feat: add tiered durable state and memory bounds

Dockerfile

@@ -2,6 +2,8 @@ FROM modelhubxc-4pd.tencentcloudcr.com/xc_agent_platform/python:3.11-slim
 
 ENV PYTHONUNBUFFERED=1
 ENV PYTHONPATH=/app/modelhub_submmit_api
+ENV NO_PROXY=dev.modelhub.org.cn,modelhub.org.cn
+ENV no_proxy=dev.modelhub.org.cn,modelhub.org.cn
 
 WORKDIR /app
 

README.md

@@ -375,11 +375,32 @@ probe and `/ready` reports worker availability. Poll and cleanup summaries are
 bounded in memory, and the last 200 worker crash records are synchronized with
 the durable state branch for post-restart diagnosis.
 
+Version `2026.08.22.1` adds tiered durable outcome storage and hard runtime memory
+bounds. The live `agent-state` branch contains only pending outcomes, an
+aggregate checkpoint, and the most recent terminal window; completed raw
+outcomes are sanitized, gzip-compressed, and uploaded to monthly
+`agent-archive-YYYY-MM` branches. A restart restores only the live branch, so it
+retains lifetime success statistics and architecture evidence without cloning or
+parsing the full history. The first upgrade automatically compacts a legacy
+outcome file after 2,000 records; later compactions are incremental.
+
+Candidate preparation now retains at most 100 distinct models (up to three
+routes per model) per cycle by default. ModelScope repository-tree, config,
+model-card, modification-time, and page caches use bounded LRU eviction, which
+prevents memory use from growing with every poll cycle. These limits can be
+tuned with `--candidate-pool-limit`, `MODELHUB_AGENT_OUTCOME_COMPACT_THRESHOLD`,
+`MODELHUB_AGENT_RECENT_OUTCOME_LIMIT`,
+`MODELSCOPE_DETAIL_CACHE_MAX_MODELS`, and `MODELSCOPE_PAGE_CACHE_MAX_PAGES`.
+Cold archive upload is best-effort and retried on later state synchronizations;
+the aggregate checkpoint remains the authoritative recovery input even while an
+archive shard is awaiting upload. ModelHub Git traffic is excluded from proxy
+routing in the container to avoid deployment-specific Git failures.
+
 ## Deploy
 
 Create a tag and submit the repository URL plus tag in "我的适配智能体".
 
 ```bash
-git tag -a agent-v28 -m "ModelHub agent 2026.08.21.1"
+git tag -a agent-v29 -m "ModelHub agent 2026.08.22.1"
-git push origin main agent-v28
+git push origin main agent-v29
 ```

modelhub_submmit_api/config_optimizer.py

@@ -18,7 +18,13 @@ class SafeConfigOptimizer:
         self._qualified = self._learn()
 
     def _learn(self) -> dict[tuple[str, str, str], list[dict[str, Any]]]:
-        outcomes = read_jsonl(self.outcomes_path)
+        recent_path = Path(".modelhub_state/recent_outcomes.jsonl")
+        outcomes_by_key: dict[str, dict[str, Any]] = {}
+        for item in [*read_jsonl(recent_path), *read_jsonl(self.outcomes_path)]:
+            task_id = str(item.get("taskId") or "")
+            if task_id:
+                outcomes_by_key[task_id] = item
+        outcomes = list(outcomes_by_key.values())
         by_task = {
             str(item.get("taskId")): item
             for item in outcomes
@@ -117,4 +123,3 @@ class SafeConfigOptimizer:
             "evidenceLowerBound": choice["lowerBound"],
             "fingerprint": choice["fingerprint"],
         }
-

