diff --git a/services/intern-decision-serve/acceptance/systemone-2026-09-30/README.md b/services/intern-decision-serve/acceptance/systemone-2026-09-30/README.md
new file mode 100644
index 0000000..cb2fed6
--- /dev/null
+++ b/services/intern-decision-serve/acceptance/systemone-2026-09-30/README.md
@@ -0,0 +1,31 @@
+# /v1/systemone acceptance — 0.1.1 on fv-ml1, 2026-09-30
+
+Live service: `http://intern-decision.fv.internal:8033`, image `intern-decision-serve:0.1.1`
+(built in `/opt/docker/src/intern-decision-serve-0.1.1`; 0.1.0 kept for rollback).
+
+## JevBench (the pass/fail line)
+
+JevBench v1.2.16 (5e95f23), its own `typesafe` adapter, over the 231 public items:
+
+    TYPESAFE_ENDPOINT=http://intern-decision.fv.internal:8033 TYPESAFE_API_KEY=$(secret get intern-decision/api-token) \
+    PYTHONPATH=/tmp/jevbench python3 -m jevbench.cli run --tasks datasets/public/{easy,original,hard}.jsonl \
+      --adapter typesafe --results ... --ledger ... --raw-dir ... --price-in-per-m 0 --price-out-per-m 0
+
+- **all 202/231, hard 83/111** — matches the expected numbers exactly.
+- Row-by-row vs the bench's own runs r1..r4 (`bench-jev-2026-09-30/raw/out/intern-decision-4b-native/`):
+  **0 changed answers/probabilities across 924 rows.**
+- Artifacts here: jb-results.jsonl, jb-summary.json, jb-ledger.jsonl, jb-manifest.json.
+- The first-run finding: the response `model` must be a string — the runner hashes it into
+  its manifest and a dict crashed the manifest step. Fixed before the run above.
+
+## Controls (live)
+
+- wrong token → 401; 17 questions → 422 ("never chunked"); `images` → 422 "images not supported".
+- token limit at the boundary: 7,167 → 200, **7,169 → 422** (binary search; "one token over").
+- `/decide/shared` positive control: 2 decisions, 1 call, fields q1/q2 — unchanged.
+
+## Memory (GPU 1 is packed beside scriberr)
+
+`gpu1-peak.csv`: per-process GPU 1 footprint, nvidia-smi at 0.2 s, container PID resolved via
+docker inspect. Largest accepted request (16 noul questions, 7,167-token prompt) found by binary
+search. **Peak 9,866 MiB ≤ 9,876 budget** (10 MiB inside).
diff --git a/services/intern-decision-serve/acceptance/systemone-2026-09-30/gpu1-peak.csv b/services/intern-decision-serve/acceptance/systemone-2026-09-30/gpu1-peak.csv
new file mode 100644
index 0000000..8129e5d
--- /dev/null
+++ b/services/intern-decision-serve/acceptance/systemone-2026-09-30/gpu1-peak.csv
@@ -0,0 +1,200 @@
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diff --git a/services/intern-decision-serve/acceptance/systemone-2026-09-30/health-end.json b/services/intern-decision-serve/acceptance/systemone-2026-09-30/health-end.json
new file mode 100644
index 0000000..e32fe86
--- /dev/null
+++ b/services/intern-decision-serve/acceptance/systemone-2026-09-30/health-end.json
@@ -0,0 +1 @@
+{"status":"ok","model":{"name":"Intern-Decision-4B","source":"internlm/Intern-Decision-4B","revision":"0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","checkpoint":"/hf/hub/models--internlm--Intern-Decision-4B/snapshots/0e5e6aa7d6d750e2b1504ba11a8136cb58aeb3cd","inference_py_sha256":"c904e2c67ca0775621a22375ee373d2ba30b52117cda870c6c9ef74143b29863","temperature":1.99241824,"dtype":"bfloat16","attn_implementation":"sdpa","device":"cuda","max_length":7168,"torch_version":"2.10.0+cu128","transformers_version":"5.17.0","vision_tower":"removed","device_name":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","allocated_gib":7.937,"reserved_gib":7.969,"max_reserved_gib":8.861},"vram_cap_gib":9.0,"max_tokens":7168,"max_decisions":64,"max_questions_per_call":16,"chunking":"/decide/shared questions are packed greedily, in request order, into calls of at most 16 (1-16, 17-32, ...); each call is one prompt, so the questions in a call are asked together. With orderings, ordering k of every decision forms wave k, packed the same way.","workloads":[],"endpoints":["/decide","/decide/shared","/v1/systemone"],"systemone":{"max_questions":16,"chunking":"none","images":"not supported","max_tokens":7168}}
\ No newline at end of file
diff --git a/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-ledger.jsonl b/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-ledger.jsonl
