feat: dynamically clean incompatible architectures

README.md

@@ -55,6 +55,8 @@ Optional tuning:
 - `MODELHUB_QUEUE_CLEANUP_INTERVAL_CYCLES` default `120`; cleanup also runs once at startup
 - `MODELHUB_QUEUE_CLEANUP_READ_CONCURRENCY` default `6`
 - `MODELHUB_QUEUE_CLEANUP_REPORT_PATH` default `.modelhub_state/queue_cleanup_latest.json`
+- `MODELHUB_ARCHITECTURE_BLACKLIST_PATH` default `.modelhub_state/architecture_compatibility_blacklist.json`
+- `MODELHUB_ARCHITECTURE_BLOCK_TTL_DAYS` default `30`
 - `MODELHUB_RECENT_MODEL_RESERVE_SLOTS` default `10` per account
 - `MODELHUB_DYNAMIC_OLD_MODEL_CLEANUP_RESERVE_SLOTS` default `5` per account
 - `MODELHUB_RECENT_MODEL_DAYS` default `7`
@@ -115,13 +117,14 @@ says that the selected framework does not support the model or architecture, the
 runner learns an exact GPU + framework + task type + architecture block from the
 candidate repository's `config.json`. Repository names are never used as
 architecture evidence. Exact `architectures` values take priority and
-`model_type` is used only when `architectures` is absent; missing metadata does
+`model_type` is also retained when the runtime explicitly says Transformers does
-not create a block. Generic unsupported operators, attention backends, GPU types,
+not recognize that type; missing metadata does not create a block. Generic unsupported operators, attention backends, GPU types,
 quantization failures, and OOMs cannot enter this blacklist. A newer success for
 the same exact combination clears the block, and otherwise it expires after 30
 days. Set `MODELHUB_ARCHITECTURE_BLOCK_TTL_DAYS` to a value from 1 to 365 to
 change that window. The stats report exposes `architectureCompatibilityBlocks`
-and per-GPU/framework block counts.
+and per-GPU/framework block counts. The live snapshot is written to
+`.modelhub_state/architecture_compatibility_blacklist.json`.
 
 Before a candidate reaches the submit queue, failure-informed preflight checks
 the actual ModelScope repository structure and file sizes. Non-GGUF text
@@ -138,7 +141,7 @@ capacities with
 `MODELHUB_GPU_MEMORY_GIB_JSON`, for example
 `{"New_gpu": 64}`.
 
-At poller startup, the same deterministic memory gate is applied to existing
+At poller startup, the same deterministic memory and learned architecture gates are applied to existing
 `waiting` and `running` tasks across every configured account. A task is stopped
 through `PUT /api/async/task/stop-create-contest-task` only when its own current
 recursive repository size, multiplied by ModelHub's observed `1.20` overhead,
@@ -151,6 +154,16 @@ same model or infer failure from historical similarity. The cleanup repeats
 every 120 poll cycles by default and writes its full evidence report to
 `.modelhub_state/queue_cleanup_latest.json`.
 
+Architecture cleanup joins each active task to the locally recorded submission
+or ledger entry to recover its exact framework and task type, then reads the
+model's `config.json`. Only an exact GPU + framework + task type + architecture
+blacklist hit can authorize cancellation. Matching waiting tasks are stopped;
+running tasks remain protected and their state is rechecked again immediately
+before the stop call. Failed outcomes are synchronized every three poll cycles.
+When fixed `MODEL_NOT_SUPPORTED` text adds a new blacklist entry, a lightweight
+architecture-only cleanup runs immediately without repeating repository-size or
+model-age scans.
+
 Each account dynamically reserves its last 10 known-capacity positions for
 models updated within seven days. If an account's discovered limit is 100, 200,
 or 500, older models stop at positions 90, 190, or 490 respectively. Old-model
@@ -270,12 +283,15 @@ Version `2026.08.12.2` learns conservative, expiring GPU/framework/architecture
 compatibility blocks only from explicit ModelHub failure text, matches candidate
 `config.json` metadata instead of repository names, and lets newer success
 evidence clear stale blocks.
+Version `2026.08.12.3` extracts unsupported `model_type`/`architectures` from the
+platform's fixed failure wording, persists a dynamically growing blacklist, and
+immediately removes exact-matching waiting tasks with two active-state checks.
 
 ## Deploy
 
 Create a tag and submit the repository URL plus tag in "我的适配智能体".
 
 ```bash
-git tag agent-v20
+git tag agent-v21
-git push origin agent-v20
+git push origin agent-v21
 ```

modelhub_submmit_api/README.md

@@ -16,7 +16,7 @@ It currently supports:
 - `main.py`: core discovery, scoring, dedup, and submission
 - `daily_runner.py`: daily wave orchestration
 - `poll_runner.py`: long-running queue refiller
-- `queue_cleanup.py`: fail-closed cleanup for active tasks that are certain to exceed GPU memory
+- `queue_cleanup.py`: fail-closed cleanup for certain OOM, architecture, and age policies
 - `runner_common.py`: shared token / key file loading
 - `hf_discovery.py`: ModelScope model discovery and inspection (keeps the legacy module name)
 - `modelhub_client.py`: ModelHub API client and token-pool routing
@@ -96,9 +96,13 @@ bash run_poll.sh --dry-run
   sub-20% 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` (`architectures`, with `model_type` only as
+  comes from candidate `config.json` plus exact unsupported `model_type` or
-  fallback), never from repository names. A newer success clears the block, and
+  `architectures` strings in the runtime log, never from repository names. A newer success clears the block, and
   generic unsupported backend/operator messages cannot create one.
+- Blacklist additions are persisted and detected every three poll cycles. A new
+  rule immediately launches a lightweight architecture-only queue scan. Exact
+  matching waiting tasks are stopped after two state checks; running tasks and
+  tasks without local framework/task metadata are protected.
 - A strategy generation lasts exactly 200 platform-accepted submissions. Rejected API calls and
   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
@@ -227,6 +231,7 @@ Persistent local scheduler state is written under `.modelhub_state/`:
 - `account_capacity.json`: learned per-account active-task limits
 - `submission_exclusions.jsonl`: non-retryable model/GPU uniqueness rejections
 - `queue_cleanup_latest.json`: latest active-task sizing evidence and cancellation result
+- `architecture_compatibility_blacklist.json`: current dynamic compatibility blocks and evidence
 
 ## Verification
 

modelhub_submmit_api/architecture_compatibility.py

@@ -15,23 +15,37 @@ def architecture_profile(
     architectures: Any,
 ) -> dict[str, Any] | None:
     """Build a stable, conservative architecture identity for feedback matching."""
+    profiles = architecture_profiles(model_type, architectures)
+    return profiles[0] if profiles else None
+
+
+def architecture_profiles(
+    model_type: Any,
+    architectures: Any,
+) -> list[dict[str, Any]]:
+    """Return exact architecture identity first, followed by model-type fallback."""
     normalized_architectures = _normalize_architectures(architectures)
     normalized_model_type = _normalize(model_type)
+    profiles: list[dict[str, Any]] = []
     if normalized_architectures:
-        return {
+        profiles.append(
+            {
                 "matchType": "architectures",
                 "signature": "architectures:" + ",".join(normalized_architectures),
                 "architectures": normalized_architectures,
                 "modelType": normalized_model_type or None,
             }
+        )
     if normalized_model_type:
-        return {
+        profiles.append(
+            {
                 "matchType": "model_type",
                 "signature": f"model_type:{normalized_model_type}",
                 "architectures": [],
                 "modelType": normalized_model_type,
             }
-    return None
+        )
+    return profiles
 
 
 def architecture_compatibility_key(

modelhub_submmit_api/candidate_preflight.py

@@ -8,7 +8,7 @@ from dataclasses import dataclass
 from datetime import timedelta
 from typing import Any
 