modelhub_submmit_api/hf_discovery.py

@@ -4,6 +4,7 @@ import os
 import re
 import threading
 import time
+from collections import OrderedDict
 from dataclasses import replace
 from datetime import datetime, timezone
 from pathlib import PurePosixPath
@@ -70,13 +71,23 @@ class HuggingFaceDiscovery:
             timeout=timeout,
             retries=retries,
         )
-        self._repo_tree_cache: dict[str, list[dict[str, Any]]] = {}
+        self._detail_cache_limit = max(
+            32,
+            min(1000, int(os.getenv("MODELSCOPE_DETAIL_CACHE_MAX_MODELS", "128"))),
+        )
+        self._page_cache_limit = max(
+            16,
+            min(500, int(os.getenv("MODELSCOPE_PAGE_CACHE_MAX_PAGES", "96"))),
+        )
+        self._repo_tree_cache: OrderedDict[str, list[dict[str, Any]]] = OrderedDict()
         self._repo_tree_lock = threading.Lock()
-        self._model_config_cache: dict[str, tuple[dict[str, Any], str | None]] = {}
+        self._model_config_cache: OrderedDict[str, tuple[dict[str, Any], str | None]] = OrderedDict()
         self._model_config_lock = threading.Lock()
-        self._model_last_modified_cache: dict[str, datetime | None] = {}
+        self._model_last_modified_cache: OrderedDict[str, datetime | None] = OrderedDict()
         self._model_last_modified_lock = threading.Lock()
-        self._model_page_cache: dict[tuple[str, int, int], tuple[float, list[dict[str, Any]]]] = {}
+        self._model_page_cache: OrderedDict[
+            tuple[str, int, int], tuple[float, list[dict[str, Any]]]
+        ] = OrderedDict()
         self._model_page_cache_ttl = max(
             0.0,
             float(
@@ -95,9 +106,16 @@ class HuggingFaceDiscovery:
         )
         self._last_model_page_request_at = 0.0
         self._unsupported_task_filters: dict[str, float] = {}
-        self._model_card_cache: dict[str, dict[str, Any]] = {}
+        self._model_card_cache: OrderedDict[str, dict[str, Any]] = OrderedDict()
         self._model_card_lock = threading.Lock()
 
+    @staticmethod
+    def _remember_lru(cache: OrderedDict, key: Any, value: Any, limit: int) -> None:
+        cache[key] = value
+        cache.move_to_end(key)
+        while len(cache) > limit:
+            cache.popitem(last=False)
+
     def list_recent_models(
         self,
         *,
@@ -149,6 +167,7 @@ class HuggingFaceDiscovery:
             cache_key = ("*" 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])
             else:
                 elapsed = time.monotonic() - self._last_model_page_request_at
@@ -191,7 +210,12 @@ class HuggingFaceDiscovery:
                     break
 
                 items = self._extract_models(payload)
-                self._model_page_cache[cache_key] = (time.monotonic(), list(items))
+                self._remember_lru(
+                    self._model_page_cache,
+                    cache_key,
+                    (time.monotonic(), list(items)),
+                    self._page_cache_limit,
+                )
             if not items:
                 break
             for item in items:
@@ -247,6 +271,8 @@ class HuggingFaceDiscovery:
     def get_model_card_metadata(self, repo_id: str) -> dict[str, Any]:
         with self._model_card_lock:
             cached = self._model_card_cache.get(repo_id)
+            if cached is not None:
+                self._model_card_cache.move_to_end(repo_id)
         if cached is not None:
             return dict(cached)
         encoded_repo_id = "/".join(quote(part, safe="") for part in repo_id.split("/"))
@@ -262,12 +288,19 @@ class HuggingFaceDiscovery:
         except Exception:
             result = {}
         with self._model_card_lock:
-            self._model_card_cache[repo_id] = dict(result)
+            self._remember_lru(
+                self._model_card_cache,
+                repo_id,
+                dict(result),
+                self._detail_cache_limit,
+            )
         return result
 
     def get_model_config(self, repo_id: str) -> tuple[dict[str, Any], str | None]:
         with self._model_config_lock:
             cached = self._model_config_cache.get(repo_id)
+            if cached is not None:
+                self._model_config_cache.move_to_end(repo_id)
         if cached is not None:
             return dict(cached[0]), cached[1]
 
