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

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

@@ -2,6 +2,30 @@
 
 This repository is packaged for the ModelHub XC agent platform.
 
+## Accuracy release 2026.10.18.1
+
+This release rejects malformed YAML command arrays, including the observed
+`/opt/entrypoint.sh    - /model` executable error, even in restored cached
+official configurations. It preserves terminal compatibility errors when a
+log also contains transient platform resource messages. Framework scoring
+includes unresolved non-platform failures; sparse model-profile evidence is
+blended with eight prior observations instead of discarded below a threshold.
+
+For operation alongside the baseline agent, the new default state branch is
+`agent-state-accuracy-v2`. Do not override it with the baseline's `agent-state`.
+The separate branch starts its own checkpoint on first boot; it does not
+overwrite or migrate the baseline state. New submissions retain agent version,
+instance ID and platform strategy ID. Historical tasks are not assigned an
+invented version.
+
+`MODELHUB_COEXISTENCE_MODE=1` is the default: this agent does not run queue
+cancellation, including deterministic cleanup, because the accounts are shared
+with the baseline. Submission preflight remains enabled. Set this variable to
+`0` only when account-wide cleanup is intended; `--disable-queue-cleanup` still
+disables cleanup explicitly. The baseline agent is unchanged. Both agents share
+account capacity and platform deduplication; separate state does not reserve
+capacity or eliminate simultaneous submission races.
+
 ## Platform Contract
 
 - Root-level `Dockerfile`
@@ -67,7 +91,7 @@ Optional tuning:
 - `MODELHUB_RECENT_MODEL_RESERVE_SLOTS` default `5` per account
 - `MODELHUB_RECENT_MODEL_DAYS` default `7`
 - `MODELHUB_STATE_SYNC_REMOTE` default `https://dev.modelhub.org.cn/CoolBoy/submmit.git`
-- `MODELHUB_STATE_SYNC_BRANCH` default `agent-state`
+- `MODELHUB_STATE_SYNC_BRANCH` default `agent-state-accuracy-v2`
 - `MODELHUB_STATE_SYNC_BATCH_SIZE` default `20`
 
 Git state synchronization reads `modelhub_user_name`, `modelhub_user_email`, and
@@ -140,7 +164,8 @@ unvetted GPU or framework.
 ## Durable State Branch
 
 The hosted worker synchronizes an allowlisted runtime snapshot to the orphan
-`agent-state` branch of this repository. Before each batch of at most 20 API
+`agent-state-accuracy-v2` branch of this repository (overridable via
+`MODELHUB_STATE_SYNC_BRANCH`). Before each batch of at most 20 API
 submissions it pushes write-ahead intents containing model, GPU, task, framework,
 safe parameter vector, and a config fingerprint. It pushes task IDs and results
 after the batch. If either push fails, no further submission begins until the
@@ -326,6 +351,18 @@ 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

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/failure_taxonomy.py

@@ -67,7 +67,7 @@ PLATFORM_PATTERNS = (
     (re.compile(r"welcome\.sh: No such file|data: command not found", re.I), "broken_platform_launch_script"),
     (re.compile(r"id_rsa.*No such file", re.I), "missing_platform_credential"),
     (re.compile(r"找不到空闲卡|no idle (?:gpu|card)", re.I), "no_idle_device"),
-    (re.compile(r"storage|download.*timed? out|connection reset", re.I), "platform_io_transient"),
+    (re.compile(r"no space left on device|download.*timed? out|connection reset", re.I), "platform_io_transient"),
 )
 
 DETERMINISTIC_LOG_PATTERNS = (
@@ -80,6 +80,8 @@ DETERMINISTIC_LOG_PATTERNS = (
 # semantics. Generic messages such as "attention backend not supported" or
 # "GPU type not supported" must not create an architecture blacklist.
 EXPLICIT_FRAMEWORK_MODEL_UNSUPPORTED_PATTERNS = (
+    re.compile(r"model type\s+[`'\"][^`'\"]+[`'\"].{0,100}does not recognize this architecture", re.I),
+    re.compile(r"Model architectures?\s*\[[^\]\n]+\]\s*(?:are|is) not supported", re.I),
     re.compile(
         r"(?:当前|该|此)(?:推理)?框架.{0,100}(?:不支持|暂不支持|无法支持|不兼容)"
         r".{0,100}(?:该|此|当前)?(?:模型架构|模型|架构)",
@@ -117,19 +119,16 @@ def classify_failure_report(report_code: str | None, log_lines: Iterable[str] =
     code = str(report_code or "").strip().upper()
     text = "\n".join(str(line) for line in log_lines)
 
