fix(05-inference): min_frames guard + configurable timeout

- Skip segments with < min_frames_for_inference (32) frames — prevents
  RoPE/attention tensor mismatch (GX029838: 20 frames)
- Timeout now reads inference_timeout_s from thresholds.yaml (default 3h)
  GX029818 (493 frames) timed out at 7200s — raised to 10800

Authored-by: Poulpe <claude@nowyouknow.fr>

pipeline/stages/05_inference.py

@@ -195,9 +195,10 @@ def run_inference(frames_dir: Path, worker_key: str, mission_name: str,
 
     print(f"  [05] Launching inference on {host}...")
     t0 = time.time()
+    inf_timeout = int(_INF_CFG.get("inference_timeout_s", 10800))
     r = subprocess.run(
         ["ssh", "-o", "StrictHostKeyChecking=no", ssh_target, demo_cmd],
-        capture_output=True, text=True, timeout=7200,  # 2h max
+        capture_output=True, text=True, timeout=inf_timeout,
     )
     elapsed = time.time() - t0
     metrics["inference_s"] = round(elapsed, 1)
@@ -265,6 +266,19 @@ def process_frames_dir(frames_dir: Path, worker_key: str, mission_name: str) ->
             if not frames:
                 continue
             print(f"\n[05] === {auv_id}/{seg_dir.name}: {len(frames)} frames ===")
+            # Guard: min frames required for model (RoPE/attention)
+            min_frames = int(_INF_CFG.get("min_frames_for_inference", 32))
+            if len(frames) < min_frames:
+                print(f"  [05] SKIP {auv_id}/{seg_dir.name}: {len(frames)} frames < {min_frames} min")
+                init_db()
+                with get_conn() as conn_mf:
+                    mr = conn_mf.execute("SELECT id FROM missions WHERE name=?", (mission_name,)).fetchone()
+                    if mr:
+                        upsert_job(conn_mf, mr["id"], auv_id, seg_dir.name, "05_inference",
+                                   status="skipped",
+                                   error_msg=f"frames_too_few={len(frames)}<{min_frames}")
+                continue
+
             m = run_inference(seg_dir, worker_key, mission_name, auv_id, seg_dir.name)
             all_metrics.append(m)