fix: reject broken configs and improve accuracy feedback with isolated agent state

modelhub_submmit_api/routing_engine.py

@@ -46,6 +46,13 @@ class SuccessFirstRoutingEngine:
         return value
 
     def _evidence(self, candidate: dict[str, Any]) -> tuple[str, int, int, float]:
+        def observed_total(item: dict[str, Any]) -> int:
+            if "failureCount" in item:
+                return max(0, int(item.get("successCount") or 0) + int(item.get("failureCount") or 0)
+                           - int(item.get("platformFailureCount") or 0))
+            return max(0, int(item.get("decisionTotal") or 0))
+
+        sparse = None
         profile = candidate.get("preflightMetadata") or {}
         gpu = str(candidate.get("targetGpu") or "")
         framework = str(candidate.get("framework") or "")
@@ -60,14 +67,18 @@ class SuccessFirstRoutingEngine:
             size_bucket = int(load_bytes).bit_length() - 1
             key = f"{gpu}|{framework}|{task_type}|{model_type}|{quantization}|{size_bucket}"
             item = (self.outcome_stats.get("sizedProfileCombinationStats") or {}).get(key) or {}
-            total = int(item.get("decisionTotal") or 0)
+            total = observed_total(item)
+            if total:
+                sparse = ("local_profile_size_shrunk", item, total)
             if total >= 20:
                 success = int(item.get("successCount") or 0)
                 return "local_profile_size", success, total, wilson_lower_bound(success, total)
         if model_type:
             key = f"{gpu}|{framework}|{task_type}|{model_type}|{quantization}"
             item = (self.outcome_stats.get("profileCombinationStats") or {}).get(key) or {}
-            total = int(item.get("decisionTotal") or 0)
+            total = observed_total(item)
+            if total and sparse is None:
+                sparse = ("local_profile_shrunk", item, total)
             if total >= 40:
                 success = int(item.get("successCount") or 0)
                 return "local_profile", success, total, wilson_lower_bound(success, total)
@@ -97,6 +108,13 @@ class SuccessFirstRoutingEngine:
             total = 2
             success = 1
             level = "global_conservative_prior"
+        if sparse is not None:
+            level, item, samples = sparse
+            # Eight prior observations stabilize small groups without hiding
+            # their failures behind an unreachable hard sample threshold.
+            prior_success = round(8 * success / max(1, total))
+            success = int(item.get("successCount") or 0) + prior_success
+            total = samples + 8
         return level, success, total, wilson_lower_bound(success, total)
 
     def annotate(self, candidate: dict[str, Any]) -> dict[str, Any]: