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.

README.md

@@ -107,8 +107,10 @@ model/GPU uniqueness response, the entire submission cycle pauses fail-closed.
 For each compatible model/GPU pair, the worker also ranks the GPU's supported
 frameworks using ModelHub's public aggregate `modelCount` and `successCount` data,
 then blends in the worker's own GPU+framework outcomes with a capped weight.
-Only frameworks with at least 300 public samples and a safe Wilson lower bound
-are eligible. Framework statistics refresh
+Only frameworks with at least 300 public samples and a Wilson lower bound of at
+least 20% are eligible. GPU pools normally require a 15% lower bound; a
+low-sample pool can remain available only when it has a recent success and a
+framework that passes the stricter framework gate. Framework statistics refresh
 every 6 hours, so they do not add per-model API traffic. Newly published
 frameworks are discovered automatically, but receive no novelty bonus: they can
 win only when their confidence-adjusted success score beats the best incumbent by
@@ -124,7 +126,10 @@ evidence on every scan; accepted route counters and lifetime sufficient statisti
 are stored in `.modelhub_state/routing_intelligence.json`.
 
 Five consecutive local failures open a 12-hour GPU/framework circuit breaker.
-A sub-20% success rate over the latest 20 terminal tasks opens a 6-hour breaker.
+Five or more recent terminal results below 10% success open the same breaker,
+including ambiguous runtime failures that are unsafe to keep feeding. A sub-20%
+attributable success rate over the latest 20 terminal tasks opens a 6-hour
+breaker.
 Platform/infrastructure failures are excluded from long-term compatibility rates
 and model/profile breakers. Three consecutive platform failures on a GPU/framework
 instead open a short 30-minute breaker, so a temporary broken runner or lack of an
@@ -296,11 +301,43 @@ model/GPU combinations, the same run progressively expands discovery to the last
 7 days, the last 30 days, and finally older history (up to 3,000 models). Scanning
 stops as soon as enough replacement candidates have been found.
 
+When only recent-model reserved positions remain, discovery also deepens the
+same recent window up to the configured scan cap (3,000 by default). It never
+admits older models into those positions or lowers the quality gates. This avoids
+rechecking only an exhausted short prefix on every poll.
+
+An ordinary account-capacity rejection temporarily marks that account full and
+tries the next account. It does not lower the persisted capacity using a cached
+active count: another worker may have filled the queue since that count was
+read. Downward capacity learning remains confined to weekly calibration.
+
+Account allocation is age-aware: recent models first use accounts with only
+reserved positions left, then the account with the fewest shared positions
+remaining. Old models continue to use the account with the most eligible space.
+Largest-headroom and rotating tie-breaks balance otherwise equivalent accounts;
+weekly calibration remains pinned to its target account. For example, with
+accounts at 94/100 and 95/100 and five recent-only positions each, assigning a
+recent model to the second account preserves the first account's last old-model
+position. A recent-then-old pair can both be accepted rather than stranding the
+old model after the recent model takes its only eligible position.
+
 Model verification results are reused for 15 minutes across poll cycles, and a
 locally failed model/GPU pair cools down for 24 hours instead of being excluded
 forever. The `[scan]` lines show every expansion stage, while `[daily] wave_done`
 includes `skip_reasons` so an empty candidate pool is directly diagnosable.
 
+Repository-tree HTTP failures are reported as retriable inspection failures,
+not cached as empty repositories. Repeated normalized file paths count once
+(using the largest reported size for that path), while different weight formats
+still contribute separately to the full-repository OOM gate.
+
+For an empty-candidate investigation, read `daily_runs/<cycle>/summary.json`,
+then follow `waveResults[].summary.runDir` to `skipped.jsonl` and `failed.jsonl`.
+OOM skips now include size/capacity/source evidence, and `scanStages` includes
+`discoveryQueries` with raw rows, accepted rows, cache hits, and stop reasons.
+Reaching a page/result budget does not mean the catalog is exhausted. These
+diagnostics contain selected fields only, never full configs or HTTP headers.
+
 Community deduplication is scoped to the exact model/GPU combination. A model
 adapted on one GPU remains eligible for another GPU. Before each submission the
 worker performs an uncached community check; a lookup failure defers the task
@@ -481,6 +518,18 @@ calibration each Monday 10:00-10:59 Asia/Shanghai. During that window only,
 the worker may borrow the five recent-model positions to measure a new limit;
 ordinary capacity-minus-five admission resumes afterward.
 
+Version `2026.09.22.1` restores accuracy-first admission while retaining bounded
+capacity recovery. Any existing result for an exact model/GPU pair, including a
+failed result, closes that pair; a different framework no longer reopens it.
+Each model is limited to six GPU routes across poll cycles and at most two local
+routes may await feedback simultaneously. Lower-ranked GPUs are still checked,
+in score order, only when the preferred six cannot produce a safe candidate.
+Download-ranked popularity fallback is removed: discovery expands by update
+window instead. Public framework/GPU confidence floors and an all-terminal
+failure circuit prevent high-volume low-quality routes from winning on relative
+rank alone. Community-confirmed model successes are used only as a tie-breaker
+inside the same confidence band.
+
 ## Deploy
 
 Create a tag and submit the repository URL plus tag in "我的适配智能体".

modelhub_submmit_api/README.md

@@ -79,21 +79,25 @@ bash run_poll.sh --dry-run
 ## Behavior
 
 - The runner auto-discovers all safe GPU/template combinations from the public submit catalog.
-- Automatic GPU selection uses exact 70/30 accepted-task scheduling: long-term
-  Wilson-ranked top 3 GPUs and the top GPUs from the latest 1,000 terminal tasks.
-  There is no all-GPU exploration category.
-- Within each category, weighted-fair scheduling uses estimated queue backlog hours,
+- Automatic GPU selection uses one success-first score built from qualified
+  local cohorts and ModelHub public evidence. There is no random or fixed-quota
+  exploration category.
+- Within the qualified success band, scheduling uses estimated queue backlog hours,
   recent public throughput/success, machine availability, and worker concurrency.
   Unavailable or stalled GPU pools are circuit-broken instead of continuing to absorb work.
 - Compatible frameworks are ranked by ModelHub public aggregate success statistics
   plus capped local GPU+framework evidence, with a 300-sample public minimum and
-  Wilson confidence bounds. Missing or undersized public evidence receives zero
-  traffic rather than falling back to exploration.
+  a 20% Wilson lower-bound floor. GPU pools normally require a 15% floor.
+  Missing or undersized public evidence receives zero traffic rather than
+  falling back to exploration; a low-sample GPU requires a recent success and a
+  framework that already passes the stricter framework gate.
 - New frameworks are discovered from the live catalog but get no novelty bonus.
   They are eligible only with a complete official build config that passes local
   validation and a confidence score at least 10% above the best incumbent.
-- Five consecutive local failures pause a GPU/framework pair for 12 hours; a
-  sub-20% rate over the latest 20 terminal tasks pauses it for 6 hours.
+- Five consecutive local failures pause a GPU/framework pair for 12 hours. Five
+  or more recent terminal outcomes below 10% success also pause it for 12 hours,
+  including ambiguous runtime failures. A sub-20% attributable rate over the
+  latest 20 terminal tasks pauses it for 6 hours.
 - An explicit "framework does not support this model/architecture" failure learns
   a 30-day GPU + framework + task + architecture block. Architecture identity
   comes from candidate `config.json` plus exact unsupported `model_type` or
@@ -114,20 +118,22 @@ bash run_poll.sh --dry-run
   duplicates do not advance it. The next cycle refreshes platform history before submitting again.
 - Strategy state is stored in `.modelhub_state/gpu_strategy.json`; a generation never recalculates
   during candidate submission.
-- Candidate discovery starts with the configured recent window, then automatically expands to
-  7 days, 30 days, and the first 3,000 raw ModelScope rows ordered by update time. If the
-  candidate pool is still short, it rotates through up to 200 download-ranked rows per cycle
-  as an older-model fallback.
+- Candidate discovery starts with the configured recent window, then
+  automatically expands to 7 days, 30 days, and the first 3,000 raw ModelScope
+  rows ordered by update time. Download popularity is not treated as ModelHub
+  compatibility evidence.
 - Model verification responses are cached across poll cycles for 15 minutes. Local model/GPU
   failures cool down after 24 hours instead of remaining permanently blocked.
-- Community deduplication is model/GPU-specific: another GPU's adaptation does not block the
-  current GPU. `未验证` with no records is eligible. `验证失败` is eligible only with a
-  different publicly vetted framework after all recorded attempts explicitly failed;
-  `已验证`, `验证中`, and ambiguous records remain blocked. Every actual submission
-  performs a fresh uncached check for its exact GPU and framework.
+- Community deduplication is model/GPU-specific: another GPU's adaptation does
+  not block the current GPU. Only `未验证` with no records is eligible;
+  `验证失败`, `已验证`, `验证中`, and ambiguous records remain blocked. Every actual
+  submission performs a fresh uncached check for its exact GPU.
 - If the community lookup is unavailable, submission is deferred. A platform model-uniqueness
   rejection permanently excludes only that model/GPU combination from future local retries.
-- The same framework is not retried for a model/GPU after a recorded failure.
+- A model may use at most six GPU routes across poll cycles, and at most two
+  locally submitted routes for that model may await feedback at once. The first
+  six ranked GPUs remain preferred; lower-ranked eligible GPUs are inspected in
+  order only when that tranche yields no safe candidate.
 - Multiple ModelHub tokens are pooled and used to route submissions to the account with available async capacity.
 - Concurrent submissions reserve account slots locally, and an account-capacity race automatically falls through to another account.
 - Concurrent local processes claim model/GPU pairs in `.modelhub_state/submission_claims.jsonl`; shared ledger, history, and outcome files use process locks and atomic replacement.