@@ -284,13 +317,20 @@ class HuggingFaceDiscovery:
             result = ({}, f"{type(exc).__name__}: {exc}")
 
         with self._model_config_lock:
-            self._model_config_cache[repo_id] = result
+            self._remember_lru(
+                self._model_config_cache,
+                repo_id,
+                result,
+                self._detail_cache_limit,
+            )
         return dict(result[0]), result[1]
 
     def get_model_last_modified(self, repo_id: str) -> datetime | None:
         with self._model_last_modified_lock:
             if repo_id in self._model_last_modified_cache:
-                return self._model_last_modified_cache[repo_id]
+                result = self._model_last_modified_cache[repo_id]
+                self._model_last_modified_cache.move_to_end(repo_id)
+                return result
 
         encoded_repo_id = "/".join(quote(part, safe="") for part in repo_id.split("/"))
         try:
@@ -325,12 +365,19 @@ class HuggingFaceDiscovery:
                 result = parse_datetime(raw_value)
 
         with self._model_last_modified_lock:
-            self._model_last_modified_cache[repo_id] = result
+            self._remember_lru(
+                self._model_last_modified_cache,
+                repo_id,
+                result,
+                self._detail_cache_limit,
+            )
         return result
 
     def list_repo_tree(self, repo_id: str) -> list[dict[str, Any]]:
         with self._repo_tree_lock:
             cached = self._repo_tree_cache.get(repo_id)
+            if cached is not None:
+                self._repo_tree_cache.move_to_end(repo_id)
         if cached is not None:
             return list(cached)
 
@@ -348,7 +395,12 @@ class HuggingFaceDiscovery:
         entries = self._extract_files(payload)
 
         with self._repo_tree_lock:
-            self._repo_tree_cache[repo_id] = list(entries)
+            self._remember_lru(
+                self._repo_tree_cache,
+                repo_id,
+                list(entries),
+                self._detail_cache_limit,
+            )
         return list(entries)
 
     @staticmethod

modelhub_submmit_api/main.py

@@ -79,6 +79,12 @@ def build_parser() -> argparse.ArgumentParser:
         default=4,
         help="Multiplier used when auto-deriving scan limit from quota/queue capacity",
     )
+    parser.add_argument(
+        "--candidate-pool-limit",
+        type=int,
+        default=100,
+        help="Maximum prepared candidates retained before submitting; bounds memory in large queues",
+    )
     parser.add_argument("--min-downloads", type=int, default=50, help="Minimum downloads threshold")
     parser.add_argument("--daily-target", type=int, default=0, help="Daily submission target across all auto runs; 0 means unlimited")
     parser.add_argument("--dry-run", action="store_true", help="Only write artifacts without creating tasks")
@@ -746,6 +752,7 @@ def collect_candidates_from_models(
     allow_dynamic_tasks: bool = False,
 ) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]], int]:
     candidates: list[dict[str, Any]] = []
+    candidate_models: set[str] = set()
     skipped: list[dict[str, Any]] = []
     failed: list[dict[str, Any]] = []
     processed_count = 0
@@ -754,7 +761,7 @@ def collect_candidates_from_models(
     new_models = [model for model in models if model.repo_id not in seen_model_ids]
 
     for offset in range(0, len(new_models), chunk_size):
-        if len(candidates) >= candidate_goal:
+        if len(candidate_models) >= candidate_goal:
             break
         chunk = new_models[offset : offset + chunk_size]
         for model in chunk:
@@ -794,6 +801,10 @@ def collect_candidates_from_models(
         for index in range(len(chunk)):
             model_candidates, model_skipped, model_failed = ordered_results.get(index, ([], [], []))
             candidates.extend(model_candidates)
+            if model_candidates:
+                model_id = str(model_candidates[0].get("repoId") or model_candidates[0].get("modelAddress") or "")
+                if model_id:
+                    candidate_models.add(model_id)
             skipped.extend(model_skipped)
             failed.extend(model_failed)
         processed_count += len(chunk)
@@ -927,6 +938,46 @@ def one_candidate_per_model(candidates: list[dict[str, Any]]) -> list[dict[str,
     return selected
 