new file mode 100644
index 0000000..267c071
--- /dev/null
+++ b/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-ledger.jsonl
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diff --git a/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-summary.json b/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-summary.json
new file mode 100644
index 0000000..6219a0b
--- /dev/null
+++ b/services/intern-decision-serve/acceptance/systemone-2026-09-30/jb-summary.json
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+  "probability_sources": [
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+          {
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+        ],
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+      "n_correct": 202,
+      "n_planned": 231,
+      "n_renormalized": 0,
+      "n_scorable": 231,
+      "n_valid": 231,
+      "operational_success": 1.0,
+      "ordinal_mae": 0.26234367203481096,
+      "price_per_1000_decisions_usd": 0.0,
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+  },
+  "version": "jevbench-v1"
+}
diff --git a/services/intern-decision-serve/intern-decision-serve.contract.md b/services/intern-decision-serve/intern-decision-serve.contract.md
index a13819e..8997f43 100644
--- a/services/intern-decision-serve/intern-decision-serve.contract.md
+++ b/services/intern-decision-serve/intern-decision-serve.contract.md
@@ -32,6 +32,7 @@ Every POST takes and returns JSON and needs `Authorization: Bearer <token>`. `GE
 | GET | `/health` | none | 200 `{status: "ok", model, vram_cap_gib, max_tokens, max_decisions, max_questions_per_call: 16, chunking, workloads: []}` |
 | POST | `/decide` | `{id, state, question, options[2..16], orderings?, workload?}` | 200 one decision result |
 | POST | `/decide/shared` | `{state, decisions: [{id, question, options, orderings?}], workload?}` | 200 `{results: [...], timing: {...}}`, results in request order |
+| POST | `/v1/systemone` | `{state, model?, questions: {"<id>": {type, instructions, criteria?}}, images? (rejected)}` | 200 the engine's own response (`answers`, `usage`, `model`) — see § POST /v1/systemone |
 
 `options` items are `{id, description}`. Validation is SemIf's, re-stated here because SemIf is
 gone. `id` and `question` are nonempty strings. `state` is a nonempty string, object or array,
@@ -39,6 +40,49 @@ and must be finite JSON. There are 2..16 options, each with a string `id` and a
 `description`, and option ids are unique within a decision. Decision ids are unique within a
 request. A violation is a 422.
 
+## POST /v1/systemone (Jev compatibility, added 0.1.1)
+
+The TypeSafe/Jev wire shape, served natively: the request goes **straight to the engine's own
+`DecisionEngine.predict`** — the checkpoint's documented Jev format — and never through the semif
+mapping below. The two surfaces use different prompts; routing one through the other would change
+the answers.
+
+```
+request  {"state": ..., "model": "<ignored>",
+          "questions": {"<id>": {"type": "noul"|"choice"|"score", "instructions": ..., "criteria": ...}},
+          "images": <rejected if present>}
+response {"answers": {"<id>": {"type": "noul",   "noul": p, ...}
+                             | {"type": "choice", "choice": label, "probabilities": {...}, ...}
+                             | {"type": "score",  "score": v,      "probabilities": {...}, ...}},
+          "usage": {...}, "model": {"name": ..., "revision": ...}}
+```
+
+- `model` is accepted with **any** value (Jev clients send `"jev-latest"`) and ignored. The
+  response `model` is a **string** naming the model actually serving and its pinned revision:
+  `"<name>@<revision>"` (JevBench's runner hashes the value into its manifest; a dict is not
+  hashable and broke its manifest step in the 0.1.1 first run).
+- The `answers` block is the engine's own response, unmodified: probabilities are the same
+  temperature-scaled numbers `/decide`'s `native` carries. `usage` is whatever the engine
+  reports (token counts); if it reports nothing, `{}`. Extra engine fields (`timing`,
+  `calibration`, `backend`) ride along — additive fields Jev clients ignore.