modelhub_submmit_api/candidate_preflight.py

@@ -191,6 +191,8 @@ class CandidatePreflightAdvisor:
             "quantizationMethod": inspection.quantization_method,
             "estimatedLoadBytes": inspection.estimated_load_bytes(framework),
             "repositoryOnDiskBytes": inspection.repository_size_bytes,
+            "recursiveRepositoryBytes": inspection.exact_repository_size_bytes,
+            "publishedSizeBytes": inspection.published_size_bytes,
             "gpuMemoryGiB": self.gpu_memory_gib.get(target_gpu),
             "gpuMemoryEvidence": self.gpu_memory_evidence.get(target_gpu),
         }
@@ -272,7 +274,9 @@ class CandidatePreflightAdvisor:
             required_gib = memory_sizing_bytes / (1024**3) * MODEL_LOAD_OVERHEAD
             metadata["estimatedRequiredGiB"] = round(required_gib, 3)
             metadata["memorySizingBasis"] = (
-                "recursive_repository_on_disk" if repository_bytes else "selected_weights_fallback"
+                "published_repository_upper_bound"
+                if (inspection.published_size_bytes or 0) > (inspection.exact_repository_size_bytes or 0)
+                else ("recursive_repository_on_disk" if repository_bytes else "selected_weights_fallback")
             )
             metadata["maximumRepositorySizeGiB"] = round(memory_gib / MODEL_LOAD_OVERHEAD, 3)
             if required_gib > memory_gib:

modelhub_submmit_api/hf_discovery.py

@@ -110,6 +110,7 @@ class HuggingFaceDiscovery:
         self._model_card_lock = threading.Lock()
         self._popular_page_lock = threading.Lock()
         self._popular_page_cursor = 0
+        self.last_listing_diagnostics: list[dict[str, Any]] = []
 
     @staticmethod
     def _remember_lru(cache: OrderedDict, key: Any, value: Any, limit: int) -> None:
@@ -133,6 +134,7 @@ class HuggingFaceDiscovery:
             return []
 
         deduped: dict[str, HFModelSummary] = {}
+        self.last_listing_diagnostics = []
         del read_concurrency
 
         per_tag_limit = limit if len(pipeline_tags) <= 1 else max(10, (limit + len(pipeline_tags) - 1) // len(pipeline_tags))
@@ -156,6 +158,7 @@ class HuggingFaceDiscovery:
                 updated_after=updated_after,
                 sort=sort,
                 start_page=start_page,
+                diagnostics=self.last_listing_diagnostics,
             ):
                 current = deduped.get(model.repo_id)
                 if current is None or (model.last_modified or parse_datetime("1970-01-01")) > (
@@ -175,6 +178,7 @@ class HuggingFaceDiscovery:
         updated_after=None,
         sort: str = "last_modified",
         start_page: int = 1,
+        diagnostics: list[dict[str, Any]] | None = None,
     ) -> list[HFModelSummary]:
         # ModelScope OpenAPI rejects values above 50 with InputParameterError.
         page_size = min(max(1, limit), 50)
@@ -183,12 +187,23 @@ class HuggingFaceDiscovery:
         filter_disabled = self._unsupported_task_filters.get(task_tag, 0.0) > time.monotonic()
         models: list[HFModelSummary] = []
         page_count = (max_items + page_size - 1) // page_size
+        diagnostic = {
+            "pipelineTag": pipeline_tag, "sort": sort,
+            "requestedLimit": limit, "effectiveLimit": max_items,
+            "pageSize": page_size, "startPage": start_page,
+            "pagesRead": 0, "cachedPages": 0, "rawRowsScanned": 0,
+            "acceptedModels": 0, "stopReason": "page_budget",
+            "catalogExhausted": False,
+        }
+        if diagnostics is not None:
+            diagnostics.append(diagnostic)
         for page_number in range(start_page, min(61, start_page + page_count)):
             cache_key = (sort, "*" if filter_disabled else task_tag, page_number, page_size)
             cached = self._model_page_cache.get(cache_key)
             if cached is not None and time.monotonic() - cached[0] < self._model_page_cache_ttl:
                 self._model_page_cache.move_to_end(cache_key)
                 items = list(cached[1])
+                diagnostic["cachedPages"] += 1
             else:
                 elapsed = time.monotonic() - self._last_model_page_request_at
                 if self._last_model_page_request_at > 0 and elapsed < self._page_interval_seconds:
@@ -211,6 +226,7 @@ class HuggingFaceDiscovery:
                     self._last_model_page_request_at = time.monotonic()
                     if exc.status_code == 400 and not filter_disabled:
                         self._unsupported_task_filters[task_tag] = time.monotonic() + 86_400
+                        diagnostic["stopReason"] = "unsupported_filter_retry"
                         print(
                             f"[modelscope] task_filter_unsupported task={task_tag} "
                             "fallback=unfiltered ttl=86400s",
@@ -223,12 +239,15 @@ class HuggingFaceDiscovery:
                             updated_after=updated_after,
                             sort=sort,
                             start_page=start_page,
+                            diagnostics=diagnostics,
                         )
                     print(
                         f"[modelscope] list_models_error task={task_tag} page={page_number} "
                         f"partial_models={len(models)} retry_next_cycle=true error={exc}",
                         flush=True,
                     )
+                    diagnostic["stopReason"] = "http_error"
+                    diagnostic["httpStatus"] = exc.status_code
                     break
 
                 items = self._extract_models(payload)
@@ -238,7 +257,11 @@ class HuggingFaceDiscovery:
                     (time.monotonic(), list(items)),
                     self._page_cache_limit,
                 )
+            diagnostic["pagesRead"] += 1
+            diagnostic["rawRowsScanned"] += len(items)
             if not items:
+                diagnostic["stopReason"] = "empty_page"
+                diagnostic["catalogExhausted"] = True
                 break
             for item in items:
                 model = self._parse_model(
@@ -249,9 +272,13 @@ class HuggingFaceDiscovery:
                 )
                 if model is not None:
                     models.append(model)
+                    diagnostic["acceptedModels"] += 1
                 if len(models) >= max_items:
+                    diagnostic["stopReason"] = "result_limit"
                     return models
             if len(items) < page_size:
+                diagnostic["stopReason"] = "short_page"
+                diagnostic["catalogExhausted"] = True
                 break
         return models
 