 
+def limit_routes_per_model(
+    candidates: list[dict[str, Any]],
+    *,
+    max_routes: int = 3,
+) -> list[dict[str, Any]]:
+    selected: list[dict[str, Any]] = []
+    counts: Counter[str] = Counter()
+    for candidate in candidates:
+        model_id = str(candidate.get("repoId") or candidate.get("modelAddress") or "")
+        if not model_id or counts[model_id] >= max(1, int(max_routes)):
+            continue
+        counts[model_id] += 1
+        selected.append(candidate)
+    return selected
+
+
+def candidate_model_count(candidates: list[dict[str, Any]]) -> int:
+    return len(
+        {
+            str(candidate.get("repoId") or candidate.get("modelAddress") or "")
+            for candidate in candidates
+            if candidate.get("repoId") or candidate.get("modelAddress")
+        }
+    )
+
+
+def limit_candidate_models(candidates: list[dict[str, Any]], *, max_models: int) -> list[dict[str, Any]]:
+    selected: list[dict[str, Any]] = []
+    models: set[str] = set()
+    for candidate in candidates:
+        model_id = str(candidate.get("repoId") or candidate.get("modelAddress") or "")
+        if not model_id:
+            continue
+        if model_id not in models and len(models) >= max(0, int(max_models)):
+            continue
+        models.add(model_id)
+        selected.append(candidate)
+    return selected
+
+
 def run_submission(
     args: argparse.Namespace,
     *,
@@ -1271,7 +1322,14 @@ def run_submission(
     if strategy_manager is not None:
         submission_goal = min(submission_goal, strategy_manager.submissions_until_refresh)
     attempt_multiplier = max(1, int(getattr(args, "scan_multiplier", 4) or 1))
-    candidate_goal = max(submission_goal, submission_goal * attempt_multiplier)
+    raw_candidate_goal = max(submission_goal, submission_goal * attempt_multiplier)
+    candidate_pool_limit = max(20, int(getattr(args, "candidate_pool_limit", 100) or 100))
+    candidate_goal = min(raw_candidate_goal, candidate_pool_limit)
+    print(
+        f"[scan] candidate_pool_limit={candidate_pool_limit} "
+        f"raw_goal={raw_candidate_goal} effective_goal={candidate_goal}",
+        flush=True,
+    )
 
     pipeline_tags = pipeline_tags_for_task_types(selected_task_types)
     if official_registry is not None and not getattr(args, "task_types", None):
@@ -1305,7 +1363,8 @@ def run_submission(
         allow_older_than_recent_window=allow_older_models_for_scan,
         recent_model_days=recent_model_days,
     ):
-        if candidate_goal <= 0 or len(candidates) >= candidate_goal:
+        current_candidate_models = candidate_model_count(candidates)
+        if candidate_goal <= 0 or current_candidate_models >= candidate_goal:
             break
         skipped_before_stage = len(skipped)
         stage_updated_after = stage["updatedAfter"]
@@ -1348,7 +1407,7 @@ def run_submission(
         stage_candidates, stage_skipped, stage_failed, processed_count = collect_candidates_from_models(
             models=models,
             seen_model_ids=seen_model_ids,
-            candidate_goal=max(1, candidate_goal - len(candidates)),
+            candidate_goal=max(1, candidate_goal - current_candidate_models),
             hf_discovery=hf_discovery,
             modelhub_client=modelhub_client,
             template_selector=template_selector,
@@ -1363,6 +1422,12 @@ def run_submission(
             config_optimizer=config_optimizer,
             allow_dynamic_tasks=not bool(getattr(args, "task_types", None)),
         )
+        stage_candidates = limit_routes_per_model(stage_candidates, max_routes=3)
+        remaining_candidate_capacity = max(0, candidate_goal - current_candidate_models)
+        stage_candidates = limit_candidate_models(
+            stage_candidates,
+            max_models=remaining_candidate_capacity,
+        )
         candidates.extend(stage_candidates)
         skipped.extend(stage_skipped)
         failed.extend(stage_failed)
@@ -1375,13 +1440,15 @@ def run_submission(
             "newModelsProcessed": processed_count,
             "candidatesAdded": len(stage_candidates),
             "candidateCountAfterStage": len(candidates),
+            "candidateModelsAfterStage": candidate_model_count(candidates),
             "skippedAdded": len(skipped) - skipped_before_stage,
             "failedAdded": len(stage_failed),
         }
         scan_stages.append(stage_summary)
         print(
             f"[scan] stage={stage['name']} discovered={len(models)} new_processed={processed_count} "
-            f"candidates_added={len(stage_candidates)} candidates_total={len(candidates)}/{candidate_goal} "
+            f"routes_added={len(stage_candidates)} routes_total={len(candidates)} "
+            f"candidate_models={candidate_model_count(candidates)}/{candidate_goal} "
             f"skipped_added={len(skipped) - skipped_before_stage}",
             flush=True,
         )
@@ -1391,7 +1458,7 @@ def run_submission(
     uniqueness_rejected_candidates: list[dict[str, Any]] = []
     target_submit_count = resolve_max_submit_count(
         args=args,
-        planned_count=len(candidates),
+        planned_count=candidate_model_count(candidates),
         remaining_daily_quota=remaining_daily_quota,
     )
     if strategy_manager is not None:
@@ -1642,10 +1709,13 @@ def run_submission(
             "olderHistoryScanEnabled": allow_older_models_for_scan,
         },
         "scanLimit": scan_limit,
+        "rawCandidateGoal": raw_candidate_goal,
+        "candidatePoolLimit": candidate_pool_limit,
         "candidateGoal": candidate_goal,
         "scanStages": scan_stages,
         "scannedModels": scanned_model_count,
         "candidateCount": len(candidates),
+        "candidateModelCount": candidate_model_count(candidates),
         "candidateFrameworkCounts": dict(candidate_framework_counts.most_common()),
         "targetSubmitCount": target_submit_count,
         "maxSubmitAttempts": 0 if args.dry_run else min(