+- **Limits, all enforced before the forward pass:** 1..16 questions per request (NO chunking —
+  in Jev semantics the questions of one call share one prompt and chunking would silently
+  change that); 422 with a clear message above 16. `images` present → 422 `images not
+  supported` (the vision tower is dropped, INV-7). Over `MAX_TOKENS` → 422, the model's own
+  pre-forward check. The same bearer token (401), the same single inference thread, the same
+  `MAX_QUEUE` (429), `MAX_BODY_BYTES` (413), VRAM cap with 503 `out_of_memory` + recovery.
+- Per-question shape validation (type must be noul/choice/score; choice criteria an object;
+  score criteria a list/object; 1..16 options; nonempty state; unique field names) is the
+  engine's own `validate_request`; its `ValueError`s map to 422 `invalid_request` like anywhere
+  else. A `noul` question needs no `criteria`.
+- **What is NOT verified:** this covers the subset of TypeSafe's API that JevBench's `typesafe`
+  adapter exercises (231 public items, 4 repeats, bench 2026-09-30). Other Jev request features
+  (batching semantics beyond one prompt, streaming, price headers, `images` serving) are not
+  implemented and not verified against the closed API.
+- **Discovery:** `/health` lists the surface: `endpoints` names all three paths, and
+  `systemone` reports `{max_questions: 16, chunking: "none", images: "not supported",
+  max_tokens: <MAX_TOKENS>}`.
+
 ## Mapping onto the model (the seam)
 
 - **One call** is one `predict()` request:
diff --git a/services/intern-decision-serve/pyproject.toml b/services/intern-decision-serve/pyproject.toml
index 0f9e4b3..9797137 100644
--- a/services/intern-decision-serve/pyproject.toml
+++ b/services/intern-decision-serve/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "intern-decision-serve"
-version = "0.1.0"
+version = "0.1.1"
 description = "Intern-Decision-4B (its own inference.py) behind semif-serve's HTTP surface"
 requires-python = ">=3.12"
 dependencies = [
diff --git a/services/intern-decision-serve/src/intern_decision_serve/app.py b/services/intern-decision-serve/src/intern_decision_serve/app.py
index 64d58bf..5406679 100644
--- a/services/intern-decision-serve/src/intern_decision_serve/app.py
+++ b/services/intern-decision-serve/src/intern_decision_serve/app.py
@@ -68,6 +68,19 @@ class SharedBody(BaseModel):
     workload: str | None = None
 
 
+class SystemOneBody(BaseModel):
+    """The Jev request. `model` is accepted with any value and ignored (Jev clients send
+    'jev-latest'); `images` is kept only to refuse it with a clear 422. Extra keys are dropped:
+    only state+questions reach predict, exactly like the bench's 60-line wrapper."""
+    model_config = ConfigDict(extra="allow")
+    state: State
+    questions: dict[str, dict[str, Any]]
+
+    @property
+    def images_present(self) -> bool:
+        return "images" in (self.model_extra or {})
+
+
 class ApiError(Exception):
     def __init__(self, status: int, code: str, message: str):
         super().__init__(message)
@@ -322,10 +335,11 @@ def create_app(settings: Settings, engine: Any, executor: ThreadPoolExecutor | N
                   "inference_seconds": inference_ms / 1000}
         return out, timing
 
-    async def score(state: State, decisions: list[Decision], waves: list[list[Slot]]):
-        """Run one request's calls on the inference thread; map their failures to contract codes."""