@@ -412,7 +439,10 @@ class HuggingFaceDiscovery:
             )
         except HttpJsonError as exc:
             print(f"[modelscope] repo_tree_error repo={repo_id} error={exc}", flush=True)
-            payload = None
+            # A failed lookup is not an empty repository. Let the caller defer
+            # inspection and retry next cycle instead of caching missing
+            # weights as a permanent framework-incompatibility decision.
+            raise
 
         entries = self._extract_files(payload)
 
@@ -570,7 +600,7 @@ def inspect_repo_tree(repo_id: str, entries: list[dict[str, Any]]) -> ModelInspe
                 break
         if size_present:
             try:
-                file_sizes[path] = max(0, int(size_value))
+                file_sizes[path] = max(file_sizes.get(path, 0), int(size_value))
             except (TypeError, ValueError):
                 pass
         filename = PurePosixPath(path).name.lower()
@@ -584,12 +614,12 @@ def inspect_repo_tree(repo_id: str, entries: list[dict[str, Any]]) -> ModelInspe
     selected_gguf = choose_best_gguf(gguf_files)
     return ModelInspection(
         repo_id=repo_id,
-        file_paths=sorted(file_paths),
+        file_paths=sorted(set(file_paths)),
         file_sizes=file_sizes,
-        gguf_files=sorted(gguf_files),
+        gguf_files=sorted(set(gguf_files)),
         selected_gguf=PurePosixPath(selected_gguf).name if selected_gguf else None,
-        weight_files=sorted(vllm_weight_files),
-        onnx_files=sorted(onnx_files),
+        weight_files=sorted(set(vllm_weight_files)),
+        onnx_files=sorted(set(onnx_files)),
     )
 
 

modelhub_submmit_api/main.py

@@ -66,6 +66,7 @@ DEFAULT_LEDGER_PATH = Path("ledger/submissions.jsonl")
 ADAPTIVE_SCAN_MAX_MODELS = 3000
 ADAPTIVE_SCAN_MIN_FALLBACK_MODELS = 500
 MAX_ROUTES_PER_MODEL = 6
+MAX_PENDING_ROUTES_PER_MODEL = 2
 
 
 def build_parser() -> argparse.ArgumentParser:
@@ -376,6 +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:
@@ -472,6 +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)
@@ -572,9 +591,11 @@ def process_model_for_candidates(
         if hasattr(modelhub_client, "model_submission_precheck"):
             precheck = modelhub_client.model_submission_precheck(model.repo_id)
             processed_gpus = set(precheck.get("processedGpus") or set())
+            successful_gpus = set(precheck.get("successfulGpus") or set())
             failed_frameworks_by_gpu = precheck.get("failedFrameworksByGpu") or {}
         else:
             processed_gpus = modelhub_client.processed_gpus_for_model(model.repo_id)
+            successful_gpus = set()
             failed_frameworks_by_gpu = {}
     except Exception as exc:
         return (
@@ -582,6 +603,31 @@ def process_model_for_candidates(
             [{"repoId": model.repo_id, "reason": "community_precheck_unavailable_fail_closed"}],
             [{"repoId": model.repo_id, "reason": f"community_precheck_error:{exc}"}],
         )
+    if len(processed_gpus) >= MAX_ROUTES_PER_MODEL:
+        return (
+            [],
+            [{
+                "repoId": model.repo_id,
+                "reason": "model_route_limit_reached",
+                "processedGpuCount": len(processed_gpus),
+            }],
+            [],
+        )
+    pending_route_count = (
+        outcome_tracker.pending_route_count_for_model(model.repo_id)
+        if outcome_tracker is not None
+        else 0
+    )
+    if pending_route_count >= MAX_PENDING_ROUTES_PER_MODEL:
+        return (
+            [],
+            [{
+                "repoId": model.repo_id,
+                "reason": "model_pending_feedback_limit",
+                "pendingRouteCount": pending_route_count,
+            }],
+            [],
+        )
     candidates: list[dict[str, Any]] = []
     skipped: list[dict[str, Any]] = []
     failed: list[dict[str, Any]] = []
@@ -605,33 +651,14 @@ def process_model_for_candidates(
             if target_gpu in processed_gpus:
                 skipped.append({"repoId": model.repo_id, "targetGpu": target_gpu, "reason": "already_processed_for_gpu"})
                 continue
-            if target_gpu in failed_frameworks_by_gpu and market_intelligence is None:
+            if target_gpu in failed_frameworks_by_gpu:
                 skipped.append({
                     "repoId": model.repo_id,
                     "targetGpu": target_gpu,
-                    "reason": "prior_failed_gpu_requires_public_framework_evidence",
+                    "reason": "prior_failed_gpu_closed_accuracy_first",
                 })
                 continue
             failed_frameworks = set(failed_frameworks_by_gpu.get(target_gpu) or set())
-            if (
-                failed_frameworks
-                and market_intelligence is not None
-                and hasattr(market_intelligence, "has_vetted_alternative_framework")
-                and allowed_task_types_set
-                and all(market_intelligence.has_framework_route(task_type, target_gpu) for task_type in allowed_task_types_set)
-                and not any(
-                    market_intelligence.has_vetted_alternative_framework(
-                        task_type, target_gpu, failed_frameworks
-                    )
-                    for task_type in allowed_task_types_set
-                )
-            ):
-                skipped.append({
-                    "repoId": model.repo_id,
-                    "targetGpu": target_gpu,
-                    "reason": "prior_failed_framework_no_alternative",
-                })
-                continue
             if outcome_tracker and outcome_tracker.is_model_gpu_failed(model.repo_id, target_gpu):
                 skipped.append({"repoId": model.repo_id, "targetGpu": target_gpu, "reason": "local_failure_cooldown_24h"})
                 continue
@@ -642,6 +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
 
@@ -708,6 +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,
@@ -719,6 +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 = (
@@ -729,11 +766,17 @@ 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:
                 record["previousFailedFrameworks"] = sorted(excluded_frameworks)
+            record["communitySuccessfulGpuCount"] = len(successful_gpus)
+            if successful_gpus:
+                record["communitySuccessfulGpus"] = sorted(successful_gpus)
             if outcome_tracker is not None:
                 record["transformersPrerequisiteSatisfied"] = (
                     outcome_tracker.has_non_transformers_failure(model.repo_id)
@@ -810,20 +853,17 @@ def build_adaptive_scan_stages(
                 "limit": max_models,
             }
         )
-    if allow_older_than_recent_window:
-        # ModelScope only exposes the first 3,000 raw rows for a sort order.
-        # Downloads ordering reaches a different older, established slice
-        # after the recent-update slice has produced too few candidates.
+    if not allow_older_than_recent_window:
+        # Reserved positions still need a deep search of eligible recent
+        # models. Widening the date window alone repeatedly scans the same
+        # short prefix (often already processed) on every poll cycle.
         stages.append(
             {
-                "name": "popular_history",
-                "updatedAfter": None,
-                "limit": min(max_models, 200),
-                "sort": "downloads",
-                "rotatePages": True,
+                "name": "recent_window_full",
+                "updatedAfter": recent_cutoff,
+                "limit": max_models,
             }
         )
-
     deduped: list[dict[str, Any]] = []
     seen: set[tuple[str | None, int, str]] = set()
     for stage in stages:
@@ -951,20 +991,20 @@ def submit_candidate(
                 "candidate": candidate,
                 "reason": "already_processed_for_gpu",
             }
-        failed_frameworks = set(
-            (precheck.get("failedFrameworksByGpu") or {}).get(candidate["targetGpu"]) or set()
-        )
-        if candidate["framework"] in failed_frameworks:
+        if len(set(precheck.get("processedGpus") or set())) >= MAX_ROUTES_PER_MODEL:
             return {
                 "outcome": "duplicate",
                 "candidate": candidate,
-                "reason": "prior_failed_framework_for_gpu",
+                "reason": "model_route_limit_reached",
             }
-        if failed_frameworks and not candidate.get("frameworkEvidenceQualified"):
+        failed_frameworks = set(
+            (precheck.get("failedFrameworksByGpu") or {}).get(candidate["targetGpu"]) or set()
+        )
+        if failed_frameworks:
             return {
-                "outcome": "framework_evidence_deferred",
+                "outcome": "duplicate",
                 "candidate": candidate,
-                "reason": "prior_failed_gpu_requires_public_framework_evidence",
+                "reason": "prior_failed_gpu_closed_accuracy_first",
             }
 