-    # Infrastructure signatures override broad report codes such as
-    # EXECUTE_EMPTY_RESULT so they do not poison model compatibility feedback.
-    for pattern, reason in PLATFORM_PATTERNS:
-        if pattern.search(text):
+    if re.search(r'exec:.*entrypoint.*(?:no such file|not found)', text, re.I):
         return FailureClassification(
-                "platform_infrastructure",
-                "gpu_framework",
-                "open_short_gpu_framework_circuit_and_retry_other_models",
-                True,
-                False,
-                reason,
+            "configuration_launch", "configuration", "reject_broken_config", True, False,
+            "invalid_executable_argv",
+        )
+    if re.search(r'quantization method .*not supported for the current GPU', text, re.I):
+        return FailureClassification(
+            "quantization_compatibility", "model_gpu_framework", "reject_quantization_route",
+            True, False, "explicit_quantization_gpu_incompatibility",
         )
-
     if code in DETERMINISTIC_POLICIES:
         return DETERMINISTIC_POLICIES[code]
     for pattern, inferred_code in DETERMINISTIC_LOG_PATTERNS:
@@ -149,6 +148,15 @@ def classify_failure_report(report_code: str | None, log_lines: Iterable[str] =
         )
     if code in SEMANTIC_POLICIES:
         return SEMANTIC_POLICIES[code]
+    # Fall back to infrastructure signatures only after specific root causes.
+    # Logs may include a transient allocation failure before a model starts.
+    for pattern, reason in PLATFORM_PATTERNS:
+        if pattern.search(text):
+            return FailureClassification(
+                "platform_infrastructure", "gpu_framework",
+                "open_short_gpu_framework_circuit_and_retry_other_models",
+                True, False, reason,
+            )
     return FailureClassification(
         "ambiguous_runtime",
         "unknown",

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

@@ -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(

modelhub_submmit_api/market_intelligence.py

@@ -4,6 +4,7 @@ import math
 import re
 import statistics
 import threading
+import yaml
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime, timedelta
 from pathlib import Path
@@ -97,6 +98,31 @@ def _validated_official_config(config: str, *, framework: str, target_gpu: str)
         return False, "missing_sut_or_ref_config"
     if any(marker in config for marker in ("{{", "}}", "PLACEHOLDER")):
         return False, "unresolved_placeholder"
+    try:
+        document = yaml.safe_load(config)
+    except yaml.YAMLError:
+        return False, "invalid_yaml"
+    if not isinstance(document, dict):
+        return False, "invalid_config_structure"
+
+    def valid_commands(node: Any) -> bool:
+        if isinstance(node, dict):
+            for key, value in node.items():
+                if key in {"command", "entrypoint"} and isinstance(value, list):
+                    if not value or not all(isinstance(arg, str) for arg in value):
+                        return False
+                    # argv[0] is an executable, not a shell command. In
+                    # particular, a folded YAML list item must not eat argv[1].
+                    if not value[0].strip() or re.search(r"\s", value[0]):
+                        return False
+                if not valid_commands(value):
+                    return False
+        elif isinstance(node, list):
+            return all(valid_commands(value) for value in node)
+        return True
+
+    if not valid_commands(document):
+        return False, "malformed_command_argv"
 