+    async def on_inference(fn):
+        """Run fn on the ONE inference thread (INV-2) and map failures to contract codes (INV-4):
+        the shared dispatch+mapping for every surface, semif and systemone alike."""
         try:
-            return await asyncio.get_running_loop().run_in_executor(executor, run, state, decisions, waves)
+            return await asyncio.get_running_loop().run_in_executor(executor, fn)
         except ValueError as exc:            # the model's own validation, token limit, ...
             raise ApiError(422, "invalid_request", str(exc)) from exc
         except OutOfMemory as exc:
@@ -333,11 +347,17 @@ def create_app(settings: Settings, engine: Any, executor: ThreadPoolExecutor | N
         except Exception as exc:             # noqa: BLE001 — any other failure, building the response included
             raise ApiError(500, "scoring_failed", f"{type(exc).__name__}: {exc}") from exc
 
+    async def score(state: State, decisions: list[Decision], waves: list[list[Slot]]):
+        return await on_inference(lambda: run(state, decisions, waves))
+
     @app.get("/health")
     async def health():
         return {"status": "ok", "model": engine.health(), "vram_cap_gib": settings.vram_cap_gib,
                 "max_tokens": settings.max_tokens, "max_decisions": settings.max_decisions,
-                "max_questions_per_call": MAX_QUESTIONS_PER_CALL, "chunking": CHUNKING, "workloads": []}
+                "max_questions_per_call": MAX_QUESTIONS_PER_CALL, "chunking": CHUNKING, "workloads": [],
+                "endpoints": ["/decide", "/decide/shared", "/v1/systemone"],
+                "systemone": {"max_questions": MAX_QUESTIONS_PER_CALL, "chunking": "none",
+                              "images": "not supported", "max_tokens": settings.max_tokens}}
 
     @app.post("/decide")
     async def decide(request: Request):
@@ -363,4 +383,43 @@ def create_app(settings: Settings, engine: Any, executor: ThreadPoolExecutor | N
         finally:
             leave()
 
+    @app.post("/v1/systemone")
+    async def systemone(request: Request):
+        """Jev wire shape. Straight passthrough to DecisionEngine.predict — NEVER the semif
+        mapping (a different prompt would change the answers). Contract § POST /v1/systemone."""
+        admit()                          # before the body is read
+        try:
+            payload = await parse(request, SystemOneBody)
+            if payload.images_present:
+                raise ApiError(422, "invalid_request", "images not supported (the vision tower is "
+                                                       "removed to fit the GPU 1 budget, INV-7)")
+            questions = payload.questions
+            if not 1 <= len(questions) <= MAX_QUESTIONS_PER_CALL:
+                raise ApiError(422, "invalid_request",
+                               f"this request has {len(questions)} questions; the limit is "
+                               f"1..{MAX_QUESTIONS_PER_CALL} in one call — questions of one call "
+                               "share one prompt, so they are never chunked")
+            response = await run_systemone({"state": payload.state, "questions": questions})
+            meta = engine.metadata
+            return {"answers": response["answers"], "usage": response["usage"],
+                    "model": f"{meta.get('name')}@{meta.get('revision')}",   # string: see contract
+                    **{k: v for k, v in response.items() if k not in ("answers", "usage", "model")}}
+        finally:
+            leave()
+
+    async def run_systemone(call: dict) -> dict:
+        """One native call on the inference thread, under the same lock and mapping as the semif
+        surface (on_inference). The engine's own validate_request enforces the per-question Jev
+        rules before any GPU work; its ValueError is a 422 like the token-limit check."""
+        def one():
+            with inference:
+                response, _sha = engine.predict(call)
+            usage = response.get("usage")
+            response["usage"] = usage if isinstance(usage, dict) else {}   # contract: {} not absent
+            return response
+        response = await on_inference(one)
+        if not isinstance(response.get("answers"), dict):
+            raise ApiError(500, "scoring_failed", "the engine returned no answers object")
+        return response
+
     return app
diff --git a/services/intern-decision-serve/tests/fake_engine.py b/services/intern-decision-serve/tests/fake_engine.py
index 1119a06..232ebd7 100644
--- a/services/intern-decision-serve/tests/fake_engine.py
+++ b/services/intern-decision-serve/tests/fake_engine.py
@@ -22,6 +22,32 @@ def score_by_description(field: str, question: dict) -> list[float]:
     return [len(d) + (0.5 if i == 0 else 0.0) for i, d in enumerate(descs)]
 
 
+def answer_for(field: str, question: dict, scorer) -> dict:
+    """One Jev answer in the shape DecisionEngine.predict returns, for any of the three types."""