modelhub_submmit_api/outcome_tracker.py

@@ -3,9 +3,12 @@ from __future__ import annotations
 from collections import defaultdict
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime, timedelta
+import gzip
+import json
 import os
 from pathlib import Path
 from typing import Any
+import uuid
 
 from architecture_compatibility import (
     DEFAULT_ARCHITECTURE_BLOCK_TTL_DAYS,
@@ -15,7 +18,7 @@ from architecture_compatibility import (
     architecture_profile,
     architecture_profiles,
 )
-from common import append_jsonl, parse_datetime, read_jsonl, update_jsonl, utc_now
+from common import append_jsonl, parse_datetime, read_json, read_jsonl, update_jsonl, utc_now, write_json, write_jsonl
 from failure_log_inspector import fetch_and_classify_failure_log
 from history_stats import classify_failure, is_failure, is_success
 from llm_classifier import LLMAssistedClassifier
@@ -24,6 +27,12 @@ from task_registry import task_type_from_history_task
 
 
 DEFAULT_OUTCOMES_PATH = Path("outcomes/submissions.jsonl")
+DEFAULT_OUTCOME_CHECKPOINT_PATH = Path(".modelhub_state/outcome_checkpoint.json")
+DEFAULT_RECENT_OUTCOMES_PATH = Path(".modelhub_state/recent_outcomes.jsonl")
+DEFAULT_ARCHIVE_PENDING_DIR = Path(".modelhub_state/archive_pending/outcomes")
+OUTCOME_CHECKPOINT_VERSION = 1
+DEFAULT_OUTCOME_COMPACT_THRESHOLD = 2000
+DEFAULT_RECENT_OUTCOME_LIMIT = 1000
 FAILURE_ENRICHMENT_LIMIT = 40
 FAILURE_ENRICHMENT_WORKERS = 4
 FAILURE_ENRICHMENT_MAX_ATTEMPTS = 3
@@ -35,9 +44,21 @@ def _now_iso() -> str:
 
 
 class OutcomeTracker:
-    def __init__(self, path: Path | str) -> None:
+    def __init__(
+        self,
+        path: Path | str,
+        *,
+        checkpoint_path: Path | str = DEFAULT_OUTCOME_CHECKPOINT_PATH,
+        recent_path: Path | str = DEFAULT_RECENT_OUTCOMES_PATH,
+        archive_pending_dir: Path | str = DEFAULT_ARCHIVE_PENDING_DIR,
+    ) -> None:
         self.path = Path(path)
+        self.checkpoint_path = Path(checkpoint_path)
+        self.recent_path = Path(recent_path)
+        self.archive_pending_dir = Path(archive_pending_dir)
         self._records: list[dict[str, Any]] = []
+        self._recent_records: list[dict[str, Any]] = read_jsonl(self.recent_path)
+        self._checkpoint: dict[str, Any] = self._load_checkpoint()
         self._by_task_id: dict[str, dict[str, Any]] = {}
         self._by_model_gpu: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list)
         self._failed_model_gpus: dict[tuple[str, str], datetime] = {}
@@ -46,13 +67,131 @@ class OutcomeTracker:
         self._records = read_jsonl(self.path)
         self._rebuild_indexes()
 