     payload = {
@@ -1622,6 +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(

modelhub_submmit_api/market_intelligence.py

@@ -20,7 +20,8 @@ DEFAULT_THROUGHPUT_WINDOW_HOURS = 6
 DEFAULT_FETCH_WORKERS = 4
 DEFAULT_FRAMEWORK_MIN_SAMPLES = 300
 DEFAULT_GPU_MIN_RECENT_TERMINALS = 20
-DEFAULT_FRAMEWORK_MIN_WILSON = 0.05
+DEFAULT_FRAMEWORK_MIN_WILSON = 0.20
+DEFAULT_GPU_MIN_WILSON = 0.15
 DEFAULT_COMMUNITY_WINDOW_DAYS = 30
 DEFAULT_COMMUNITY_MAX_MODELS = 200
 DEFAULT_COMMUNITY_HYDRATE_PER_REFRESH = 50
@@ -201,7 +202,7 @@ class MarketIntelligenceManager:
                 and health_factor >= 0.5
                 and int(item.get("recentTerminal") or 0) >= DEFAULT_GPU_MIN_RECENT_TERMINALS
                 and int(item.get("recentSuccess") or 0) > 0
-                and wilson >= DEFAULT_FRAMEWORK_MIN_WILSON
+                and wilson >= DEFAULT_GPU_MIN_WILSON
             )
 
     def _base_state(self, gpus: list[str], task_types: list[str], now: datetime) -> dict[str, Any]:
@@ -554,7 +555,7 @@ class MarketIntelligenceManager:
                 and health_factor >= 0.5
                 and int(stats.get("recentTerminal") or 0) >= DEFAULT_GPU_MIN_RECENT_TERMINALS
                 and int(stats.get("recentSuccess") or 0) > 0
-                and wilson >= DEFAULT_FRAMEWORK_MIN_WILSON
+                and wilson >= DEFAULT_GPU_MIN_WILSON
             )
             result[gpu] = stats
 
@@ -661,7 +662,26 @@ class MarketIntelligenceManager:
             # Missing live data falls back to the scheduler's already-proven
             # long/recent pools. Explicitly failed public gates never do.
             if eligibility is False:
-                continue
+                low_sample = int(item.get("recentTerminal") or 0) < DEFAULT_GPU_MIN_RECENT_TERMINALS
+                has_recent_success = int(item.get("recentSuccess") or 0) > 0
+                recent_wilson = float(item.get("recentWilsonLowerBound") or 0.0)
+                healthy = float(item.get("healthFactor") or 1.0) >= 0.5
+                has_proven_framework = any(
+                    bool(framework.get("officialConfigValid", False))
+                    and int(framework.get("modelCount") or 0) >= self.framework_min_samples
+                    and float(framework.get("wilsonLowerBound") or 0.0) >= DEFAULT_FRAMEWORK_MIN_WILSON
+                    for by_gpu in ((self.state or {}).get("frameworkStats") or {}).values()
+                    for framework in ((by_gpu or {}).get(gpu) or {}).values()
+                )
+                if (
+                    item.get("canVerify") is False
+                    or not low_sample
+                    or not has_recent_success
+                    or recent_wilson < DEFAULT_GPU_MIN_WILSON
+                    or not healthy
+                    or not has_proven_framework
+                ):
+                    continue
             ranked.append((-float(item.get("selectionWeight") or 1.0), index, gpu))
         return [gpu for _weight, _index, gpu in sorted(ranked)]
 
@@ -723,6 +743,7 @@ class MarketIntelligenceManager:
 
         recent_item = (self.local_outcome_stats.get("recentCombinationStats") or {}).get(local_key) or {}
         recent_success = max(0, int(recent_item.get("successCount") or 0))
+        recent_all_failures = max(0, int(recent_item.get("failureCount") or 0))
         recent_failure = max(
             0,
             int(recent_item.get("attributableFailureCount", recent_item.get("failureCount") or 0)),
@@ -743,6 +764,16 @@ class MarketIntelligenceManager:
         elif last_terminal_at is not None and consecutive_failures >= 5:
             circuit_reason = "five_consecutive_local_failures"
             circuit_until = last_terminal_at + timedelta(hours=12)
+        elif (
+            last_terminal_at is not None
+            and recent_success + recent_all_failures >= 5
+            and recent_success / (recent_success + recent_all_failures) < 0.10
+        ):
+            # Runtime-empty and other unresolved failures must not create a
+            # permanent compatibility blacklist. Repetition is still strong
+            # operational evidence, so pause the route while feedback settles.
+            circuit_reason = "recent_terminal_success_below_10_percent"
+            circuit_until = last_terminal_at + timedelta(hours=12)
         elif last_terminal_at is not None and recent_samples >= 20 and recent_rate is not None and recent_rate < 0.20:
             circuit_reason = "recent_local_success_below_20_percent"
             circuit_until = last_terminal_at + timedelta(hours=6)

modelhub_submmit_api/modelhub_client.py

@@ -38,6 +38,7 @@ def parse_model_submission_precheck(payload: Any) -> dict[str, Any]:
         raise ModelHubAPIError("Community model precheck verifyResult is invalid", payload=payload)
 
     blocked_gpus: set[str] = set()
+    successful_gpus: set[str] = set()
     failed_frameworks_by_gpu: dict[str, set[str]] = {}
     for gpu, gpu_result in verify_result.items():
         name = str(gpu)
@@ -48,6 +49,12 @@ def parse_model_submission_precheck(payload: Any) -> dict[str, Any]:
         records = gpu_result.get("records")
         if status == "未验证" and isinstance(records, list) and not records:
             continue
+        blocked_gpus.add(name)
+        if status == "已验证" or any(
+            isinstance(record, dict) and _positive_verify_result(record.get("verifyResult"))
+            for record in (records or [])
+        ):
+            successful_gpus.add(name)
         if status == "验证失败" and isinstance(records, list) and records:
             frameworks: set[str] = set()
             for record in records:
@@ -63,20 +70,26 @@ def parse_model_submission_precheck(payload: Any) -> dict[str, Any]:
                 frameworks.add(framework)
             else:
                 failed_frameworks_by_gpu[name] = frameworks
-                continue
-        # Verified, validating and unknown states remain closed. A failed GPU
-        # opens only for a different framework with explicit failed records.
-        blocked_gpus.add(name)
+        # Accuracy-first admission: any existing model/GPU state remains
+        # closed. Failed-framework details are diagnostic evidence only.
 