-from architecture_compatibility import architecture_compatibility_key, architecture_profile
+from architecture_compatibility import architecture_compatibility_key, architecture_profiles
 from llm_classifier import LLMAssistedClassifier
 from models import ModelInspection
 from common import parse_datetime, utc_now
@@ -422,9 +422,7 @@ class CandidatePreflightAdvisor:
         framework: str,
         task_type: str,
     ) -> dict[str, Any] | None:
-        profile = architecture_profile(inspection.model_type, inspection.architectures)
+        for profile in architecture_profiles(inspection.model_type, inspection.architectures):
-        if profile is None:
-            return None
             key = architecture_compatibility_key(
                 target_gpu,
                 framework,
@@ -432,14 +430,15 @@ class CandidatePreflightAdvisor:
                 profile["signature"],
             )
             if key is None:
-            return None
+                continue
             block = self._architecture_compatibility_blocks.get(key)
             if not isinstance(block, dict):
-            return None
+                continue
             expires_at = parse_datetime(block.get("expiresAt"))
             if expires_at is None or expires_at <= utc_now():
-            return None
+                continue
             return dict(block)
+        return None
 
     def summary(self) -> dict[str, Any]:
         with self._lock:

modelhub_submmit_api/failure_log_inspector.py

@@ -22,6 +22,16 @@ ERROR_LINE_PATTERN = re.compile(
     r"找不到空闲卡|不支持|暂不支持|不兼容|请换用|请更换)",
     re.IGNORECASE,
 )
+MODEL_TYPE_NOT_RECOGNIZED_PATTERN = re.compile(
+    r"model\s+type\s+[`'\"](?P<model_type>[A-Za-z0-9_.-]+)[`'\"]\s+but\s+"
+    r"(?:Transformers\s+)?does\s+not\s+recognize\s+this\s+architecture",
+    re.IGNORECASE,
+)
+MODEL_ARCHITECTURES_NOT_SUPPORTED_PATTERN = re.compile(
+    r"Model\s+architectures?\s*(?P<architectures>\[[^\]\n]{1,500}\])\s+"
+    r"(?:are|is)\s+not\s+supported\s+for\s+now",
+    re.IGNORECASE,
+)
 
 
 def fetch_and_classify_failure_log(
@@ -94,6 +104,13 @@ def classify_failure_archive(
     observed_memory_gib = _extract_observed_gpu_memory_gib(error_lines)
     if report_code == "PREFLIGHT_OOM" and observed_memory_gib is not None:
         result["failureObservedGpuMemoryGiB"] = observed_memory_gib
+    unsupported_architectures, unsupported_model_types = _extract_unsupported_architectures(
+        error_lines
+    )
+    if unsupported_architectures:
+        result["failureUnsupportedArchitectures"] = unsupported_architectures
+    if unsupported_model_types:
+        result["failureUnsupportedModelTypes"] = unsupported_model_types
     if classification.needs_llm and llm_classifier is not None and llm_classifier.enabled:
         llm_decision = llm_classifier.classify_failure(
             task_context=dict(task_context or {}),
@@ -146,3 +163,21 @@ def _extract_observed_gpu_memory_gib(error_lines: list[str]) -> float | None:
             if 0 < value <= 1024:
                 return value
     return None
+
+
+def _extract_unsupported_architectures(
+    error_lines: list[str],
+) -> tuple[list[str], list[str]]:
+    architectures: set[str] = set()
+    model_types: set[str] = set()
+    for line in error_lines:
+        for match in MODEL_TYPE_NOT_RECOGNIZED_PATTERN.finditer(line):
+            value = match.group("model_type").strip()
+            if value:
+                model_types.add(value)
+        for match in MODEL_ARCHITECTURES_NOT_SUPPORTED_PATTERN.finditer(line):
+            for value in re.findall(r"['\"]([^'\"]+)['\"]", match.group("architectures")):
+                value = value.strip()
+                if value:
+                    architectures.add(value)
+    return sorted(architectures, key=str.casefold), sorted(model_types, key=str.casefold)

modelhub_submmit_api/outcome_tracker.py

@@ -13,6 +13,7 @@ from architecture_compatibility import (
     EXPLICIT_ARCHITECTURE_FAILURE_REASON,
     architecture_compatibility_key,
     architecture_profile,
+    architecture_profiles,
 )
 from common import append_jsonl, parse_datetime, read_jsonl, update_jsonl, utc_now
 from failure_log_inspector import fetch_and_classify_failure_log
@@ -53,6 +54,26 @@ class OutcomeTracker:
     def set_failure_llm_classifier(self, classifier: LLMAssistedClassifier | None) -> None:
         self._failure_llm_classifier = classifier
 
+    def get_task_compatibility_contexts(self) -> dict[str, dict[str, Any]]:
+        """Return locally known submit metadata needed for account queue cleanup."""
+        contexts: dict[str, dict[str, Any]] = {}
+        for task_id, record in self._by_task_id.items():
+            framework = str(record.get("framework") or "").strip()
+            task_type = str(record.get("taskType") or "").strip()
+            if not framework or not task_type:
+                continue
+            profile = record.get("modelProfile")
+            contexts[task_id] = {
+                "taskId": task_id,
+                "modelId": str(record.get("modelId") or ""),
+                "targetGpu": str(record.get("targetGpu") or ""),
+                "framework": framework,
+                "taskType": task_type,
+                "modelProfile": dict(profile) if isinstance(profile, dict) else {},
+                "submitTime": record.get("submitTime"),
+            }
+        return contexts
+
     def _rebuild_indexes(self) -> None:
         self._by_task_id.clear()
         self._by_model_gpu.clear()
@@ -176,9 +197,10 @@ class OutcomeTracker:
                     self._by_model_gpu[(model_id, target_gpu)].append(record)
                     updated_count += 1
 
-        # Retry a small bounded set of our own failed submissions. Historical
+        # Retry a small bounded set of our own failed submissions. A stored
-        # tasks without the locally recorded framework/profile are intentionally
+        # framework is sufficient: fixed MODEL_NOT_SUPPORTED log text can yield
-        # excluded to avoid downloading thousands of old log archives at once.
+        # an exact architecture/model_type even for older records that predate
+        # local modelProfile capture.
         candidate_ids = {id(record) for record in enrichment_candidates}
         for record in self._records:
             if len(enrichment_candidates) >= FAILURE_ENRICHMENT_LIMIT:
@@ -189,7 +211,6 @@ class OutcomeTracker:
                 record.get("outcome") == "failed"
                 and record.get("logCosUrl")
                 and record.get("framework")
-                and record.get("modelProfile")
                 and not record.get("failureCategory")
                 and int(record.get("failureEnrichmentAttempts") or 0) < FAILURE_ENRICHMENT_MAX_ATTEMPTS
             ):
@@ -537,35 +558,36 @@ def _build_architecture_compatibility_blocks(
     cutoff = now - timedelta(days=max(1, int(ttl_days)))
 
     for record in records:
-        profile_data = record.get("modelProfile")
-        if not isinstance(profile_data, dict):
-            continue
-        profile = architecture_profile(
-            profile_data.get("modelType"),
-            profile_data.get("architectures"),
-        )
-        if profile is None:
-            continue
         target_gpu = str(record.get("targetGpu") or "").strip()
         framework = str(record.get("framework") or "").strip()
         task_type = str(record.get("taskType") or "").strip()
+        event_time = (
+            parse_datetime(record.get("submitTime"))
+            or parse_datetime(record.get("lastSyncTime"))
+        )
+        if not target_gpu or not framework or not task_type or event_time is None:
+            continue
+        if record.get("outcome") == "success":
+            for profile in _stored_architecture_profiles(record, include_model_type=True):
                 key = architecture_compatibility_key(
                     target_gpu,
                     framework,
                     task_type,
                     profile["signature"],
                 )
-        event_time = (
+                if key is not None:
-            parse_datetime(record.get("submitTime"))
-            or parse_datetime(record.get("lastSyncTime"))
-        )
-        if key is None or event_time is None:
-            continue
-        if record.get("outcome") == "success":
                     successes[key].append((event_time, record))
             continue
         if not _is_explicit_architecture_failure(record) or event_time < cutoff:
             continue
+        for profile in _failure_architecture_profiles(record):
+            key = architecture_compatibility_key(
+                target_gpu,
+                framework,
+                task_type,
+                profile["signature"],
+            )
+            if key is not None:
                 failures[key].append((event_time, record, profile))
 
     blocks: dict[str, dict[str, Any]] = {}
@@ -606,6 +628,47 @@ def _build_architecture_compatibility_blocks(
     return blocks
 
