fix: retry failed model inspections and preserve rejection evidence

Do not cache transient repository-list failures as empty weight sets.
Deduplicate normalized file paths without lowering reported file sizes.
Record whitelisted OOM evidence and bounded discovery diagnostics.

modelhub_submmit_api/main.py

@@ -377,6 +377,7 @@ def choose_candidate_for_gpu_detailed(
     require_official_config: bool = False,
     config_optimizer: SafeConfigOptimizer | None = None,
     excluded_frameworks: set[str] | None = None,
+    rejection_details: list[dict[str, Any]] | None = None,
 ) -> tuple[CandidateModel | None, str | None]:
     last_rejection_reason: str | None = None
     for task_type in task_types:
@@ -473,6 +474,23 @@ def choose_candidate_for_gpu_detailed(
                 )
                 if not assessment.allowed:
                     last_rejection_reason = assessment.reason
+                    if rejection_details is not None:
+                        rejection_details.append({
+                            "taskType": task_type,
+                            "framework": framework,
+                            "reason": assessment.reason,
+                            "metadata": {
+                                key: assessment.metadata[key]
+                                for key in (
+                                    "modelType", "architectures", "quantizationMethod",
+                                    "estimatedLoadBytes", "repositoryOnDiskBytes",
+                                    "recursiveRepositoryBytes", "publishedSizeBytes",
+                                    "gpuMemoryGiB", "gpuMemoryEvidence",
+                                    "estimatedRequiredGiB", "memorySizingBasis",
+                                    "maximumRepositorySizeGiB",
+                                ) if key in assessment.metadata
+                            },
+                        })
                     continue
                 config_params = assessment.config_params
                 warnings.extend(assessment.warnings)
@@ -651,6 +669,14 @@ def process_model_for_candidates(
                         "repoId": model.repo_id,
                         "targetGpu": target_gpu,
                         "reason": "preflight_predicted_oom",
+                        "preflightDetails": {
+                            "stage": "published_size_precheck",
+                            "publishedSizeBytes": model.file_size,
+                            "estimatedRequiredGiB": model.file_size / (1024**3) * MODEL_LOAD_OVERHEAD,
+                            "gpuMemoryGiB": memory_gib,
+                            "gpuMemoryEvidence": preflight_advisor.gpu_memory_evidence.get(target_gpu),
+                            "loadOverhead": MODEL_LOAD_OVERHEAD,
+                        },
                     })
                     continue
 
@@ -717,6 +743,7 @@ def process_model_for_candidates(
         for target_gpu, task_types, excluded_frameworks in pending_routes:
             if len(candidates) - before_count >= MAX_ROUTES_PER_MODEL:
                 break
+            rejection_details: list[dict[str, Any]] = []
             best, preflight_reason = choose_candidate_for_gpu_detailed(
                 model=model,
                 inspection=inspection,
@@ -728,6 +755,7 @@ def process_model_for_candidates(
                 require_official_config=official_registry is not None,
                 config_optimizer=config_optimizer,
                 excluded_frameworks=excluded_frameworks,
+                rejection_details=rejection_details,
             )
             if best is None:
                 reason = (
@@ -738,7 +766,10 @@ def process_model_for_candidates(
                         else "no_compatible_auto_template_or_framework"
                     )
                 )
-                skipped.append({"repoId": model.repo_id, "targetGpu": target_gpu, "reason": reason})
+                skipped.append({
+                    "repoId": model.repo_id, "targetGpu": target_gpu, "reason": reason,
+                    **({"preflightRejections": rejection_details} if rejection_details else {}),
+                })
                 continue
             record = candidate_to_record(best)
             if excluded_frameworks:
@@ -1631,6 +1662,7 @@ def run_submission(
             "candidateModelsAfterStage": candidate_model_count(candidates),
             "skippedAdded": len(skipped) - skipped_before_stage,
             "failedAdded": len(stage_failed),
+            "discoveryQueries": list(getattr(hf_discovery, "last_listing_diagnostics", [])),
         }
         scan_stages.append(stage_summary)
         print(