+    kind = question["type"]
+    if kind == "noul":
+        probs = {"yes": 0.75, "no": 0.25}
+        return {"type": "noul", "probabilities": probs, "confidence": 0.75, "noul": 0.75,
+                "source": "local", "decision": "yes"}
+    criteria = question["criteria"]
+    if isinstance(criteria, dict):
+        keys = list(criteria)
+        descriptions = list(criteria.values())
+    else:                                                   # score: a list of levels
+        keys = [str(i) for i in range(len(criteria))]
+        descriptions = [str(level) for level in criteria]
+    probs = dict(zip(keys, softmax(scorer(field, {**question, "criteria": dict(zip(keys, descriptions))}))))
+    best = min(keys, key=lambda k: (-probs[k], k))
+    answer = {"type": kind, "probabilities": probs, "confidence": probs[best], "source": "local", "decision": best}
+    if kind == "choice":
+        answer["choice"] = best
+    else:
+        answer["score"] = sum(float(k) * p for k, p in probs.items())
+        answer["legend"] = {k: (criteria[i] if isinstance(criteria, list) else v)
+                            for i, (k, v) in enumerate(zip(keys, criteria.values() if isinstance(criteria, dict) else criteria))}
+    return answer
+
+
 class FakeEngine:
     def __init__(self, scorer=score_by_description, tokens_per_question: int = 100):
         self.scorer = scorer
@@ -35,13 +61,8 @@ class FakeEngine:
 
     def predict(self, request: dict) -> tuple[dict, str]:
         self.calls.append(copy.deepcopy(request))
-        answers = {}
-        for field, question in request["questions"].items():
-            ids = list(question["criteria"])
-            probs = dict(zip(ids, softmax(self.scorer(field, question))))
-            best = min(ids, key=lambda i: (-probs[i], i))
-            answers[field] = {"type": "choice", "probabilities": probs, "confidence": probs[best],
-                              "choice": best, "source": "local", "decision": best}
+        answers = {field: answer_for(field, question, self.scorer)
+                   for field, question in request["questions"].items()}
         response = {"answers": answers,
                     "usage": {"input_tokens": self.tokens_per_question * len(answers),
                               "output_tokens": len(answers), "decision_count": len(answers)},
diff --git a/services/intern-decision-serve/tests/test_systemone.py b/services/intern-decision-serve/tests/test_systemone.py
new file mode 100644
index 0000000..c6fd373
--- /dev/null
+++ b/services/intern-decision-serve/tests/test_systemone.py
@@ -0,0 +1,224 @@
+"""POST /v1/systemone: Jev wire shape, straight passthrough to DecisionEngine.predict.
+Contract: intern-decision-serve.contract.md § POST /v1/systemone"""
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+import pytest
+from fastapi.testclient import TestClient
+
+from fake_engine import FakeEngine
+from intern_decision_serve.app import create_app
+from intern_decision_serve.config import Settings
+from intern_decision_serve.errors import OutOfMemory
+
+TOKEN = "t" * 40
+AUTH = {"Authorization": f"Bearer {TOKEN}"}
+
+CHOICE = {"type": "choice", "instructions": "Did it pass?",
+          "criteria": {"yes": "It passed.", "no": "It did not pass at all."}}
+NOUL = {"type": "noul", "instructions": "Is the deploy healthy?"}
+SCORE = {"type": "score", "instructions": "How risky?", "criteria": ["low", "medium", "high"]}
+
+
+def make_client(engine=None, **overrides):
+    return TestClient(create_app(Settings(api_token=TOKEN, **overrides), engine or FakeEngine()))
+
+
+def body(**over):
+    b = {"state": "The deploy finished at 14:00.", "model": "jev-latest", "questions": {"decision": CHOICE}}
+    b.update(over)
+    return b
+
+
+# ---------------------------------------------------------------- happy paths
+
+def test_choice_request_is_passed_straight_through_and_the_engine_answer_is_returned():
+    engine = FakeEngine()
+    response = make_client(engine).post("/v1/systemone", json=body(), headers=AUTH)
+    assert response.status_code == 200
+    native, _ = FakeEngine().predict(engine.calls[0])
+    # straight through: only state+questions reach predict, model is not forwarded, the question
+    # dict is untouched (no semif mapping: no A=id:description conversion, no field rename)
+    assert engine.calls == [{"state": "The deploy finished at 14:00.", "questions": {"decision": CHOICE}}]
+    out = response.json()
+    assert out["answers"] == native["answers"]