     return {
         "isInDB": data.get("isInDB") is True,
         "processedGpus": blocked_gpus,
+        "successfulGpus": successful_gpus,
         "failedFrameworksByGpu": failed_frameworks_by_gpu,
         "verifyResult": verify_result,
         "modelInfo": data.get("modelInfo") if isinstance(data.get("modelInfo"), dict) else {},
     }
 
 
+def _positive_verify_result(value: Any) -> bool:
+    try:
+        return int(value) > 0
+    except (TypeError, ValueError):
+        return False
+
+
 class ModelHubClient:
     def __init__(
         self,
@@ -935,6 +948,26 @@ class ModelHubClientPool:
             usable = {index: remaining for index, remaining in remaining_by_index.items() if remaining > 0}
             if not usable:
                 return None
+            if (
+                reserve_capacity_slots is None
+                and weekly_target is None
+                and self.recent_model_reserve_slots > 0
+            ):
+                # Recent models can use every position; old models cannot.
+                # Pack recent work into reserved positions first, then the
+                # account nearest its old-model threshold. This preserves
+                # shared positions for later old candidates, regardless of
+                # candidate arrival order. Keep largest-headroom/round-robin
+                # balancing among accounts with the same shared capacity.
+                shared_slots = {
+                    index: max(0, remaining - self.recent_model_reserve_slots)
+                    for index, remaining in usable.items()
+                }
+                least_shared = min(shared_slots.values())
+                usable = {
+                    index: remaining for index, remaining in usable.items()
+                    if shared_slots[index] == least_shared
+                }
             best_remaining = max(usable.values())
             tied = [index for index, remaining in usable.items() if remaining == best_remaining]
             selected_index = min(
@@ -1016,7 +1049,12 @@ class ModelHubClientPool:
                 self._capacity_probe_cooldown_until[index] = (
                     self._capacity_probe_cycle + self._capacity_probe_cooldown_cycles
                 )
-            else:
+            elif self._weekly_target_locked() == index:
+                # Only the scheduled calibration may lower a learned cap.
+                # Ordinary rejection can race with another agent filling this
+                # account; our cached count is not evidence of its capacity.
+                # Persisting it (even zero -> one) strands otherwise usable
+                # positions until the next calibration.
                 observed_capacity = max(1, self._effective_count_locked(index))
                 if observed_capacity < self._account_caps[index]:
                     self._account_caps[index] = observed_capacity

modelhub_submmit_api/models.py

@@ -140,12 +140,17 @@ class ModelInspection:
         return None
 
     @property
-    def repository_size_bytes(self) -> int | None:
+    def exact_repository_size_bytes(self) -> int | None:
         """Return exact recursive on-disk size when every file has a size."""
-        exact: int | None = None
         if self.file_paths and not any(path not in self.file_sizes for path in self.file_paths):
             total = sum(max(0, int(self.file_sizes[path])) for path in self.file_paths)
-            exact = total if total > 0 else None
+            return total if total > 0 else None
+        return None
+
+    @property
+    def repository_size_bytes(self) -> int | None:
+        """Use the conservative maximum of recursive and published sizes."""
+        exact = self.exact_repository_size_bytes
         published = int(self.published_size_bytes or 0) or None
         values = [value for value in (exact, published) if value is not None]
         return max(values) if values else None

modelhub_submmit_api/outcome_tracker.py

@@ -344,6 +344,17 @@ class OutcomeTracker:
         self._by_model_gpu[(model_id, target_gpu)].append(record)
         append_jsonl(self.path, record)
 
+    def pending_route_count_for_model(self, model_id: str) -> int:
+        """Return distinct locally submitted GPU routes still awaiting an outcome."""
+        pending_gpus: set[str] = set()
+        for record in self._records:
+            if record.get("modelId") != model_id or record.get("outcome") != "pending":
+                continue
+            target_gpu = str(record.get("targetGpu") or "").strip()
+            if target_gpu:
+                pending_gpus.add(target_gpu)
+        return len(pending_gpus)
+
     def mark_policy_cancellations(self, decisions: list[dict[str, Any]]) -> int:
         """Persist intentional task stops so they never become failure evidence."""
         marked = 0

modelhub_submmit_api/routing_engine.py

@@ -123,6 +123,9 @@ class SuccessFirstRoutingEngine:
                 "routingEtaHours": eta,
                 "routingEtaUnknown": eta_unknown,
                 "routingExpectedSuccessPerHour": expected,
+                "routingCommunityModelSuccesses": max(
+                    0, int(candidate.get("communitySuccessfulGpuCount") or 0)
+                ),
                 "strategyCategory": "unified_success_first",
             }
         )
@@ -168,6 +171,7 @@ class SuccessFirstRoutingEngine:
         selected.sort(
             key=lambda item: (
                 -int(item["routingSuccessBand"]),
+                -int(item.get("routingCommunityModelSuccesses") or 0),
                 -float(item["routingExpectedSuccessPerHour"]),
                 -int(item["routingSamples"]),
                 -int(item.get("downloads") or 0),

modelhub_submmit_api/version.py

@@ -1 +1 @@
-AGENT_VERSION = "2026.09.20.2"
+AGENT_VERSION = "2026.09.22.1"

tests/test_candidate_preflight.py

@@ -243,6 +243,26 @@ class CandidatePreflightTests(unittest.TestCase):
         self.assertFalse(assessment.allowed)
         self.assertEqual("recursive_repository_on_disk", assessment.metadata["memorySizingBasis"])
 
+    def test_memory_diagnostics_distinguish_published_upper_bound(self) -> None:
+        gib = 1024 ** 3
+        inspection = ModelInspection(
+            repo_id="owner/published-larger",
+            file_paths=["config.json", "tokenizer.json", "model.safetensors"],
+            file_sizes={"config.json": 0, "tokenizer.json": 0, "model.safetensors": 20 * gib},
+            weight_files=["model.safetensors"],
+            model_config={"model_type": "qwen2"},
+            published_size_bytes=40 * gib,
+        )
+        assessment = CandidatePreflightAdvisor(gpu_memory_gib={"gpu": 32}).assess(
+            inspection=inspection, task_type="text-generation", target_gpu="gpu",
+            framework="vllm", config_params="max_model_len: 4096",
+        )
+        self.assertEqual("preflight_predicted_oom", assessment.reason)
+        self.assertEqual(20 * gib, assessment.metadata["recursiveRepositoryBytes"])
+        self.assertEqual(40 * gib, assessment.metadata["publishedSizeBytes"])
+        self.assertEqual(48.0, assessment.metadata["estimatedRequiredGiB"])
+        self.assertEqual("published_repository_upper_bound", assessment.metadata["memorySizingBasis"])
+
     def test_p800_verified_capacity_accepts_80_gib_repository_boundary(self) -> None:
         gib = 1024**3
 

tests/test_concurrency.py

@@ -6,6 +6,7 @@ import tempfile
 import threading
 import unittest
 from concurrent.futures import ThreadPoolExecutor
+from dataclasses import replace
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from unittest.mock import patch
@@ -216,6 +217,75 @@ def make_candidate(index: int) -> dict:
 