 
+def _stored_architecture_profiles(
+    record: dict[str, Any],
+    *,
+    include_model_type: bool,
+) -> list[dict[str, Any]]:
+    profile_data = record.get("modelProfile")
+    if not isinstance(profile_data, dict):
+        return []
+    if include_model_type:
+        return architecture_profiles(
+            profile_data.get("modelType"),
+            profile_data.get("architectures"),
+        )
+    profile = architecture_profile(
+        profile_data.get("modelType"),
+        profile_data.get("architectures"),
+    )
+    return [profile] if profile is not None else []
+
+
+def _failure_architecture_profiles(record: dict[str, Any]) -> list[dict[str, Any]]:
+    profiles: list[dict[str, Any]] = []
+    unsupported_architectures = record.get("failureUnsupportedArchitectures")
+    if isinstance(unsupported_architectures, list) and unsupported_architectures:
+        profile = architecture_profile(None, unsupported_architectures)
+        if profile is not None:
+            profiles.append(profile)
+    unsupported_model_types = record.get("failureUnsupportedModelTypes")
+    if isinstance(unsupported_model_types, list):
+        for model_type in unsupported_model_types:
+            profile = architecture_profile(model_type, None)
+            if profile is not None:
+                profiles.append(profile)
+    if not profiles:
+        profiles.extend(_stored_architecture_profiles(record, include_model_type=False))
+    deduped: dict[str, dict[str, Any]] = {}
+    for profile in profiles:
+        deduped[profile["signature"]] = profile
+    return list(deduped.values())
+
+
 def _is_explicit_architecture_failure(record: dict[str, Any]) -> bool:
     return bool(
         record.get("outcome") == "failed"

modelhub_submmit_api/poll_runner.py

@@ -8,7 +8,7 @@ import time
 from pathlib import Path
 from typing import Any, Callable
 
-from common import utc_now, write_json
+from common import read_jsonl, utc_now, write_json
 from daily_runner import DEFAULT_DAILY_RUNS_DIR, log, run_daily_batches
 from gpu_strategy import DEFAULT_GPU_STRATEGY_PATH
 from hf_discovery import HuggingFaceDiscovery
@@ -31,6 +31,9 @@ from version import AGENT_VERSION
 
 
 DEFAULT_POLL_RUNS_DIR = Path("poll_runs")
+DEFAULT_ARCHITECTURE_BLACKLIST_PATH = Path(
+    ".modelhub_state/architecture_compatibility_blacklist.json"
+)
 
 
 def build_parser() -> argparse.ArgumentParser:
@@ -204,6 +207,14 @@ def build_parser() -> argparse.ArgumentParser:
         default=os.getenv("MODELHUB_QUEUE_CLEANUP_REPORT_PATH", ".modelhub_state/queue_cleanup_latest.json"),
         help=argparse.SUPPRESS,
     )
+    parser.add_argument(
+        "--architecture-blacklist-path",
+        default=os.getenv(
+            "MODELHUB_ARCHITECTURE_BLACKLIST_PATH",
+            str(DEFAULT_ARCHITECTURE_BLACKLIST_PATH),
+        ),
+        help=argparse.SUPPRESS,
+    )
     return parser
 
 
@@ -251,6 +262,53 @@ def resolve_age_cleanup_policy(
     return "dynamic", dynamic_reserve_slots
 
 
+def _persist_architecture_blacklist(
+    report: dict[str, Any],
+    *,
+    path: Path,
+) -> set[str]:
+    blocks = report.get("architectureCompatibilityBlocks") or {}
+    blocks = blocks if isinstance(blocks, dict) else {}
+    write_json(
+        path,
+        {
+            "generatedAt": report.get("generatedAt"),
+            "summary": report.get("architectureCompatibilitySummary") or {},
+            "blocks": blocks,
+        },
+    )
+    return set(str(key) for key in blocks)
+
+
+def _load_task_compatibility_contexts(
+    outcome_tracker: OutcomeTracker,
+    *,
+    ledger_path: Path,
+) -> dict[str, dict[str, Any]]:
+    contexts = outcome_tracker.get_task_compatibility_contexts()
+    for record in read_jsonl(ledger_path):
+        task_id_value = record.get("taskId")
+        if task_id_value is None:
+            continue
+        task_id = str(task_id_value)
+        existing = contexts.get(task_id)
+        if existing is None:
+            contexts[task_id] = {
+                "taskId": task_id,
+                "modelId": str(record.get("modelId") or ""),
+                "targetGpu": str(record.get("targetGpu") or ""),
+                "framework": str(record.get("framework") or ""),
+                "taskType": str(record.get("taskType") or ""),
+                "modelProfile": {},
+                "submitTime": record.get("submitTime"),
+            }
+            continue
+        for field in ("modelId", "targetGpu", "framework", "taskType", "submitTime"):
+            if not existing.get(field) and record.get(field):
+                existing[field] = record.get(field)
+    return contexts
+
+
 def run_poll_loop(
     *,
     base_args: argparse.Namespace,
@@ -286,6 +344,8 @@ def run_poll_loop(
     cycles = 0
     stopped_reason = "max_cycles_reached"
     initial_age_cleanup_pending = True
+    pending_architecture_cleanup = False
+    last_cleaned_architecture_blocks: set[str] = set()
 
     while True:
         if base_args.max_cycles and cycles >= base_args.max_cycles:
@@ -296,22 +356,90 @@ def run_poll_loop(
         if hasattr(modelhub_client, "configure_capacity_probe"):
             modelhub_client.configure_capacity_probe(cycles)
 