     declared = re.search(r"(?m)^\s*framework:\s*['\"]?([^'\"\s]+)", config)
     if declared is None or declared.group(1).strip() != framework:
@@ -736,7 +762,7 @@ class MarketIntelligenceManager:
         local_success = max(0, int(local_item.get("successCount") or 0))
         local_failure = max(
             0,
-            int(local_item.get("attributableFailureCount", local_item.get("failureCount") or 0)),
+            int(local_item.get("failureCount") or 0) - int(local_item.get("platformFailureCount") or 0),
         )
         local_samples = local_success + local_failure
         local_score = _wilson_lower_bound(local_success, local_samples)
@@ -746,7 +772,7 @@ class MarketIntelligenceManager:
         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)),
+            int(recent_item.get("failureCount") or 0) - int(recent_item.get("platformFailureCount") or 0),
         )
         recent_samples = recent_success + recent_failure
         recent_rate = recent_success / recent_samples if recent_samples else None
@@ -849,11 +875,31 @@ class MarketIntelligenceManager:
             if incumbent_best > 0 and float(evidence["combinedScore"]) < incumbent_best * NEW_FRAMEWORK_PROMOTION_MARGIN:
                 continue
             promoted.append(framework)
-        return self.rank_frameworks(
+        ranked = self.rank_frameworks(
             task_type=task_type,
             target_gpu=target_gpu,
             compatible_frameworks=promoted,
         )
+        model_type = str(getattr(inspection, "model_type", None) or "").strip()
+        quantization = str(getattr(inspection, "quantization_method", None) or "none").strip()
+        if not model_type:
+            return ranked
+
+        def profile_score(framework: str) -> float:
+            prior = float(self._framework_evidence(task_type, target_gpu, framework)["combinedScore"])
+            key = f"{target_gpu}|{framework}|{task_type}|{model_type}|{quantization}"
+            item = (self.local_outcome_stats.get("profileCombinationStats") or {}).get(key) or {}
+            success = max(0, int(item.get("successCount") or 0))
+            failure = max(0, int(item.get("failureCount") or 0) - int(item.get("platformFailureCount") or 0))
+            samples = success + failure
+            if not samples:
+                return prior
+            # Model-specific negative evidence must affect the framework
+            # choice, not only the ranking after the first framework wins.
+            lower = _wilson_lower_bound(success + 8 * prior, samples + 8)
+            return lower
+
+        return sorted(ranked, key=profile_score, reverse=True)
 
     def compatible_discovered_frameworks(
         self,
@@ -941,6 +987,11 @@ class MarketIntelligenceManager:
         config = item.get("officialConfig")
         if not isinstance(config, str) or not config:
             return None
+        valid, reason = _validated_official_config(config, framework=framework, target_gpu=target_gpu)
+        if not valid:
+            item["officialConfigValid"] = False
+            item["officialConfigError"] = reason
+            return None
         return _render_official_config(config, gguf_filename=gguf_filename)
 
     def framework_metadata(self, task_type: str, target_gpu: str, framework: str) -> dict[str, Any]:

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

@@ -22,6 +22,8 @@ from history_stats import classify_failure, is_failure, is_success
 from llm_classifier import LLMAssistedClassifier
 from modelhub_client import ModelHubClient, ModelHubClientPool
 from task_registry import task_type_from_history_task
+from common import runtime_instance_id
+from version import AGENT_VERSION
 
 
 DEFAULT_OUTCOMES_PATH = Path("outcomes/submissions.jsonl")
@@ -36,6 +38,7 @@ FAILURE_ENRICHMENT_MAX_ATTEMPTS = 3
 TERMINAL_TASK_STATUSES = {"success", "failed", "error", "cancelled", "completed"}
 
 DECISION_RECORD_FIELDS = {
+    "agentVersion", "instanceId", "strategyId",
     "taskId",
     "modelId",
     "targetGpu",
@@ -325,6 +328,9 @@ class OutcomeTracker:
         model_profile: dict[str, Any] | None = None,
     ) -> None:
         record: dict[str, Any] = {
+            "agentVersion": AGENT_VERSION,
+            "instanceId": runtime_instance_id(),
+            "strategyId": os.getenv("STRATEGY_ID"),
             "modelId": model_id,
             "targetGpu": target_gpu,
             "framework": framework,

modelhub_submmit_api/poll_runner.py

@@ -234,7 +234,9 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--post-cycle-cooldown-seconds", type=int, default=2, help="Short sleep after a successful cycle")
     parser.add_argument("--max-cycles", type=int, default=0, help="Optional hard stop after N cycles; 0 means run until quota is reached")
     parser.add_argument("--print-stats", action="store_true", help="Load outcomes, sync, print stats report, and exit")
-    parser.add_argument("--disable-queue-cleanup", action="store_true", help=argparse.SUPPRESS)
+    parser.add_argument("--disable-queue-cleanup", action="store_true",
+                        default=os.getenv("MODELHUB_COEXISTENCE_MODE", "1") != "0",
+                        help=argparse.SUPPRESS)
     parser.add_argument(
         "--queue-cleanup-interval-cycles",
         type=int,
@@ -702,6 +704,11 @@ def run_poll_loop(
     STATS_PRINT_INTERVAL = 10
 
     log(f"[poll] version={AGENT_VERSION} poll_run_dir={poll_run_dir}")
+    log(
+        f"[coexistence] state_branch={base_args.state_sync_branch} "
+        f"queue_cancellation={'disabled' if base_args.disable_queue_cleanup else 'enabled'} "
+        "submission_preflight=enabled"
+    )
     log(
         "[state-retention] mode=decision_state_only full_archive=disabled "
         f"recent_outcomes={DEFAULT_RECENT_OUTCOME_LIMIT} "