 
 class ClientPoolConcurrencyTests(unittest.TestCase):
+    def test_recent_work_preserves_shared_slots_for_later_old_models(self) -> None:
+        for initial_counts, ages, expected_counts in (
+            ([94, 95], ["recent", "old"], [95, 96]),
+            ([93, 94], ["recent", "recent", "old", "old"], [95, 96]),
+        ):
+            with self.subTest(initial_counts=initial_counts):
+                clients = [FakeClient(active_count=count) for count in initial_counts]
+                pool = ModelHubClientPool(
+                    clients,  # type: ignore[arg-type]
+                    active_task_cap=100,
+                    recent_model_reserve_slots=5,
+                    active_counts_ttl=60,
+                    capacity_probe_interval_cycles=0,
+                )
+                for index, age in enumerate(ages):
+                    pool.add_task_for_model(
+                        {"model": f"model-{index}"},
+                        model_last_modified=(
+                            "2026-10-01T00:00:00Z" if age == "recent"
+                            else "2025-01-01T00:00:00Z"
+                        ),
+                        submitted_at="2026-10-02T00:00:00Z",
+                    )
+                self.assertEqual(expected_counts, pool.active_task_counts())
+                self.assertEqual(len(ages), sum(len(client.submitted) for client in clients))
+                self.assertEqual(0, pool.old_model_submit_slots())
+
+    def test_recent_packing_keeps_largest_headroom_without_reserved_slots(self) -> None:
+        clients = [FakeClient(active_count=2), FakeClient(active_count=3)]
+        pool = ModelHubClientPool(
+            clients,  # type: ignore[arg-type]
+            active_task_cap=5,
+            recent_model_reserve_slots=0,
+        )
+        pool.add_task({"model": "recent"})
+        self.assertEqual([1, 0], [len(client.submitted) for client in clients])
+
+    def test_stale_capacity_rejection_does_not_poison_learned_limit(self) -> None:
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            state_path = Path(temporary_dir) / "capacity.json"
+            client = FakeClient(active_count=0, reject_capacity=True)
+            pool = ModelHubClientPool(
+                [client],  # type: ignore[list-item]
+                active_task_cap=100,
+                recent_model_reserve_slots=5,
+                capacity_probe_interval_cycles=0,
+                capacity_state_path=state_path,
+            )
+            self.assertEqual(100, pool.available_submit_slots())
+            with self.assertRaises(ModelHubAPIError):
+                pool.add_task({"model": "rejected-during-race"})
+
+            self.assertEqual([100], pool.account_capacity_limits())
+            client.reject_capacity = False
+            pool.refresh_active_counts()
+            self.assertEqual(95, pool.old_model_submit_slots())
+            pool.add_task_for_model(
+                {"model": "eligible-old-model"},
+                model_last_modified="2025-01-01T00:00:00Z",
+                submitted_at="2026-10-02T00:00:00Z",
+            )
+            self.assertEqual(1, len(client.submitted))
+            restarted = ModelHubClientPool(
+                [client],  # type: ignore[list-item]
+                active_task_cap=100,
+                capacity_state_path=state_path,
+            )
+            self.assertEqual([100], restarted.account_capacity_limits())
+
     def test_exact_active_count_uses_filtered_platform_totals(self) -> None:
         client = ModelHubClient(token="test-token")
         calls: list[str] = []
@@ -481,8 +551,37 @@ class ClientPoolConcurrencyTests(unittest.TestCase):
             recent_model_days=7,
         )
 
-        self.assertEqual(["configured_window", "last_7_days"], [stage["name"] for stage in stages])
+        self.assertEqual(
+            ["configured_window", "last_7_days", "recent_window_full"],
+            [stage["name"] for stage in stages],
+        )
         self.assertTrue(all(stage["updatedAfter"] >= now - timedelta(days=7) for stage in stages))
+        self.assertEqual(3000, stages[-1]["limit"])
+
+    def test_recent_only_scan_deepens_even_when_date_window_cannot_expand(self) -> None:
+        now = datetime(2026, 8, 11, tzinfo=timezone.utc)
+        for initial_cutoff in (None, now - timedelta(days=7), now - timedelta(days=30)):
+            with self.subTest(initial_cutoff=initial_cutoff):
+                stages = build_adaptive_scan_stages(
+                    now=now,
+                    initial_updated_after=initial_cutoff,
+                    initial_limit=20,
+                    max_models=800,
+                    allow_older_than_recent_window=False,
+                )
+                self.assertEqual([20, 800], [stage["limit"] for stage in stages])
+                self.assertTrue(all(stage["updatedAfter"] == now - timedelta(days=7) for stage in stages))
+
+    def test_recent_only_scan_deduplicates_an_already_full_window(self) -> None:
+        now = datetime(2026, 8, 11, tzinfo=timezone.utc)
+        stages = build_adaptive_scan_stages(
+            now=now,
+            initial_updated_after=now - timedelta(days=7),
+            initial_limit=100,
+            max_models=100,
+            allow_older_than_recent_window=False,
+        )
+        self.assertEqual(1, len(stages))
 
     def test_submit_candidate_reports_old_model_policy_skip_at_dynamic_threshold(self) -> None:
         client = FakeClient(active_count=95)
@@ -733,11 +832,59 @@ class ClientPoolConcurrencyTests(unittest.TestCase):
             )
 
             self.assertEqual(2, summary["submittedCount"])
-            self.assertEqual(len(supported_gpus), summary["skipReasonCounts"]["already_processed_for_gpu"])
-            self.assertEqual("popular_history", summary["scanStages"][-1]["name"])
-            self.assertEqual("downloads", summary["scanStages"][-1]["sort"])
+            self.assertEqual(1, summary["skipReasonCounts"]["model_route_limit_reached"])
+            self.assertEqual("all_history", summary["scanStages"][-1]["name"])
+            self.assertEqual("last_modified", summary["scanStages"][-1]["sort"])
             self.assertIsNone(discovery.calls[-1])
 
+    def test_reserved_slots_find_recent_candidates_beyond_first_scan_prefix(self) -> None:
+        class RecentOnlyClient(ExhaustedRecentClient):
+            def old_model_submit_slots(self) -> int:
+                return 0
+
+        class PagedRecentDiscovery(FakeDiscovery):
+            def __init__(self) -> None:
+                super().__init__(4)
+                self.models[0] = replace(self.models[0], repo_id="owner/recent-exhausted")
+                self.limits: list[int] = []
+
+            def list_recent_models(self, *, limit, **_kwargs):  # noqa: ANN003
+                self.limits.append(limit)
+                return self.models[:limit]
+
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            root = Path(temporary_dir)
+            args = build_parser().parse_args([
+                "--task-types", "text-generation", "--since-hours", "168",
+                "--limit", "1", "--max-scan-models", "4",
+                "--skip-outcome-sync", "--skip-history-archive",
+                "--disable-gpu-strategy",
+            ])
+            for field, filename in (
+                ("runs_dir", "runs"), ("ledger_path", "ledger.jsonl"),
+                ("outcomes_path", "outcomes.jsonl"), ("claims_path", "claims.jsonl"),
+                ("history_archive_path", "history.jsonl"),
+                ("submission_exclusions_path", "exclusions.jsonl"),
+            ):
+                setattr(args, field, str(root / filename))
+            selector = TemplateSelector()
+            discovery = PagedRecentDiscovery()
+            client = RecentOnlyClient(
+                available=2,
+                processed_gpus=set(selector.supported_target_gpus("text-generation", auto_only=True)),
+            )
+            summary = run_submission(
+                args,
+                now=datetime(2026, 1, 2, 12, tzinfo=timezone.utc),
+                hf_discovery=discovery,  # type: ignore[arg-type]
+                modelhub_client=client,  # type: ignore[arg-type]
+                template_selector=selector,
+            )
+            self.assertEqual([1, 4], discovery.limits)
+            self.assertEqual(2, summary["submittedCount"])
+            self.assertEqual("recent_window_full", summary["scanStages"][-1]["name"])
+            self.assertEqual(4, summary["scannedModels"])
+
     def test_adaptive_strategy_counts_only_platform_accepted_tasks(self) -> None:
         with tempfile.TemporaryDirectory() as temporary_dir:
             root = Path(temporary_dir)

tests/test_market_intelligence.py

@@ -219,7 +219,7 @@ class MarketIntelligenceTests(unittest.TestCase):
             "frameworkStats": {
                 "text-generation": {
                     "gpu": {
-                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.10},
+                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.30},
                         "vllm-mlu": {"modelCount": 800, "wilsonLowerBound": 0.35},
                         "vllm-customized": {"modelCount": 2, "wilsonLowerBound": 0.90},
                     }
@@ -357,7 +357,7 @@ class MarketIntelligenceTests(unittest.TestCase):
             "frameworkStats": {
                 "text-generation": {
                     "Cambricon_mlu-370-x4": {
-                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.10},
+                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.30},
                         "future-engine": {
                             "modelCount": 1000,
                             "successCount": 500,
@@ -396,7 +396,7 @@ class MarketIntelligenceTests(unittest.TestCase):
             "frameworkStats": {
                 "text-generation": {
                     "Cambricon_mlu-370-x4": {
-                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.10},
+                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.30},
                         "future-engine": {
                             "modelCount": 2,
                             "wilsonLowerBound": 0.90,
@@ -454,6 +454,124 @@ class MarketIntelligenceTests(unittest.TestCase):
         )
         self.assertIsNone(candidate)
 