+    assert out["answers"]["decision"]["type"] == "choice"
+    assert out["answers"]["decision"]["choice"] in ("yes", "no")
+    assert out["usage"] == native["usage"]
+    # a STRING: JevBench's runner hashes the value into its manifest (a dict broke it on the
+    # 0.1.1 first run) and real clients compare it; the string names the real model and revision
+    assert out["model"] == "Intern-Decision-4B@" + "0" * 40
+
+
+def test_noul_question_needs_no_criteria_and_answers_with_a_probability():
+    engine = FakeEngine()
+    response = make_client(engine).post("/v1/systemone", json=body(questions={"q": NOUL}), headers=AUTH)
+    assert response.status_code == 200
+    assert engine.calls == [{"state": "The deploy finished at 14:00.", "questions": {"q": NOUL}}]
+    answer = response.json()["answers"]["q"]
+    assert answer["type"] == "noul" and answer["noul"] == 0.75
+
+
+def test_score_question_with_a_list_of_levels_returns_probabilities_over_the_levels():
+    engine = FakeEngine()
+    response = make_client(engine).post("/v1/systemone", json=body(questions={"q": SCORE}), headers=AUTH)
+    assert response.status_code == 200
+    answer = response.json()["answers"]["q"]
+    assert answer["type"] == "score"
+    assert set(answer["probabilities"]) == {"0", "1", "2"}
+    assert abs(sum(answer["probabilities"].values()) - 1.0) < 1e-9
+
+
+def test_any_number_of_questions_up_to_16_is_ONE_call_so_they_share_one_prompt():
+    engine = FakeEngine()
+    questions = {f"q{i}": NOUL for i in range(16)}
+    response = make_client(engine).post("/v1/systemone", json=body(questions=questions), headers=AUTH)
+    assert response.status_code == 200
+    assert len(engine.calls) == 1 and len(engine.calls[0]["questions"]) == 16
+
+
+def test_the_model_field_is_accepted_with_any_value_and_ignored():
+    engine = FakeEngine()
+    for model in ("jev-latest", "anything-else", 7, None):
+        response = make_client(engine).post("/v1/systemone", json=body(model=model), headers=AUTH)
+        assert response.status_code == 200
+
+
+class NoUsageEngine(FakeEngine):
+    def predict(self, request):
+        response, sha = super().predict(request)
+        response.pop("usage")
+        return response, sha
+
+
+def test_an_engine_that_reports_no_usage_yields_an_empty_usage_object():
+    response = make_client(NoUsageEngine()).post("/v1/systemone", json=body(), headers=AUTH)
+    assert response.status_code == 200
+    assert response.json()["usage"] == {}
+
+
+# ---------------------------------------------------------------- refusals
+
+@pytest.mark.parametrize("headers", [{}, {"Authorization": "Bearer wrong"}, {"Authorization": TOKEN}])
+def test_without_the_right_bearer_a_systemone_post_is_401_and_never_reaches_the_engine(headers):
+    engine = FakeEngine()
+    response = make_client(engine).post("/v1/systemone", json=body(), headers=headers)
+    assert response.status_code == 401
+    assert response.json()["error"]["code"] == "unauthorized"
+    assert engine.calls == []
+
+
+@pytest.mark.parametrize("images", [["a.png"], [], "a.png"])
+def test_images_any_value_at_all_is_422_before_the_engine_runs(images):
+    engine = FakeEngine()
+    response = make_client(engine).post("/v1/systemone", json=body(images=images), headers=AUTH)
+    assert response.status_code == 422
+    assert response.json()["error"]["code"] == "invalid_request"
+    assert "images not supported" in response.json()["error"]["message"]
+    assert engine.calls == []
+
+
+def test_17_questions_is_422_not_a_chunked_2_call_request():
+    engine = FakeEngine()
+    questions = {f"q{i}": NOUL for i in range(17)}
+    response = make_client(engine).post("/v1/systemone", json=body(questions=questions), headers=AUTH)
+    assert response.status_code == 422
+    assert "16" in response.json()["error"]["message"]
+    assert engine.calls == []
+
+
+def test_zero_questions_is_422():
+    response = make_client().post("/v1/systemone", json=body(questions={}), headers=AUTH)
+    assert response.status_code == 422
+
+
+def test_a_body_that_is_not_json_or_has_no_questions_is_422():
+    client = make_client()
+    assert client.post("/v1/systemone", content=b"{nope", headers={**AUTH, "content-type": "application/json"}
+                       ).status_code == 422
+    assert client.post("/v1/systemone", json={"state": "s"}, headers=AUTH).status_code == 422
+
+
+class TokenLimitEngine(FakeEngine):
+    """Stands in for the model's own pre-forward check: predict raises ValueError, never a tensor."""