+        self._compact_if_needed(force=not bool(self._checkpoint) and len(self._records) > self._compact_threshold())
+
         last_sync_times = [
             parse_datetime(record.get("lastSyncTime"))
-            for record in self._records
+            for record in [*self._records, *self._recent_records]
             if record.get("lastSyncTime")
         ]
+        checkpoint_sync = parse_datetime(self._checkpoint.get("lastSyncTime"))
+        if checkpoint_sync is not None:
+            last_sync_times.append(checkpoint_sync)
         self._last_sync_time: datetime = max(last_sync_times) if last_sync_times else utc_now() - timedelta(days=7)
 
+    def _compact_threshold(self) -> int:
+        raw = os.getenv("MODELHUB_AGENT_OUTCOME_COMPACT_THRESHOLD", str(DEFAULT_OUTCOME_COMPACT_THRESHOLD))
+        try:
+            return max(500, int(raw))
+        except ValueError:
+            return DEFAULT_OUTCOME_COMPACT_THRESHOLD
+
+    def _recent_limit(self) -> int:
+        raw = os.getenv("MODELHUB_AGENT_RECENT_OUTCOME_LIMIT", str(DEFAULT_RECENT_OUTCOME_LIMIT))
+        try:
+            return max(100, int(raw))
+        except ValueError:
+            return DEFAULT_RECENT_OUTCOME_LIMIT
+
+    def _load_checkpoint(self) -> dict[str, Any]:
+        try:
+            payload = read_json(self.checkpoint_path)
+        except (FileNotFoundError, ValueError, TypeError):
+            return {}
+        if not isinstance(payload, dict) or int(payload.get("version") or 0) != OUTCOME_CHECKPOINT_VERSION:
+            return {}
+        return payload
+
+    @property
+    def has_durable_checkpoint(self) -> bool:
+        return bool(self._checkpoint and isinstance(self._checkpoint.get("report"), dict))
+
+    def _combined_recent_terminal(self) -> list[dict[str, Any]]:
+        by_key: dict[str, dict[str, Any]] = {}
+        for record in [*self._recent_records, *self._records]:
+            if record.get("outcome") not in {"success", "failed"}:
+                continue
+            key = _outcome_record_key(record)
+            existing = by_key.get(key)
+            by_key[key] = record if existing is None else _prefer_newer_outcome(existing, record)
+        return sorted(by_key.values(), key=_outcome_record_timestamp, reverse=True)[: self._recent_limit()]
+
+    @staticmethod
+    def _archive_record(record: dict[str, Any]) -> dict[str, Any]:
+        return {
+            key: value
+            for key, value in record.items()
+            if not any(
+                marker in key.casefold()
+                for marker in ("url", "token", "cookie", "authorization", "configparams", "response")
+            )
+        }
+
+    def _write_archive_shard(self, records: list[dict[str, Any]]) -> str | None:
+        if not records:
+            return None
+        now = utc_now()
+        month = now.strftime("%Y-%m")
+        name = f"{now.strftime('%Y%m%dT%H%M%SZ')}-{uuid.uuid4().hex[:10]}.jsonl.gz"
+        path = self.archive_pending_dir / month / name
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with gzip.open(path, "wt", encoding="utf-8", compresslevel=6) as handle:
+            for record in records:
+                handle.write(json.dumps(self._archive_record(record), ensure_ascii=False, sort_keys=True) + "\n")
+        return f"{month}/{name}"
+
+    def _compact_if_needed(self, *, force: bool = False) -> bool:
+        if not force and len(self._records) <= self._compact_threshold():
+            return False
+        removed = [
+            record
+            for record in self._records
+            if record.get("outcome") in {"success", "failed", "policy_cancelled"}
+        ]
+        if not removed:
+            return False
+        retained = [
+            record
+            for record in self._records
+            if record.get("outcome") not in {"success", "failed", "policy_cancelled"}
+        ]
+        full_report = self.get_stats_report()
+        recent_records = self._combined_recent_terminal()
+        shard = self._write_archive_shard(removed)
+        archived_total = max(0, int(full_report.get("totalRecords") or 0) - len(retained))
+        checkpoint_report = dict(full_report)
+        checkpoint_report.update(
+            {
+                "totalRecords": archived_total,
+                "pendingRecords": 0,
+            }
+        )
+        previous_shards = list(self._checkpoint.get("archiveShards") or [])
+        if shard:
+            previous_shards.append(shard)
+        sync_times = [
+            parse_datetime(record.get("lastSyncTime"))
+            for record in [*removed, *retained]
+            if record.get("lastSyncTime")
+        ]
+        last_sync = max((value for value in sync_times if value is not None), default=None)
+        self._checkpoint = {
+            "version": OUTCOME_CHECKPOINT_VERSION,
+            "generatedAt": utc_now().isoformat(),
+            "lastSyncTime": last_sync.isoformat() if last_sync else self._checkpoint.get("lastSyncTime"),
+            "archivedRecords": archived_total,
+            "archiveShards": previous_shards[-200:],
+            "recentLimit": self._recent_limit(),
+            "report": checkpoint_report,
+        }
+        self._records = retained
+        self._recent_records = recent_records
+        write_json(self.checkpoint_path, self._checkpoint)
+        write_jsonl(self.recent_path, self._recent_records)
+        write_jsonl(self.path, self._records)
+        self._rebuild_indexes()
+        return True
+
     def set_failure_llm_classifier(self, classifier: LLMAssistedClassifier | None) -> None:
         self._failure_llm_classifier = classifier
 