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]:

modelhub_submmit_api/state_sync.py

@@ -17,14 +17,14 @@ from typing import Any, Iterable
 from dulwich import porcelain
 from dulwich.repo import Repo
 
-from common import read_json, read_jsonl, write_json, write_jsonl
+from common import read_json, read_jsonl, write_json, write_jsonl, runtime_instance_id
 from runner_common import load_key_files
 from version import AGENT_VERSION
 
 
 STATE_SCHEMA_VERSION = 1
 DEFAULT_REMOTE = "https://dev.modelhub.org.cn/CoolBoy/submmit.git"
-DEFAULT_BRANCH = "agent-state"
+DEFAULT_BRANCH = "agent-state-accuracy-v2"
 DEFAULT_BATCH_SIZE = 100
 DEFAULT_HISTORY_DEPTH = 200
 DEFAULT_RECENT_TERMINAL_INTENTS = 300
@@ -32,6 +32,7 @@ DEFAULT_LEDGER_RECORDS = 500
 DEFAULT_CRASH_RECORDS = 50
 
 STATE_OUTCOME_FIELDS = {
+    "agentVersion", "instanceId", "strategyId",
     "taskId", "modelId", "targetGpu", "framework", "taskType", "submitTime",
     "lastSyncTime", "status", "verifyResult", "outcome", "failReason",
     "modelProfile", "failureCode", "failureCategory", "failureScope",
@@ -342,6 +343,7 @@ class StateGitSync:
         retained_terminal = terminal[:DEFAULT_RECENT_TERMINAL_INTENTS]
         discarded = terminal[DEFAULT_RECENT_TERMINAL_INTENTS:]
         compact_fields = {
+            "agentVersion", "instanceId", "strategyId",
             "intentId", "batchId", "status", "createdAt", "completedAt",
             "repoId", "targetGpu", "taskType", "framework", "configSource",
             "configFingerprint", "safeConfigVector", "taskId",
@@ -372,6 +374,9 @@ class StateGitSync:
         config = str(candidate.get("configParams") or "")
         return {
             "intentId": uuid.uuid4().hex,
+            "agentVersion": AGENT_VERSION,
+            "instanceId": runtime_instance_id(),
+            "strategyId": os.getenv("STRATEGY_ID"),
             "batchId": batch_id,
             "status": "pending",
             "createdAt": _utc_now().isoformat(),

modelhub_submmit_api/version.py

@@ -1 +1 @@
-AGENT_VERSION = "2026.09.22.1"
+AGENT_VERSION = "2026.10.18.1"