+        outcome_synced_this_cycle = False
+        if (
+            cycles % OUTCOME_SYNC_INTERVAL == 0
+            and not getattr(base_args, "skip_outcome_sync", False)
+        ):
+            try:
+                synced = outcome_tracker.sync_from_api(modelhub_client)
+                outcome_synced_this_cycle = True
+                if synced > 0:
+                    log(f"[poll] cycle={cycles} outcome_sync_updated={synced}")
+                sync_feedback = outcome_tracker.get_stats_report()
+                active_block_keys = _persist_architecture_blacklist(
+                    sync_feedback,
+                    path=Path(
+                        getattr(
+                            base_args,
+                            "architecture_blacklist_path",
+                            DEFAULT_ARCHITECTURE_BLACKLIST_PATH,
+                        )
+                    ),
+                )
+                new_block_keys = active_block_keys - last_cleaned_architecture_blocks
+                if (
+                    new_block_keys
+                    and not bool(getattr(base_args, "disable_queue_cleanup", False))
+                    and isinstance(modelhub_client, ModelHubClientPool)
+                ):
+                    pending_architecture_cleanup = True
+                    log(
+                        f"[queue-cleanup] dynamic_architecture_blocks_added={len(new_block_keys)} "
+                        f"cleanup_next=immediate"
+                    )
+            except Exception as exc:
+                log(f"[poll] cycle={cycles} outcome_sync_error={exc}")
+
         cleanup_interval = max(0, int(getattr(base_args, "queue_cleanup_interval_cycles", 120) or 0))
+        scheduled_queue_cleanup = bool(
+            cycles == 1 or (cleanup_interval > 0 and cycles % cleanup_interval == 0)
+        )
+        architecture_only_cleanup = bool(
+            pending_architecture_cleanup and not scheduled_queue_cleanup
+        )
         should_cleanup_queue = (
             not bool(getattr(base_args, "disable_queue_cleanup", False))
             and isinstance(modelhub_client, ModelHubClientPool)
-            and (cycles == 1 or (cleanup_interval > 0 and cycles % cleanup_interval == 0))
+            and (
+                scheduled_queue_cleanup
+                or pending_architecture_cleanup
+            )
         )
         if should_cleanup_queue:
             try:
+                if (
+                    not outcome_synced_this_cycle
+                    and not getattr(base_args, "skip_outcome_sync", False)
+                ):
+                    synced_before_cleanup = outcome_tracker.sync_from_api(modelhub_client)
+                    if synced_before_cleanup:
+                        log(
+                            f"[queue-cleanup] outcome_sync_updated={synced_before_cleanup}"
+                        )
                 cleanup_feedback = outcome_tracker.get_stats_report()
                 cleanup_gpu_memory = cleanup_feedback.get("observedGpuMemoryGiB") or {}
+                cleanup_architecture_blocks = (
+                    cleanup_feedback.get("architectureCompatibilityBlocks") or {}
+                )
+                active_architecture_block_keys = _persist_architecture_blacklist(
+                    cleanup_feedback,
+                    path=Path(
+                        getattr(
+                            base_args,
+                            "architecture_blacklist_path",
+                            DEFAULT_ARCHITECTURE_BLACKLIST_PATH,
+                        )
+                    ),
+                )
+                if active_architecture_block_keys - last_cleaned_architecture_blocks:
+                    pending_architecture_cleanup = True
                 age_cleanup_mode, age_cleanup_reserve_slots = resolve_age_cleanup_policy(
                     base_args,
                     initial_cleanup_pending=initial_age_cleanup_pending,
                 )
                 log(
-                    f"[queue-cleanup] mode={age_cleanup_mode} "
+                    f"[queue-cleanup] mode={'architecture_dynamic' if architecture_only_cleanup else age_cleanup_mode} "
                     f"reserve_recent_slots={age_cleanup_reserve_slots} "
                     f"recent_days={max(1, int(getattr(base_args, 'recent_model_days', 7) or 7))}"
                 )
@@ -321,6 +449,18 @@ def run_poll_loop(
                     dry_run=bool(base_args.dry_run),
                     read_concurrency=max(1, int(getattr(base_args, "queue_cleanup_read_concurrency", 6) or 6)),
                     gpu_memory_gib=cleanup_gpu_memory if isinstance(cleanup_gpu_memory, dict) else None,
+                    architecture_compatibility_blocks=(
+                        cleanup_architecture_blocks
+                        if isinstance(cleanup_architecture_blocks, dict)
+                        else None
+                    ),
+                    task_compatibility_contexts=(
+                        _load_task_compatibility_contexts(
+                            outcome_tracker,
+                            ledger_path=Path(base_args.ledger_path),
+                        )
+                    ),
+                    architecture_only=architecture_only_cleanup,
                     age_reserved_slots=age_cleanup_reserve_slots,
                     log=log,
                 )
@@ -343,18 +483,31 @@ def run_poll_loop(
                 queue_cleanup_runs.append(
                     {
                         "cycle": cycles,
-                        "mode": age_cleanup_mode,
+                        "mode": (
+                            "architecture_dynamic"
+                            if architecture_only_cleanup
+                            else age_cleanup_mode
+                        ),
                         "ageReservedSlots": age_cleanup_reserve_slots,
                         "ageQueueThresholds": cleanup_summary["oldModelQueueThresholds"],
                         "activeScanned": cleanup_summary["activeScanned"],
                         "certainOomCount": cleanup_summary["certainOomCount"],
+                        "architectureBlockCount": cleanup_summary[
+                            "architectureBlockCount"
+                        ],
+                        "architectureIncompatibleCount": cleanup_summary[
+                            "architectureIncompatibleCount"
+                        ],
                         "oldOverflowCount": cleanup_summary["oldOverflowCount"],
                         "cancelledCount": cleanup_summary["cancelledCount"],
                         "policyCancelledRecorded": policy_cancelled_recorded,
                         "stopErrorCount": len(cleanup_summary["stopErrors"]),
                     }
                 )
+                if not architecture_only_cleanup:
                     initial_age_cleanup_pending = False
+                pending_architecture_cleanup = False
+                last_cleaned_architecture_blocks = active_architecture_block_keys
             except Exception as exc:
                 log(f"[queue-cleanup] error={type(exc).__name__}: {exc} continue_polling=true")
 
@@ -410,14 +563,6 @@ def run_poll_loop(
                 time.sleep(base_args.idle_interval_seconds)
             continue
 
-        if cycles % OUTCOME_SYNC_INTERVAL == 0:
-            try:
-                synced = outcome_tracker.sync_from_api(modelhub_client)
-                if synced > 0:
-                    log(f"[poll] cycle={cycles} outcome_sync_updated={synced}")
-            except Exception as exc:
-                log(f"[poll] cycle={cycles} outcome_sync_error={exc}")
-
         if cycles % STATS_PRINT_INTERVAL == 0:
             try:
                 stats = outcome_tracker.get_stats_report()

modelhub_submmit_api/queue_cleanup.py

@@ -7,6 +7,7 @@ from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Any, Callable, Iterable
 
+from architecture_compatibility import architecture_compatibility_key, architecture_profiles
 from candidate_preflight import CandidatePreflightAdvisor, MODEL_LOAD_OVERHEAD
 from common import utc_now, write_json
 from hf_discovery import HuggingFaceDiscovery, inspect_repo_tree
@@ -195,6 +196,43 @@ def _load_model_last_modified(
     return values, errors
 
 
+def _load_model_configs(
+    model_ids: set[str],
+    *,
+    discovery: HuggingFaceDiscovery,
+    read_concurrency: int,
+    log: Callable[[str], None],
+) -> tuple[dict[str, dict[str, Any]], dict[str, str]]:
+    configs: dict[str, dict[str, Any]] = {}
+    errors: dict[str, str] = {}
+    if not model_ids:
+        return configs, errors
+    completed = 0
+    workers = min(max(1, int(read_concurrency)), len(model_ids))
+    with ThreadPoolExecutor(max_workers=workers) as executor:
+        futures = {
+            executor.submit(discovery.get_model_config, model_id): model_id
+            for model_id in sorted(model_ids)
+        }
+        for future in as_completed(futures):
+            model_id = futures[future]
+            try:
+                config, fetch_error = future.result()
+                if fetch_error or not isinstance(config, dict) or not config:
+                    errors[model_id] = str(fetch_error or "model_config_empty")
+                else:
+                    configs[model_id] = dict(config)
+            except Exception as exc:
+                errors[model_id] = f"{type(exc).__name__}: {exc}"
+            completed += 1
+            if completed == len(model_ids) or completed % 50 == 0:
+                log(
+                    f"[queue-cleanup] architecture_scan={completed}/{len(model_ids)} "
+                    f"complete={len(configs)} unknown={len(errors)}"
+                )
+    return configs, errors
+
+
 def find_certain_oom_tasks(
     tasks: list[OwnedTask],
     *,
@@ -238,6 +276,94 @@ def find_certain_oom_tasks(
     return decisions, skipped
 