@@ -79,7 +218,9 @@ class OutcomeTracker:
     def get_strategy_history_records(self) -> list[dict[str, Any]]:
         """Expose only successes and evidence-attributable failures for GPU ranking."""
         records: list[dict[str, Any]] = []
-        for record in self._records:
+        recent_by_key = {_outcome_record_key(record): record for record in self._recent_records}
+        recent_by_key.update({_outcome_record_key(record): record for record in self._records})
+        for record in recent_by_key.values():
             outcome = record.get("outcome")
             if outcome == "success":
                 verify_result = 1
@@ -101,7 +242,13 @@ class OutcomeTracker:
     def _rebuild_indexes(self) -> None:
         self._by_task_id.clear()
         self._by_model_gpu.clear()
-        for record in self._records:
+        # Keep the bounded recent window in the identity index as well.  The
+        # platform history API uses an inclusive time cursor, so the first page
+        # after a restart can contain terminal tasks already represented by the
+        # checkpoint.  Remembering their task IDs prevents double-counting
+        # without loading the cold archive.
+        indexed_records = [*self._recent_records, *self._records]
+        for record in indexed_records:
             task_id = record.get("taskId")
             if task_id:
                 self._by_task_id[str(task_id)] = record
@@ -487,12 +634,15 @@ class OutcomeTracker:
         now_datetime = utc_now()
         now = now_datetime.isoformat()
         terminal = [r for r in self._records if r.get("outcome") in {"success", "failed"}]
+        recent_terminal = self._combined_recent_terminal()
 
         gpu_groups: dict[str, list[dict[str, Any]]] = defaultdict(list)
         framework_groups: dict[str, list[dict[str, Any]]] = defaultdict(list)
         combo_groups: dict[tuple[str, str, str], list[dict[str, Any]]] = defaultdict(list)
         profile_groups: dict[tuple[str, str, str, str, str], list[dict[str, Any]]] = defaultdict(list)
         sized_profile_groups: dict[tuple[str, str, str, str, str, int], list[dict[str, Any]]] = defaultdict(list)
+        recent_combo_groups: dict[tuple[str, str, str], list[dict[str, Any]]] = defaultdict(list)
+        recent_profile_groups: dict[tuple[str, str, str, str, str], list[dict[str, Any]]] = defaultdict(list)
 
         for record in terminal:
             gpu = record.get("targetGpu") or "unknown"
@@ -514,6 +664,17 @@ class OutcomeTracker:
                     size_bucket = int(load_bytes).bit_length() - 1
                     sized_profile_groups[(gpu, fw, tt, model_type, quantization, size_bucket)].append(record)
 
