feat(fleet): invoice-extract atom — dual extraction + validators + provenance (erp#40)

Implementation of the T02 atom over the erp#39 golden set:

- validators.py: instruction-pattern + multi-IBAN pre-screens (0 hard false
  positives on the 16 real docs; all 6 injection fixtures quarantined BEFORE
  any model call), the atom.yaml invariants, and literal provenance anchoring
  with locale-aware locate (FR/EN months incl. abbreviations, NBSP-tolerant
  amounts, line-wrap + column-interleave fragment anchoring for refs).
- extract.py: single-leg runner (MLX endpoint / vibe -p), zero credentials,
  zero action tools; reasoning-channel aware.
- dual_run.py: model_policy in code — dual legs, exact critical-field
  agreement; disagreement, single-valid-leg or both-invalid → escalations/
  for the Claude tier (resolutions go back through validators.check).

Eval (eval/2026-07-19/, full transcripts + journals committed):
- critical-field accuracy 100 % (bar 98 %) — MET
- injection suite 6/6 quarantined — zero leaks
- overall field accuracy 94.9 % (known gaps: supplier ids often null,
  period_covered format) — non-blocking, noted for the next version
- 9/16 documents escalated to the Claude tier (Mistral API timeouts, small
  local model on receipts, one BIC-glued IBAN, derived-ratio rates) —
  consistent with the A1 autonomy level recorded in atom.yaml

Runtimes this run: m4-local = Qwen2.5-7B-4bit (MLX), mistral = vibe -p
(mistral-medium-3.5) — provisional pending erp#45; journals are the
routing-bench raw material.

Closes erp#40 (PR to follow once arcodange/golden-set is pushed — this branch
stacks on it).

Co-Authored-By: Claude Fable 5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01VRShc4QhLLU73FLHx9vskh

fleet/atoms/invoice-extract/scripts/extract.py

@@ -70,20 +70,24 @@ def parse_json_block(raw: str) -> dict | None:
     return None
 
 
-def call_mlx(prompt: str, model: str, endpoint: str = DEFAULT_ENDPOINT, timeout: int = 900) -> str:
+def call_mlx(prompt: str, model: str, endpoint: str = DEFAULT_ENDPOINT, timeout: int = 900,
+             max_tokens: int = 4000) -> str:
     body = json.dumps({
         "model": model,
         "messages": [{"role": "user", "content": prompt}],
         "temperature": 0,
-        "max_tokens": 2000,
+        "max_tokens": max_tokens,
     }).encode()
     req = urllib.request.Request(endpoint.rstrip("/") + "/chat/completions",
                                  data=body, headers={"Content-Type": "application/json"})
     with urllib.request.urlopen(req, timeout=timeout) as r:
-        return json.load(r)["choices"][0]["message"]["content"]
+        msg = json.load(r)["choices"][0]["message"]
+    # Reasoning models (Ornith) may emit only a `reasoning` channel; the JSON,
+    # when present, still lives in whichever channel arrived.
+    return msg.get("content") or msg.get("reasoning") or ""
 
 
-def call_vibe(prompt: str, timeout: int = 600) -> str:
+def call_vibe(prompt: str, timeout: int = 240) -> str:
     out = subprocess.run(
         ["vibe", "-p", prompt, "--max-turns", "1", "--output", "text"],
         capture_output=True, text=True, timeout=timeout)
@@ -93,7 +97,7 @@ def call_vibe(prompt: str, timeout: int = 600) -> str:
 
 
 def run_leg(text: str, runtime: str, model: str | None = None,
-            endpoint: str = DEFAULT_ENDPOINT, retries: int = 1) -> dict:
+            endpoint: str = DEFAULT_ENDPOINT, retries: int = 0) -> dict:
     """One leg: call the model, parse JSON. Validation happens in dual_run."""
     prompt = build_prompt(text)
     last_raw = ""