 
+def find_architecture_incompatible_tasks(
+    tasks: list[OwnedTask],
+    *,
+    architecture_blocks: dict[str, dict[str, Any]],
+    task_contexts: dict[str, dict[str, Any]],
+    model_configs: dict[str, dict[str, Any]],
+) -> tuple[list[dict[str, Any]], dict[str, int]]:
+    """Select waiting tasks that exactly match a learned compatibility block."""
+    decisions: list[dict[str, Any]] = []
+    skipped = {
+        "submissionContextUnknown": 0,
+        "submissionContextMismatch": 0,
+        "modelArchitectureUnknown": 0,
+        "noMatchingBlock": 0,
+        "runningMatchedProtected": 0,
+    }
+    if not architecture_blocks:
+        return decisions, skipped
+
+    for task in tasks:
+        context = task_contexts.get(str(task.task_id))
+        if not isinstance(context, dict):
+            skipped["submissionContextUnknown"] += 1
+            continue
+        context_model = str(context.get("modelId") or "").strip()
+        context_gpu = str(context.get("targetGpu") or "").strip()
+        framework = str(context.get("framework") or "").strip()
+        task_type = str(context.get("taskType") or "").strip()
+        if (
+            not framework
+            or not task_type
+            or (context_model and context_model != task.model_id)
+            or (context_gpu and context_gpu.casefold() != task.gpu_type.casefold())
+        ):
+            skipped["submissionContextMismatch"] += 1
+            continue
+
+        profile_data = context.get("modelProfile")
+        if not isinstance(profile_data, dict):
+            profile_data = {}
+        model_type = profile_data.get("modelType")
+        architectures = profile_data.get("architectures")
+        if not model_type and not architectures:
+            config = model_configs.get(task.model_id) or {}
+            model_type = config.get("model_type")
+            architectures = config.get("architectures")
+        profiles = architecture_profiles(model_type, architectures)
+        if not profiles:
+            skipped["modelArchitectureUnknown"] += 1
+            continue
+
+        matching_block: dict[str, Any] | None = None
+        for profile in profiles:
+            key = architecture_compatibility_key(
+                task.gpu_type,
+                framework,
+                task_type,
+                profile["signature"],
+            )
+            block = architecture_blocks.get(key or "")
+            if isinstance(block, dict):
+                matching_block = block
+                break
+        if matching_block is None:
+            skipped["noMatchingBlock"] += 1
+            continue
+        if task.status != "waiting":
+            skipped["runningMatchedProtected"] += 1
+            continue
+        decisions.append(
+            {
+                "accountIndex": task.account_index + 1,
+                "taskId": task.task_id,
+                "modelId": task.model_id,
+                "gpuType": task.gpu_type,
+                "framework": framework,
+                "taskType": task_type,
+                "status": task.status,
+                "architectureSignature": matching_block.get("architectureSignature"),
+                "architectureMatchType": matching_block.get("matchType"),
+                "architectureBlockExpiresAt": matching_block.get("expiresAt"),
+                "architectureBlockEvidenceCount": matching_block.get("evidenceCount"),
+                "reason": "known_framework_architecture_incompatible",
+            }
+        )
+    return decisions, skipped
+
+
 def find_old_overflow_tasks(
     tasks: list[OwnedTask],
     *,
@@ -319,11 +445,14 @@ def cleanup_certain_oom_tasks(
     read_concurrency: int = 6,
     stop_batch_size: int = DEFAULT_STOP_BATCH_SIZE,
     gpu_memory_gib: dict[str, float] | None = None,
+    architecture_compatibility_blocks: dict[str, dict[str, Any]] | None = None,
+    task_compatibility_contexts: dict[str, dict[str, Any]] | None = None,
+    architecture_only: bool = False,
     age_reserved_slots: int | None = None,
     reference_time: datetime | None = None,
     log: Callable[[str], None] = print,
 ) -> dict[str, Any]:
-    """Stop deterministic OOM tasks and old tasks beyond each account's protected prefix."""
+    """Stop deterministic OOM/architecture tasks and over-threshold old tasks."""
     clients = list(modelhub.clients)
     reference_time = reference_time or utc_now()
     configured_reserved_slots = (
@@ -359,6 +488,15 @@ def cleanup_certain_oom_tasks(
     }
 
     model_ids = {task.model_id for task in tasks}
+    repository_sizes: dict[str, int] = {}
+    size_errors: dict[str, str] = {}
+    oom_decisions: list[dict[str, Any]] = []
+    skipped = {
+        "repositorySizeUnknown": 0,
+        "gpuCapacityUnknown": 0,
+        "fitsKnownCapacity": 0,
+    }
+    if not architecture_only:
         repository_sizes, size_errors = _load_repository_sizes(
             model_ids,
             discovery=discovery,
@@ -376,15 +514,71 @@ def cleanup_certain_oom_tasks(
         f"gpu_unknown={skipped['gpuCapacityUnknown']} dry_run={str(bool(dry_run)).lower()}"
     )
 
-    oom_task_keys = {
+    architecture_blocks = (
-        (int(decision["accountIndex"]) - 1, int(decision["taskId"]))
+        architecture_compatibility_blocks
-        for decision in oom_decisions
+        if isinstance(architecture_compatibility_blocks, dict)
+        else {}
+    )
+    task_contexts = (
+        task_compatibility_contexts
+        if isinstance(task_compatibility_contexts, dict)
+        else {}
+    )
+    block_combinations = {
+        (
+            str(block.get("targetGpu") or "").strip().casefold(),
+            str(block.get("framework") or "").strip().casefold(),
+            str(block.get("taskType") or "").strip().casefold(),
+        )
+        for block in architecture_blocks.values()
+        if isinstance(block, dict)
     }
-    # OOM tasks are stopped first. Rank the age-policy queue as it will look
+    architecture_model_ids: set[str] = set()
-    # after those certain failures are gone, so an old task moving into the
+    for task in tasks:
-    # protected first N positions is not over-cancelled.
+        context = task_contexts.get(str(task.task_id))
+        if not isinstance(context, dict):
+            continue
+        combination = (
+            task.gpu_type.casefold(),
+            str(context.get("framework") or "").strip().casefold(),
+            str(context.get("taskType") or "").strip().casefold(),
+        )
+        profile = context.get("modelProfile")
+        profile = profile if isinstance(profile, dict) else {}
+        if combination in block_combinations and not (
+            profile.get("modelType") or profile.get("architectures")
+        ):
+            architecture_model_ids.add(task.model_id)
+    model_configs, model_config_errors = _load_model_configs(
+        architecture_model_ids,
+        discovery=discovery,
+        read_concurrency=read_concurrency,
+        log=log,
+    )
+    architecture_decisions, architecture_skipped = find_architecture_incompatible_tasks(
+        tasks,
+        architecture_blocks=architecture_blocks,
+        task_contexts=task_contexts,
+        model_configs=model_configs,
+    )
+    log(
+        f"[queue-cleanup] architecture_incompatible={len(architecture_decisions)} "
+        f"blocks={len(architecture_blocks)} "
+        f"context_unknown={architecture_skipped['submissionContextUnknown']} "
+        f"architecture_unknown={architecture_skipped['modelArchitectureUnknown']} "
+        f"running_protected={architecture_skipped['runningMatchedProtected']}"
+    )
+
+    deterministic_task_keys = {
+        (int(decision["accountIndex"]) - 1, int(decision["taskId"]))
+        for decision in [*oom_decisions, *architecture_decisions]
+    }
+    # Deterministically impossible tasks are stopped first. Rank the age-policy
+    # queue as it will look afterwards to avoid over-cancelling old models.
     age_rank_tasks = [
-        task for task in tasks if (task.account_index, task.task_id) not in oom_task_keys
+        task
+        for task in tasks
+        if (task.account_index, task.task_id) not in deterministic_task_keys
     ]
     overflow_model_ids = {
         task.model_id
@@ -396,6 +590,18 @@ def cleanup_certain_oom_tasks(
         )[queue_thresholds.get(account_index, len(age_rank_tasks)):]
         if task.status == "waiting"
     }
+    model_last_modified: dict[str, datetime] = {}
+    age_errors: dict[str, str] = {}
+    old_overflow_decisions: list[dict[str, Any]] = []
+    age_skipped = {
+        "accountsWithIncompleteListing": len(listing_errors),
+        "withinFirstQueuePositions": 0,
+        "recentOverflowTasks": 0,
+        "modelAgeUnknown": 0,
+        "accountThresholdUnknown": 0,
+        "runningOverflowProtected": 0,
+    }
+    if not architecture_only:
         model_last_modified, age_errors = _load_model_last_modified(
             overflow_model_ids,
             discovery=discovery,
@@ -420,10 +626,22 @@ def cleanup_certain_oom_tasks(
     )
 