+    def test_low_confidence_public_framework_is_rejected_even_with_many_samples(self) -> None:
+        manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
+        manager.state = {
+            "frameworkStats": {
+                "text-generation": {
+                    "Cambricon_mlu-370-x4": {
+                        "vllm": {"modelCount": 10_000, "wilsonLowerBound": 0.12},
+                    }
+                }
+            }
+        }
+        candidate = choose_candidate_for_gpu(
+            model=HFModelSummary(
+                repo_id="owner/model",
+                downloads=100,
+                last_modified=None,
+                pipeline_tag="text-generation",
+            ),
+            inspection=ModelInspection(repo_id="owner/model", weight_files=["model.safetensors"]),
+            template_selector=TemplateSelector(),
+            task_types=["text-generation"],
+            target_gpu="Cambricon_mlu-370-x4",
+            market_intelligence=manager,
+        )
+        self.assertIsNone(candidate)
+
+    def test_repeated_ambiguous_failures_open_operational_circuit(self) -> None:
+        manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
+        manager.state = {
+            "frameworkStats": {
+                "text-generation": {
+                    "Biren_166m": {
+                        "vllm_fix_tokenizer": {"modelCount": 10_000, "wilsonLowerBound": 0.35},
+                    }
+                }
+            }
+        }
+        manager.set_local_outcome_stats(
+            {
+                "recentCombinationStats": {
+                    "Biren_166m|vllm_fix_tokenizer|text-generation": {
+                        "successCount": 0,
+                        "failureCount": 5,
+                        "attributableFailureCount": 0,
+                        "consecutiveFailures": 0,
+                        "lastTerminalAt": datetime.now(timezone.utc).isoformat(),
+                    }
+                }
+            }
+        )
+        metadata = manager.framework_metadata(
+            "text-generation", "Biren_166m", "vllm_fix_tokenizer"
+        )
+        self.assertTrue(metadata["frameworkCircuitOpen"])
+        self.assertEqual(
+            "recent_terminal_success_below_10_percent",
+            metadata["frameworkCircuitReason"],
+        )
+
+    def test_low_sample_gpu_with_recent_success_and_proven_framework_remains_eligible(self) -> None:
+        manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
+        manager.state = {
+            "gpuStats": {
+                "Sunrise_pt-200-x1": {
+                    "canVerify": True,
+                    "submissionEligible": False,
+                    "recentTerminal": 10,
+                    "recentSuccess": 5,
+                    "recentWilsonLowerBound": 0.24,
+                    "healthFactor": 1.0,
+                    "selectionWeight": 0.5,
+                }
+            },
+            "frameworkStats": {
+                "text-generation": {
+                    "Sunrise_pt-200-x1": {
+                        "vllm_fix_tokenizer": {
+                            "modelCount": 6_000,
+                            "wilsonLowerBound": 0.41,
+                            "officialConfigValid": True,
+                        }
+                    }
+                }
+            },
+        }
+        self.assertEqual(
+            ["Sunrise_pt-200-x1"],
+            manager.eligible_gpus(["Sunrise_pt-200-x1"]),
+        )
+
+    def test_low_sample_gpu_does_not_bypass_absolute_quality_floor(self) -> None:
+        manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
+        manager.state = {
+            "gpuStats": {
+                "gpu-low-quality": {
+                    "canVerify": True,
+                    "submissionEligible": False,
+                    "recentTerminal": 10,
+                    "recentSuccess": 1,
+                    "recentWilsonLowerBound": 0.02,
+                    "healthFactor": 1.0,
+                    "selectionWeight": 0.5,
+                }
+            },
+            "frameworkStats": {
+                "text-generation": {
+                    "gpu-low-quality": {
+                        "vllm": {
+                            "modelCount": 6_000,
+                            "wilsonLowerBound": 0.41,
+                            "officialConfigValid": True,
+                        }
+                    }
+                }
+            },
+        }
+        self.assertEqual([], manager.eligible_gpus(["gpu-low-quality"]))
+
     def test_five_consecutive_local_failures_circuit_break_framework(self) -> None:
         manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
         manager.state = {

tests/test_modelscope_discovery.py

@@ -11,7 +11,7 @@ if str(PACKAGE_DIR) in sys.path:
     sys.path.remove(str(PACKAGE_DIR))
 sys.path.insert(0, str(PACKAGE_DIR))
 
-from hf_discovery import HuggingFaceDiscovery  # noqa: E402
+from hf_discovery import HuggingFaceDiscovery, inspect_repo_tree  # noqa: E402
 from http_json import HttpJsonError  # noqa: E402
 
 
@@ -58,6 +58,56 @@ class ModelMetadataClient:
 
 
 class ModelScopeDiscoveryTests(unittest.TestCase):
+    def test_listing_diagnostics_distinguish_filtered_prefix_from_exhaustion(self) -> None:
+        class FilteredPageClient:
+            def request_json(self, *_args, **_kwargs):
+                return {"data": {"models": [
+                    {"id": f"owner/model-{index}", "downloads": 0}
+                    for index in range(50)
+                ]}}
+
+        discovery = HuggingFaceDiscovery(http_client=FilteredPageClient())  # type: ignore[arg-type]
+        self.assertEqual([], discovery.list_recent_models(
+            pipeline_tags=["text-generation"], limit=50, min_downloads=50,
+        ))
+        diagnostic = discovery.last_listing_diagnostics[0]
+        self.assertEqual(50, diagnostic["rawRowsScanned"])
+        self.assertEqual(0, diagnostic["acceptedModels"])
+        self.assertEqual("page_budget", diagnostic["stopReason"])
+        self.assertFalse(diagnostic["catalogExhausted"])
+
+    def test_failed_repo_tree_is_retried_instead_of_cached_as_empty(self) -> None:
+        class RecoveringClient:
+            calls = 0
+
+            def request_json(self, *_args, **_kwargs):
+                self.calls += 1
+                if self.calls == 1:
+                    raise HttpJsonError("temporary outage", status_code=503)
+                return {"Data": {"Files": [{"Path": "model.safetensors", "Size": 1024}]}}
+
+        client = RecoveringClient()
+        discovery = HuggingFaceDiscovery(legacy_http_client=client)  # type: ignore[arg-type]
+        with self.assertRaises(HttpJsonError):
+            discovery.list_repo_tree("owner/recoverable")
+        self.assertNotIn("owner/recoverable", discovery._repo_tree_cache)
+        entries = discovery.list_repo_tree("owner/recoverable")
+        self.assertEqual("model.safetensors", entries[0]["Path"])
+        self.assertEqual(entries, discovery.list_repo_tree("owner/recoverable"))
+        self.assertEqual(2, client.calls)
+
+    def test_duplicate_normalized_paths_do_not_inflate_repository_size(self) -> None:
+        gib = 1024 ** 3
+        inspection = inspect_repo_tree("owner/model", [
+            {"Path": "model.safetensors", "Size": 20 * gib},
+            {"Path": "./model.safetensors", "Size": 19 * gib},
+            {"Path": "/model.safetensors", "Size": 20 * gib},
+        ])
+        self.assertEqual(["model.safetensors"], inspection.file_paths)
+        self.assertEqual(["model.safetensors"], inspection.weight_files)
+        self.assertEqual(20 * gib, inspection.repository_size_bytes)
+        self.assertEqual(20 * gib, inspection.estimated_load_bytes("vllm"))
+
     def test_model_last_modified_uses_detail_api_and_is_cached(self) -> None:
         metadata_client = ModelMetadataClient()
         discovery = HuggingFaceDiscovery(legacy_http_client=metadata_client)  # type: ignore[arg-type]