+    def predict(self, request):
+        self.calls.append(request)
+        raise ValueError("Example has 7169 tokens, above 7168; truncation is forbidden")
+
+
+def test_over_max_tokens_is_422_from_the_models_own_before_the_forward_check():
+    response = make_client(TokenLimitEngine(), max_tokens=7168).post("/v1/systemone", json=body(), headers=AUTH)
+    assert response.status_code == 422
+    assert "7169" in response.json()["error"]["message"]
+
+
+# ---------------------------------------------------------------- queue and memory
+
+class SlowEngine(FakeEngine):
+    def __init__(self):
+        super().__init__()
+        self.entered, self.release = threading.Event(), threading.Event()
+
+    def predict(self, request):
+        self.entered.set()
+        assert self.release.wait(5)
+        return super().predict(request)
+
+
+def test_more_than_max_queue_systemone_posts_get_429_busy():
+    engine = SlowEngine()
+    with make_client(engine, max_queue=1) as client, ThreadPoolExecutor(2) as pool:
+        held = pool.submit(client.post, "/v1/systemone", json=body(), headers=AUTH)
+        assert engine.entered.wait(5)
+        time.sleep(0.2)
+        extra = client.post("/v1/systemone", json=body(), headers=AUTH)
+        assert extra.status_code == 429 and extra.json()["error"]["code"] == "busy"
+        engine.release.set()
+        assert held.result().status_code == 200
+
+
+class OomOnceEngine(FakeEngine):
+    def __init__(self):
+        super().__init__()
+        self.raised = False
+
+    def predict(self, request):
+        if not self.raised:
+            self.raised, _ = True, self.calls.append(request)
+            raise OutOfMemory("CUDA out of memory.")
+        return super().predict(request)
+
+
+def test_an_oom_is_503_with_the_contract_envelope_and_the_next_request_succeeds():
+    engine = OomOnceEngine()
+    client = make_client(engine)
+    first = client.post("/v1/systemone", json=body(), headers=AUTH)
+    assert first.status_code == 503 and first.json()["error"]["code"] == "out_of_memory"
+    second = client.post("/v1/systemone", json=body(), headers=AUTH)
+    assert second.status_code == 200
+
+
+def test_systemone_runs_on_the_same_single_inference_thread_as_decide():
+    seen = []
+
+    class ThreadSpy(FakeEngine):
+        def predict(self, request):
+            seen.append(threading.current_thread().name)
+            return super().predict(request)
+
+    client = make_client(ThreadSpy())
+    client.post("/v1/systemone", json=body(), headers=AUTH)
+    client.post("/decide", json={"id": "r", "state": "s", "question": "q?", "options":
+                                 [{"id": "a", "description": "x"}, {"id": "b", "description": "yy"}]},
+                headers=AUTH)
+    assert len(set(seen)) == 1 and seen[0].startswith("inference")
+
+
+# ---------------------------------------------------------------- /health
+
+def test_health_advertises_the_systemone_surface_and_its_limits():
+    out = make_client(max_tokens=7168).get("/health").json()
+    assert set(out["endpoints"]) == {"/decide", "/decide/shared", "/v1/systemone"}
+    assert out["systemone"] == {"max_questions": 16, "chunking": "none",
+                                "images": "not supported", "max_tokens": 7168}
diff --git a/services/intern-decision-serve/uv.lock b/services/intern-decision-serve/uv.lock
index 704eb94..104ebaa 100644
--- a/services/intern-decision-serve/uv.lock
+++ b/services/intern-decision-serve/uv.lock
@@ -284,7 +284,7 @@ wheels = [
 
 [[package]]
 name = "intern-decision-serve"
-version = "0.1.0"
+version = "0.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "fastapi" },