     decisions_by_key: dict[tuple[int, int], dict[str, Any]] = {}
-    for decision in oom_decisions:
+    for decision in [*oom_decisions, *architecture_decisions]:
         enriched = dict(decision)
         enriched["cleanupReasons"] = [decision["reason"]]
-        decisions_by_key[(int(decision["accountIndex"]), int(decision["taskId"]))] = enriched
+        key = (int(decision["accountIndex"]), int(decision["taskId"]))
+        existing = decisions_by_key.get(key)
+        if existing is None:
+            decisions_by_key[key] = enriched
+            continue
+        existing["cleanupReasons"].append(decision["reason"])
+        existing.update(
+            {
+                field: value
+                for field, value in decision.items()
+                if field not in {"reason", "cleanupReasons"} and value is not None
+            }
+        )
     for decision in old_overflow_decisions:
         key = (int(decision["accountIndex"]), int(decision["taskId"]))
         existing = decisions_by_key.get(key)
@@ -455,15 +673,16 @@ def cleanup_certain_oom_tasks(
             active_ids_by_account.setdefault(task.account_index, set()).add(task.task_id)
             active_status_by_account.setdefault(task.account_index, {})[task.task_id] = task.status
         for account_index in range(len(clients)):
-            planned_oom_ids = {
+            planned_deterministic_ids = {
                 int(decision["taskId"])
-                for decision in oom_decisions
+                for decision in [*oom_decisions, *architecture_decisions]
                 if int(decision["accountIndex"]) - 1 == account_index
             }
             ordered_ids = sorted(
                 task.task_id
                 for task in refreshed_tasks
-                if task.account_index == account_index and task.task_id not in planned_oom_ids
+                if task.account_index == account_index
+                and task.task_id not in planned_deterministic_ids
             )
             active_positions_by_account[account_index] = {
                 task_id: position for position, task_id in enumerate(ordered_ids, start=1)
@@ -488,6 +707,10 @@ def cleanup_certain_oom_tasks(
                 continue
             cleanup_reasons = set(decision.get("cleanupReasons") or [decision.get("reason")])
             age_only = cleanup_reasons == {"old_model_beyond_account_queue_threshold"}
+            architecture_without_oom = bool(
+                "known_framework_architecture_incompatible" in cleanup_reasons
+                and "certain_oom_repository_size_exceeds_gpu_capacity" not in cleanup_reasons
+            )
             current_position = active_positions_by_account.get(account_index, {}).get(int(decision["taskId"]))
             current_status = active_status_by_account.get(account_index, {}).get(int(decision["taskId"]))
             account_queue_threshold = queue_thresholds.get(account_index)
@@ -510,6 +733,16 @@ def cleanup_certain_oom_tasks(
                     }
                 )
                 continue
+            if architecture_without_oom and current_status != "waiting":
+                policy_no_longer_applies.append(
+                    {
+                        **decision,
+                        "recheckedQueuePosition": current_position,
+                        "recheckedStatus": current_status,
+                        "policyChangeReason": "task_started_running",
+                    }
+                )
+                continue
             if current_position is not None:
                 decision["recheckedQueuePosition"] = current_position
             by_account.setdefault(account_index, []).append(decision)
@@ -520,17 +753,36 @@ def cleanup_certain_oom_tasks(
             if stop_failed:
                 break
             decisions_by_id = {int(item["taskId"]): item for item in by_account[account_index]}
-            for is_oom_phase in (True, False):
+            for cleanup_phase in ("oom", "architecture", "age"):
                 task_ids = sorted(
                     task_id
                     for task_id, decision in decisions_by_id.items()
-                    if ("certain_oom_repository_size_exceeds_gpu_capacity" in decision["cleanupReasons"])
+                    if (
-                    == is_oom_phase
+                        cleanup_phase == "oom"
+                        and "certain_oom_repository_size_exceeds_gpu_capacity"
+                        in decision["cleanupReasons"]
                     )
-                if not is_oom_phase and task_ids:
+                    or (
-                    # OOM stops can change actual positions, and a waiting task
+                        cleanup_phase == "architecture"
-                    # can start running after the account-wide recheck above.
+                        and "certain_oom_repository_size_exceeds_gpu_capacity"
-                    # Re-read this account immediately before its age-only stop.
+                        not in decision["cleanupReasons"]
+                        and "known_framework_architecture_incompatible"
+                        in decision["cleanupReasons"]
+                    )
+                    or (
+                        cleanup_phase == "age"
+                        and "certain_oom_repository_size_exceeds_gpu_capacity"
+                        not in decision["cleanupReasons"]
+                        and "known_framework_architecture_incompatible"
+                        not in decision["cleanupReasons"]
+                        and "old_model_beyond_account_queue_threshold"
+                        in decision["cleanupReasons"]
+                    )
+                )
+                if cleanup_phase != "oom" and task_ids:
+                    # Earlier deterministic stops can change positions, and a
+                    # waiting architecture/age task can start running after the
+                    # account-wide recheck. Re-read before each later phase.
                     try:
                         phase_tasks: dict[int, OwnedTask] = {}
                         for status in ACTIVE_FILTER_STATUSES:
@@ -545,7 +797,7 @@ def cleanup_certain_oom_tasks(
                             {
                                 "accountIndex": account_index + 1,
                                 "taskIds": task_ids,
-                                "error": f"age_policy_final_recheck_failed: {type(exc).__name__}: {exc}",
+                                "error": f"policy_final_recheck_failed: {type(exc).__name__}: {exc}",
                             }
                         )
                         stop_failed = True
@@ -563,12 +815,24 @@ def cleanup_certain_oom_tasks(
                             disappeared.append(decisions_by_id[task_id])
                             continue
                         current_position = phase_positions.get(task_id)
-                        if (
+                        reasons = set(
-                            account_queue_threshold is None
+                            decisions_by_id[task_id].get("cleanupReasons")
-                            or current_position is None
+                            or [decisions_by_id[task_id].get("reason")]
-                            or current_position <= account_queue_threshold
+                        )
-                            or current_task.status != "waiting"
+                        architecture_applies = bool(
-                        ):
+                            cleanup_phase == "architecture"
+                            and "known_framework_architecture_incompatible" in reasons
+                            and current_task.status == "waiting"
+                        )
+                        age_applies = bool(
+                            cleanup_phase == "age"
+                            and "old_model_beyond_account_queue_threshold" in reasons
+                            and account_queue_threshold is not None
+                            and current_position is not None
+                            and current_position > account_queue_threshold
+                            and current_task.status == "waiting"
+                        )
+                        if not architecture_applies and not age_applies:
                             policy_no_longer_applies.append(
                                 {
                                     **decisions_by_id[task_id],
@@ -577,7 +841,7 @@ def cleanup_certain_oom_tasks(
                                     "policyChangeReason": (
                                         "task_started_running"
                                         if current_task.status == "running"
-                                        else "queue_position_or_status_changed"
+                                        else "queue_position_status_or_policy_changed"
                                     ),
                                 }
                             )
@@ -612,6 +876,7 @@ def cleanup_certain_oom_tasks(
 
     return {
         "dryRun": bool(dry_run),
+        "architectureOnly": bool(architecture_only),
         "accounts": len(clients),
         "activeScanned": len(tasks),
         "uniqueModels": len(model_ids),
@@ -622,6 +887,12 @@ def cleanup_certain_oom_tasks(
         "listingErrors": {str(index + 1): values for index, values in listing_errors.items()},
         "certainOomCount": len(oom_decisions),
         "certainOomTasks": oom_decisions,
+        "architectureBlockCount": len(architecture_blocks),
+        "architectureIncompatibleCount": len(architecture_decisions),
+        "architectureIncompatibleTasks": architecture_decisions,
+        "architectureModelConfigsComplete": len(model_configs),
+        "architectureModelConfigErrors": model_config_errors,
+        "architecturePolicySkipped": architecture_skipped,
         "oldOverflowCount": len(old_overflow_decisions),
         "oldOverflowTasks": old_overflow_decisions,
         "oldModelQueueThresholds": [
@@ -646,7 +917,9 @@ def cleanup_certain_oom_tasks(
 