tests/test_submission_safety.py

@@ -21,6 +21,7 @@ from modelhub_client import (  # noqa: E402
     parse_model_submission_precheck,
 )
 from models import HFModelSummary, ModelInspection  # noqa: E402
+from outcome_tracker import OutcomeTracker  # noqa: E402
 from submission_exclusions import SubmissionExclusionStore  # noqa: E402
 from template_selector import TemplateSelector  # noqa: E402
 
@@ -286,9 +287,10 @@ class SubmissionSafetyTests(unittest.TestCase):
         }
         precheck = parse_model_submission_precheck(payload)
         self.assertEqual(
-            {"running", "success", "unknown", "failed_without_framework"},
+            {"failed", "running", "success", "unknown", "failed_without_framework"},
             precheck["processedGpus"],
         )
+        self.assertEqual({"success"}, precheck["successfulGpus"])
         self.assertEqual({"failed": {"vllm"}}, precheck["failedFrameworksByGpu"])
 
     def test_unverified_gpu_becomes_candidate_but_validating_gpu_does_not(self) -> None:
@@ -321,7 +323,7 @@ class SubmissionSafetyTests(unittest.TestCase):
         self.assertEqual([], active_candidates)
         self.assertEqual("already_processed_for_gpu", active_skips[0]["reason"])
 
-    def test_failed_gpu_requires_an_alternative_framework(self) -> None:
+    def test_failed_gpu_remains_closed_even_when_an_alternative_framework_exists(self) -> None:
         model = SafeDiscovery().model
         client = FailedFrameworkClient()
         candidates, skipped, failed = process_model_for_candidates(
@@ -334,23 +336,14 @@ class SubmissionSafetyTests(unittest.TestCase):
             market_intelligence=TwoFrameworkMarket(),  # type: ignore[arg-type]
             official_registry=AllGpuOfficialRegistry(),  # type: ignore[arg-type]
         )
-        self.assertEqual([], skipped)
+        self.assertEqual("prior_failed_gpu_closed_accuracy_first", skipped[-1]["reason"])
         self.assertEqual([], failed)
-        self.assertEqual(["vllm-mlu"], [item["framework"] for item in candidates])
-        self.assertEqual(["vllm"], candidates[0]["previousFailedFrameworks"])
+        self.assertEqual([], candidates)
 
-        blocked = submit_candidate({**candidates[0], "framework": "vllm"}, client)  # type: ignore[arg-type]
-        accepted = submit_candidate(candidates[0], client)  # type: ignore[arg-type]
+        blocked = submit_candidate(candidate("Cambricon_mlu-370-x4"), client)  # type: ignore[arg-type]
         self.assertEqual("duplicate", blocked["outcome"])
-        self.assertEqual("prior_failed_framework_for_gpu", blocked["reason"])
-        self.assertEqual("submitted", accepted["outcome"])
-        self.assertEqual(1, client.add_calls)
-
-        unvetted = submit_candidate(
-            {**candidates[0], "frameworkEvidenceQualified": False}, client  # type: ignore[arg-type]
-        )
-        self.assertEqual("framework_evidence_deferred", unvetted["outcome"])
-        self.assertEqual(1, client.add_calls)
+        self.assertEqual("prior_failed_gpu_closed_accuracy_first", blocked["reason"])
+        self.assertEqual(0, client.add_calls)
 
         no_routes, no_route_skips, _ = process_model_for_candidates(
             model=model,
@@ -363,7 +356,7 @@ class SubmissionSafetyTests(unittest.TestCase):
             official_registry=AllGpuOfficialRegistry(),  # type: ignore[arg-type]
         )
         self.assertEqual([], no_routes)
-        self.assertEqual("prior_failed_framework_no_alternative", no_route_skips[-1]["reason"])
+        self.assertEqual("prior_failed_gpu_closed_accuracy_first", no_route_skips[-1]["reason"])
 
         no_market_routes, no_market_skips, _ = process_model_for_candidates(
             model=model,
@@ -375,10 +368,65 @@ class SubmissionSafetyTests(unittest.TestCase):
         )
         self.assertEqual([], no_market_routes)
         self.assertEqual(
-            "prior_failed_gpu_requires_public_framework_evidence",
+            "prior_failed_gpu_closed_accuracy_first",
             no_market_skips[-1]["reason"],
         )
 
+    def test_model_route_limit_is_global_across_poll_cycles(self) -> None:
+        class SixRouteClient:
+            @staticmethod
+            def model_submission_precheck(_model_id: str) -> dict:
+                return {
+                    "processedGpus": {f"gpu-{index}" for index in range(1, 7)},
+                    "successfulGpus": {"gpu-1"},
+                    "failedFrameworksByGpu": {},
+                }
+
+        candidates, skipped, failed = process_model_for_candidates(
+            model=SafeDiscovery().model,
+            hf_discovery=SafeDiscovery(),  # type: ignore[arg-type]
+            modelhub_client=SixRouteClient(),  # type: ignore[arg-type]
+            template_selector=TemplateSelector(),
+            target_gpus=["gpu-7"],
+            allowed_task_types=["text-generation"],
+        )
+        self.assertEqual([], candidates)
+        self.assertEqual([], failed)
+        self.assertEqual("model_route_limit_reached", skipped[0]["reason"])
+        self.assertEqual(6, skipped[0]["processedGpuCount"])
+
+    def test_model_waits_for_feedback_after_two_pending_routes(self) -> None:
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            root = Path(temporary_dir)
+            tracker = OutcomeTracker(
+                root / "outcomes.jsonl",
+                checkpoint_path=root / "checkpoint.json",
+                recent_path=root / "recent.jsonl",
+            )
+            tracker.record_submission(
+                "owner/model", "gpu-1", "vllm", "text-generation", "task-1",
+                "2026-09-22T00:00:00+00:00",
+            )
+            tracker.record_submission(
+                "owner/model", "gpu-2", "vllm", "text-generation", "task-2",
+                "2026-09-22T00:01:00+00:00",
+            )
+
+            candidates, skipped, failed = process_model_for_candidates(
+                model=SafeDiscovery().model,
+                hf_discovery=SafeDiscovery(),  # type: ignore[arg-type]
+                modelhub_client=OtherGpuProcessedClient(),  # type: ignore[arg-type]
+                template_selector=TemplateSelector(),
+                target_gpus=["gpu-3"],
+                allowed_task_types=["text-generation"],
+                outcome_tracker=tracker,
+            )
+
+            self.assertEqual([], candidates)
+            self.assertEqual([], failed)
+            self.assertEqual("model_pending_feedback_limit", skipped[0]["reason"])
+            self.assertEqual(2, skipped[0]["pendingRouteCount"])
+
     def test_published_size_avoids_official_calls_for_certain_oom(self) -> None:
         class CountingRegistry(AllGpuOfficialRegistry):
             calls = 0