (especially tool-use-heavy streams).
@@ -58,6 +58,6 @@ timeout_secs = 30
 # whether a mail also goes out. An unknown provider is a hard startup
 # failure rather than silently swallowed notifications.
 provider = "smtp"
-smtp_url = "smtps://user%40l4ir.net:CHANGEME@mail.l4ir.net:465"
+smtp_url = "smtps://mailer%40example.org:CHANGEME@smtp.example.org:465"
 from_addr = "helexa <no-reply@helexa.ai>"
 notify_to = "angels@helexa.ai"
@@ -26,8 +26,15 @@ region = "eu-central"

 # Product tiers → real model ids (#166). The SPA sends the tier names.
 [aliases]
-"helexa/small" = "Qwen/Qwen3-8B"
+# helexa/small is the tier anonymous web chat uses. It points at the 1.7B on
+# quadbrat, the same target as the catalogue alias in models.toml: the 8B
+# needs benjy, and while benjy serves nothing an alias aimed there made
+# anonymous chat answer "no operator serves model 'Qwen/Qwen3-8B'".
+"helexa/small" = "Qwen/Qwen3-1.7B"
 "helexa/balanced" = "Qwen/Qwen3.8-27B"
+# System-One decisions (#333). Also the model /v1/systemone falls back
+# to when a request names none or names a service no operator serves.
+"helexa/one" = "convaiinnovations/laya"

 # hanzalova operator cortex, plaintext over the WireGuard mesh (the mesh
 # is the trust boundary; no TLS pin needed for http://).
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`{"version": "1.0", "truncation": null, "padding": null, "added_tokens": [], "normalizer": null, "pre_tokenizer": {"type": "Whitespace"}, "post_processor": null, "decoder": null, "model": {"type": "WordLevel", "vocab": {"[PAD]": 0, "[UNK]": 1, "[CLS]": 2, "[SEP]": 3, "[MASK]": 4, "a": 5, "b": 6}, "unk_token": "[UNK]"}}`