 
 def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(description="Safely stop deterministic OOM and over-threshold old ModelHub tasks.")
+    parser = argparse.ArgumentParser(
+        description="Safely stop deterministic OOM/architecture and over-threshold old ModelHub tasks."
+    )
     parser.add_argument("--execute", action="store_true", help="Actually terminate selected tasks; otherwise only print a preview")
     parser.add_argument("--read-concurrency", type=int, default=6)
     parser.add_argument("--stop-batch-size", type=int, default=DEFAULT_STOP_BATCH_SIZE)
@@ -678,6 +951,7 @@ def main(argv: list[str] | None = None) -> int:
     write_json(report_path, summary)
     print(
         f"[queue-cleanup] finished certain_oom={summary['certainOomCount']} "
+        f"architecture_incompatible={summary['architectureIncompatibleCount']} "
         f"old_overflow={summary['oldOverflowCount']} "
         f"cancelled={summary['cancelledCount']} stop_errors={len(summary['stopErrors'])} "
         f"report={report_path}",

modelhub_submmit_api/version.py

@@ -1 +1 @@
-AGENT_VERSION = "2026.08.12.2"
+AGENT_VERSION = "2026.08.12.3"

tests/test_candidate_preflight.py

@@ -492,6 +492,34 @@ class CandidatePreflightTests(unittest.TestCase):
         self.assertFalse(result["failureNeedsLlm"])
         self.assertEqual(0, classifier.calls)
 
+    def test_fixed_platform_error_extracts_unsupported_model_type(self) -> None:
+        result = classify_failure_archive(
+            make_failure_archive(
+                "MODEL_NOT_SUPPORTED",
+                "Value error, The checkpoint you are trying to load has model type `qwen3_5` "
+                "but Transformers does not recognize this architecture.",
+                "当前框架版本不支持该模型架构，检查框架版本或改用兼容的推理后端。",
+            )
+        )
+
+        self.assertEqual("framework_architecture_unsupported", result["failureCategory"])
+        self.assertEqual(["qwen3_5"], result["failureUnsupportedModelTypes"])
+
+    def test_fixed_runtime_error_extracts_unsupported_architecture_names(self) -> None:
+        result = classify_failure_archive(
+            make_failure_archive(
+                "MODEL_NOT_SUPPORTED",
+                "ValueError: Model architectures ['CogVLMForCausalLM'] are not supported for now. "
+                "Supported architectures: dict_keys(['Qwen2ForCausalLM'])",
+                "当前框架版本不支持该模型架构，检查框架版本或改用兼容的推理后端。",
+            )
+        )
+
+        self.assertEqual(
+            ["CogVLMForCausalLM"],
+            result["failureUnsupportedArchitectures"],
+        )
+
     def test_generic_unsupported_backend_does_not_create_architecture_feedback(self) -> None:
         classification = classify_failure_report(
             "ATTENTION_NOT_SUPPORTED",
@@ -663,6 +691,50 @@ class CandidatePreflightTests(unittest.TestCase):
 
         self.assertEqual({}, report["architectureCompatibilityBlocks"])
 
+    def test_parsed_model_type_builds_dynamic_block_without_saved_model_profile(self) -> None:
+        now = datetime.now(timezone.utc)
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            tracker = OutcomeTracker(Path(temporary_dir) / "outcomes.jsonl")
+            tracker.record_submission(
+                "owner/source",
+                "gpu",
+                "vllm",
+                "text-generation",
+                "task-no-profile",
+                now.isoformat(),
+            )
+            tracker._records[0].update(  # noqa: SLF001
+                {
+                    "outcome": "failed",
+                    "failureCategory": "framework_architecture_unsupported",
+                    "failureDeterministic": True,
+                    "failureClassificationReason": "explicit_framework_model_unsupported",
+                    "failureUnsupportedModelTypes": ["qwen3_5"],
+                }
+            )
+            report = tracker.get_stats_report()
+
+        key = "gpu|vllm|text-generation|model_type:qwen3_5"
+        self.assertIn(key, report["architectureCompatibilityBlocks"])
+        advisor = CandidatePreflightAdvisor(gpu_memory_gib={})
+        advisor.set_feedback_stats(report)
+        assessment = advisor.assess(
+            inspection=ModelInspection(
+                repo_id="unrelated/repository-name",
+                model_config={
+                    "model_type": "qwen3_5",
+                    "architectures": ["Qwen3_5ForCausalLM"],
+                },
+            ),
+            task_type="text-generation",
+            target_gpu="gpu",
+            framework="vllm",
+            config_params="",
+        )
+
+        self.assertFalse(assessment.allowed)
+        self.assertEqual("preflight_learned_architecture_incompatible", assessment.reason)
+
     def test_outcome_sync_enriches_failure_and_excludes_platform_fault_from_feedback(self) -> None:
         with tempfile.TemporaryDirectory() as temporary_dir:
             path = Path(temporary_dir) / "outcomes.jsonl"

tests/test_poll_policy.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
 import argparse
+import json
 import sys
+import tempfile
 import unittest
+from datetime import datetime, timezone
 from pathlib import Path
 
 
@@ -11,10 +14,48 @@ if str(PACKAGE_DIR) in sys.path:
     sys.path.remove(str(PACKAGE_DIR))
 sys.path.insert(0, str(PACKAGE_DIR))
 
-from poll_runner import resolve_age_cleanup_policy  # noqa: E402
+from outcome_tracker import OutcomeTracker  # noqa: E402
+from poll_runner import _load_task_compatibility_contexts, resolve_age_cleanup_policy  # noqa: E402
 
 
 class PollPolicyTests(unittest.TestCase):
+    def test_cleanup_contexts_merge_outcomes_with_older_ledger_entries(self) -> None:
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            root = Path(temporary_dir)
+            tracker = OutcomeTracker(root / "outcomes.jsonl")
+            tracker.record_submission(
+                "owner/new",
+                "gpu-a",
+                "vllm",
+                "text-generation",
+                "task-new",
+                datetime.now(timezone.utc).isoformat(),
+                model_profile={"architectures": ["Qwen2ForCausalLM"]},
+            )
+            ledger_path = root / "ledger.jsonl"
+            ledger_path.write_text(
+                json.dumps(
+                    {
+                        "taskId": "task-old",
+                        "modelId": "owner/old",
+                        "targetGpu": "gpu-b",
+                        "framework": "mindie",
+                        "taskType": "text-generation",
+                    }
+                )
+                + "\n",
+                encoding="utf-8",
+            )
+
+            contexts = _load_task_compatibility_contexts(
+                tracker,
+                ledger_path=ledger_path,
+            )
+
+        self.assertEqual(["Qwen2ForCausalLM"], contexts["task-new"]["modelProfile"]["architectures"])
+        self.assertEqual("mindie", contexts["task-old"]["framework"])
+        self.assertEqual({}, contexts["task-old"]["modelProfile"])
+
     def test_age_cleanup_uses_minus_ten_once_then_minus_five(self) -> None:
         args = argparse.Namespace(
             recent_model_reserve_slots=10,

tests/test_queue_cleanup.py

@@ -12,10 +12,12 @@ if str(PACKAGE_DIR) in sys.path:
     sys.path.remove(str(PACKAGE_DIR))
 sys.path.insert(0, str(PACKAGE_DIR))
 