tests/test_accuracy_audit.py

@@ -0,0 +1,96 @@
+import os
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+from types import SimpleNamespace
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "modelhub_submmit_api"))
+from failure_taxonomy import classify_failure_report
+from market_intelligence import _validated_official_config, MarketIntelligenceManager
+from routing_engine import SuccessFirstRoutingEngine
+from state_sync import StateGitSync, DEFAULT_BRANCH
+from poll_runner import build_parser
+
+
+class AccuracyAuditTests(unittest.TestCase):
+    def config(self, command):
+        return f"framework: vllm_fix_tokenizer\nsut_config:\n  command: {command}\nref_config: {{}}\n"
+
+    def test_broken_official_and_cached_config_rejected(self):
+        bad = self.config('["/opt/entrypoint.sh    - /model", "--port", "80"]')
+        for gpu in ("Biren_166m", "Kunlunxin_p-800"):
+            self.assertEqual((False, "malformed_command_argv"), _validated_official_config(
+                bad, framework="vllm_fix_tokenizer", target_gpu=gpu))
+            with tempfile.TemporaryDirectory() as tmp:
+                manager = MarketIntelligenceManager(Path(tmp) / "market.json")
+                manager.state = {"frameworkStats": {"text-generation": {gpu: {
+                    "vllm_fix_tokenizer": {"officialConfigValid": True, "officialConfig": bad}}}}}
+                self.assertIsNone(manager.official_config(task_type="text-generation", target_gpu=gpu,
+                                                        framework="vllm_fix_tokenizer"))
+
+    def test_separate_argv_and_shell_command_remain_valid(self):
+        for command in ('["/opt/entrypoint.sh", "/model"]', '["/bin/sh", "-c", "echo hello"]'):
+            self.assertEqual((True, None), _validated_official_config(
+                self.config(command), framework="vllm_fix_tokenizer", target_gpu="Biren_166m"))
+
+    def test_specific_failure_survives_transient_resource_error(self):
+        result = classify_failure_report("MODEL_NOT_SUPPORTED", [
+            "找不到空闲卡", "当前框架版本不支持该模型架构"])
+        self.assertEqual("framework_architecture_unsupported", result.category)
+        self.assertEqual("tokenizer_compatibility", classify_failure_report(
+            "TOKENIZER_FAILED", ["no idle gpu", "Tokenizer class missing"]).category)
+        self.assertEqual("platform_infrastructure", classify_failure_report(
+            "EXECUTE_EMPTY_RESULT", ["no idle gpu"]).category)
+        self.assertEqual("framework_architecture_unsupported", classify_failure_report(
+            None, ["no idle gpu", "Model architectures ['NewForCausalLM'] are not supported for now"]).category)
+
+    def test_launch_and_quantization_have_specific_categories(self):
+        self.assertEqual("configuration_launch", classify_failure_report("EXECUTE_EMPTY_RESULT", [
+            'exec: "/opt/entrypoint.sh    - /model": no such file or directory']).category)
+        self.assertEqual("quantization_compatibility", classify_failure_report("EXECUTE_EMPTY_RESULT", [
+            "The quantization method compressed-tensors is not supported for the current GPU"]).category)
+
+    def test_sparse_failures_lower_score_even_without_classification(self):
+        candidate = {"targetGpu": "gpu", "framework": "vllm", "taskType": "text-generation",
+                     "frameworkMarketSamples": 1000, "frameworkMarketSuccessRate": 0.8,
+                     "preflightMetadata": {"modelType": "family", "estimatedLoadBytes": 1024}}
+        with tempfile.TemporaryDirectory() as tmp:
+            engine = SuccessFirstRoutingEngine(Path(tmp) / "route.json")
+            prior = engine._evidence(candidate)[3]
+            engine.outcome_stats = {"profileCombinationStats": {
+                "gpu|vllm|text-generation|family|none": {
+                    "successCount": 0, "failureCount": 5, "decisionTotal": 0}}}
+            evidence = engine._evidence(candidate)
+            self.assertEqual("local_profile_shrunk", evidence[0])
+            self.assertLess(evidence[3], prior)
+
+    def test_coexistence_defaults_and_submission_provenance(self):
+        with patch.dict(os.environ, {"MODELHUB_COEXISTENCE_MODE": "1", "STRATEGY_ID": "new-agent"}):
+            self.assertTrue(build_parser().parse_args([]).disable_queue_cleanup)
+            intent = StateGitSync._intent({"repoId": "owner/model"}, "batch")
+            self.assertEqual("new-agent", intent["strategyId"])
+            self.assertTrue(intent["agentVersion"])
+            self.assertTrue(intent["instanceId"])
+        self.assertNotEqual("agent-state", DEFAULT_BRANCH)
+
+    def test_model_specific_failures_change_framework_selection(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            manager = MarketIntelligenceManager(Path(tmp) / "market.json")
+            manager.state = {"frameworkStats": {"text-generation": {"gpu": {
+                "first": {"modelCount": 1000, "wilsonLowerBound": 0.8},
+                "second": {"modelCount": 1000, "wilsonLowerBound": 0.6},
+            }}}}
+            manager.set_local_outcome_stats({"profileCombinationStats": {
+                "gpu|first|text-generation|family|none": {"successCount": 0, "failureCount": 4}
+            }})
+            with patch.object(manager, "compatible_discovered_frameworks", return_value=[]):
+                ranked = manager.selectable_frameworks(
+                    task_type="text-generation", target_gpu="gpu", incumbent_frameworks=["first", "second"],
+                    inspection=SimpleNamespace(model_type="family", quantization_method=None))
+            self.assertEqual(["second", "first"], ranked)
+
+
+if __name__ == "__main__":
+    unittest.main()

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_market_intelligence.py

@@ -283,6 +283,7 @@ class MarketIntelligenceTests(unittest.TestCase):
                         "successCount": 10,
                         "failureCount": 3,
                         "attributableFailureCount": 0,
+                        "platformFailureCount": 3,
                     }
                 },
                 "recentCombinationStats": {
@@ -290,6 +291,7 @@ class MarketIntelligenceTests(unittest.TestCase):
                         "successCount": 10,
                         "failureCount": 3,
                         "attributableFailureCount": 0,
+                        "platformFailureCount": 3,
                         "consecutiveFailures": 0,
                         "consecutivePlatformFailures": 3,
                         "lastPlatformFailureAt": datetime.now(timezone.utc).isoformat(),

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]