+from architecture_compatibility import architecture_compatibility_key  # noqa: E402
 from modelhub_client import ModelHubClient, ModelHubClientPool  # noqa: E402
 from queue_cleanup import (  # noqa: E402
     OwnedTask,
     cleanup_certain_oom_tasks,
+    find_architecture_incompatible_tasks,
     find_certain_oom_tasks,
     find_old_overflow_tasks,
 )
@@ -72,9 +74,11 @@ class FakeDiscovery:
         self,
         sizes: dict[str, int | None],
         last_modified: dict[str, datetime | None] | None = None,
+        configs: dict[str, dict[str, Any]] | None = None,
     ) -> None:
         self.sizes = sizes
         self.last_modified = last_modified or {}
+        self.configs = configs or {}
 
     def list_repo_tree(self, repo_id: str) -> list[dict[str, Any]]:
         size = self.sizes[repo_id]
@@ -85,6 +89,10 @@ class FakeDiscovery:
     def get_model_last_modified(self, repo_id: str) -> datetime | None:
         return self.last_modified.get(repo_id)
 
+    def get_model_config(self, repo_id: str) -> tuple[dict[str, Any], str | None]:
+        config = self.configs.get(repo_id)
+        return (dict(config), None) if config is not None else ({}, "config_not_found")
+
 
 class RecordingHttpClient:
     def __init__(self) -> None:
@@ -103,6 +111,181 @@ class RecordingHttpClient:
 
 
 class QueueCleanupTests(unittest.TestCase):
+    @staticmethod
+    def architecture_block(
+        *,
+        gpu: str = "Iluvatar_bi-100",
+        framework: str = "vllm",
+        task_type: str = "text-generation",
+        signature: str = "architectures:qwen2forcausallm",
+    ) -> tuple[str, dict[str, Any]]:
+        key = architecture_compatibility_key(gpu, framework, task_type, signature)
+        assert key is not None
+        return key, {
+            "targetGpu": gpu,
+            "framework": framework,
+            "taskType": task_type,
+            "matchType": "architectures",
+            "architectureSignature": signature,
+            "evidenceCount": 1,
+            "expiresAt": "2026-09-11T00:00:00+00:00",
+        }
+
+    def test_architecture_cleanup_matches_exact_context_and_protects_running(self) -> None:
+        key, block = self.architecture_block()
+        tasks = [
+            OwnedTask(0, 1, "owner/waiting", "Iluvatar_bi-100", "waiting"),
+            OwnedTask(0, 2, "owner/running", "Iluvatar_bi-100", "running"),
+            OwnedTask(0, 3, "owner/other-framework", "Iluvatar_bi-100", "waiting"),
+        ]
+        contexts = {
+            "1": {
+                "modelId": "owner/waiting",
+                "targetGpu": "Iluvatar_bi-100",
+                "framework": "vllm",
+                "taskType": "text-generation",
+                "modelProfile": {"architectures": ["Qwen2ForCausalLM"]},
+            },
+            "2": {
+                "modelId": "owner/running",
+                "targetGpu": "Iluvatar_bi-100",
+                "framework": "vllm",
+                "taskType": "text-generation",
+                "modelProfile": {"architectures": ["Qwen2ForCausalLM"]},
+            },
+            "3": {
+                "modelId": "owner/other-framework",
+                "targetGpu": "Iluvatar_bi-100",
+                "framework": "mindie",
+                "taskType": "text-generation",
+                "modelProfile": {"architectures": ["Qwen2ForCausalLM"]},
+            },
+        }
+
+        selected, skipped = find_architecture_incompatible_tasks(
+            tasks,
+            architecture_blocks={key: block},
+            task_contexts=contexts,
+            model_configs={},
+        )
+
+        self.assertEqual([1], [item["taskId"] for item in selected])
+        self.assertEqual(1, skipped["runningMatchedProtected"])
+        self.assertEqual(1, skipped["noMatchingBlock"])
+
+    def test_queue_cleanup_fetches_config_and_stops_known_incompatible_waiting_task(self) -> None:
+        key, block = self.architecture_block()
+        client = FakeQueueClient(
+            [
+                {
+                    "taskId": 1,
+                    "modelId": "owner/model",
+                    "gpuType": "Iluvatar_bi-100",
+                    "status": "waiting",
+                }
+            ]
+        )
+        pool = ModelHubClientPool([client], active_task_cap=100)  # type: ignore[list-item]
+        summary = cleanup_certain_oom_tasks(
+            pool,
+            FakeDiscovery(
+                {"owner/model": 1 * GIB},
+                configs={
+                    "owner/model": {
+                        "model_type": "qwen2",
+                        "architectures": ["Qwen2ForCausalLM"],
+                    }
+                },
+            ),  # type: ignore[arg-type]
+            architecture_compatibility_blocks={key: block},
+            task_compatibility_contexts={
+                "1": {
+                    "modelId": "owner/model",
+                    "targetGpu": "Iluvatar_bi-100",
+                    "framework": "vllm",
+                    "taskType": "text-generation",
+                    "modelProfile": {},
+                }
+            },
+            log=lambda _message: None,
+        )
+
+        self.assertEqual(1, summary["architectureIncompatibleCount"])
+        self.assertEqual(1, summary["cancelledCount"])
+        self.assertEqual([[1]], client.stopped)
+
+    def test_dynamic_architecture_only_cleanup_skips_expensive_size_and_age_scans(self) -> None:
+        key, block = self.architecture_block()
+        client = FakeQueueClient(
+            [
+                {
+                    "taskId": 1,
+                    "modelId": "owner/model",
+                    "gpuType": "Iluvatar_bi-100",
+                    "status": "waiting",
+                }
+            ]
+        )
+        pool = ModelHubClientPool([client], active_task_cap=100)  # type: ignore[list-item]
+        summary = cleanup_certain_oom_tasks(
+            pool,
+            FakeDiscovery({}),  # type: ignore[arg-type]
+            architecture_compatibility_blocks={key: block},
+            task_compatibility_contexts={
+                "1": {
+                    "modelId": "owner/model",
+                    "targetGpu": "Iluvatar_bi-100",
+                    "framework": "vllm",
+                    "taskType": "text-generation",
+                    "modelProfile": {"architectures": ["Qwen2ForCausalLM"]},
+                }
+            },
+            architecture_only=True,
+            log=lambda _message: None,
+        )
+
+        self.assertTrue(summary["architectureOnly"])
+        self.assertEqual(0, summary["repositorySizesComplete"])
+        self.assertEqual(0, summary["modelAgeMetadataComplete"])
+        self.assertEqual(1, summary["architectureIncompatibleCount"])
+        self.assertEqual([[1]], client.stopped)
+
+    def test_architecture_cleanup_recheck_protects_task_that_started_running(self) -> None:
+        key, block = self.architecture_block()
+        client = FakeQueueClient(
+            [
+                {
+                    "taskId": 1,
+                    "modelId": "owner/model",
+                    "gpuType": "Iluvatar_bi-100",
+                    "status": "waiting",
+                }
+            ],
+            promote_on_waiting_read=2,
+        )
+        pool = ModelHubClientPool([client], active_task_cap=100)  # type: ignore[list-item]
+        summary = cleanup_certain_oom_tasks(
+            pool,
+            FakeDiscovery({"owner/model": 1 * GIB}),  # type: ignore[arg-type]
+            architecture_compatibility_blocks={key: block},
+            task_compatibility_contexts={
+                "1": {
+                    "modelId": "owner/model",
+                    "targetGpu": "Iluvatar_bi-100",
+                    "framework": "vllm",
+                    "taskType": "text-generation",
+                    "modelProfile": {"architectures": ["Qwen2ForCausalLM"]},
+                }
+            },
+            read_concurrency=1,
+            log=lambda _message: None,
+        )
+
+        self.assertEqual(1, summary["architectureIncompatibleCount"])
+        self.assertEqual(0, summary["cancelledCount"])
+        self.assertEqual("task_started_running", summary["policyNoLongerAppliesTasks"][0]["policyChangeReason"])
+        self.assertEqual([], client.stopped)
+
     def test_old_models_use_each_accounts_own_capacity_minus_ten_threshold(self) -> None:
         now = datetime(2026, 8, 11, tzinfo=timezone.utc)
         tasks = [