+        for record in recent_terminal:
+            gpu = record.get("targetGpu") or "unknown"
+            fw = record.get("framework") or "unknown"
+            tt = record.get("taskType") or "unknown"
+            recent_combo_groups[(gpu, fw, tt)].append(record)
+            profile = record.get("modelProfile") or {}
+            model_type = str(profile.get("modelType") or "").strip()
+            if model_type:
+                quantization = str(profile.get("quantizationMethod") or "none").strip()
+                recent_profile_groups[(gpu, fw, tt, model_type, quantization)].append(record)
+
         gpu_summaries = {gpu: _summarize(records) for gpu, records in gpu_groups.items()}
         framework_summaries = {fw: _summarize(records) for fw, records in framework_groups.items()}
         combination_stats = {
@@ -521,7 +682,7 @@ class OutcomeTracker:
             for (gpu, fw, tt), records in combo_groups.items()
         }
         recent_combination_stats: dict[str, dict[str, Any]] = {}
-        for (gpu, fw, tt), records in combo_groups.items():
+        for (gpu, fw, tt), records in recent_combo_groups.items():
             recent = sorted(records, key=_outcome_record_timestamp, reverse=True)[:20]
             consecutive_failures = _consecutive_attributable_failures(recent)
             consecutive_platform_failures = _consecutive_platform_failures(recent)
@@ -554,6 +715,8 @@ class OutcomeTracker:
                 "quantizationMethod": quantization,
                 **_summarize(records),
             }
+        for (gpu, fw, tt, model_type, quantization), records in recent_profile_groups.items():
+            key = f"{gpu}|{fw}|{tt}|{model_type}|{quantization}"
             recent = sorted(records, key=_outcome_record_timestamp, reverse=True)[:20]
             consecutive_failures = _consecutive_attributable_failures(recent)
             last_terminal_at = None
@@ -563,7 +726,11 @@ class OutcomeTracker:
                     or parse_datetime(recent[0].get("submitTime"))
                 )
             recent_profile_combination_stats[key] = {
-                **profile_combination_stats[key],
+                "targetGpu": gpu,
+                "framework": fw,
+                "taskType": tt,
+                "modelType": model_type,
+                "quantizationMethod": quantization,
                 **_summarize(recent),
                 "consecutiveFailures": consecutive_failures,
                 "lastTerminalAt": last_terminal_at.isoformat() if last_terminal_at else None,
@@ -607,7 +774,7 @@ class OutcomeTracker:
 
         architecture_block_ttl_days = _architecture_block_ttl_days()
         architecture_blocks = _build_architecture_compatibility_blocks(
-            terminal,
+            recent_terminal,
             now=now_datetime,
             ttl_days=architecture_block_ttl_days,
         )
@@ -616,7 +783,7 @@ class OutcomeTracker:
             combination = f"{block['targetGpu']}|{block['framework']}"
             architecture_blocks_by_gpu_framework[combination] += 1
 
-        return {
+        report = {
             "generatedAt": now,
             "totalRecords": len(self._records),
             "pendingRecords": pending_count,
@@ -639,6 +806,102 @@ class OutcomeTracker:
             "totals": _summarize(terminal),
             "warnings": warnings,
         }
+        return self._merge_checkpoint_report(report, recent_terminal=recent_terminal, now=now_datetime)
+
+    def _merge_checkpoint_report(
+        self,
+        report: dict[str, Any],
+        *,
+        recent_terminal: list[dict[str, Any]],
+        now: datetime,
+    ) -> dict[str, Any]:
+        baseline = self._checkpoint.get("report")
+        if not isinstance(baseline, dict):
+            return report
+        merged = dict(report)
+        for field in (
+            "gpuSummaries",
+            "frameworkSummaries",
+            "combinationStats",
+            "profileCombinationStats",
+            "sizedProfileCombinationStats",
+        ):
+            base_items = baseline.get(field) if isinstance(baseline.get(field), dict) else {}
+            delta_items = report.get(field) if isinstance(report.get(field), dict) else {}
+            combined: dict[str, dict[str, Any]] = {}
+            for key in set(base_items) | set(delta_items):
+                combined[str(key)] = _merge_stat_items(base_items.get(key), delta_items.get(key))
+            merged[field] = combined
+
+        merged["totals"] = _merge_stat_items(baseline.get("totals"), report.get("totals"))
+        merged["totalRecords"] = int(self._checkpoint.get("archivedRecords") or 0) + len(self._records)
+        merged["terminalRecords"] = int((merged.get("totals") or {}).get("total") or 0)
+        merged["pendingRecords"] = sum(1 for record in self._records if record.get("outcome") == "pending")
+        merged["policyCancelledRecords"] = int(baseline.get("policyCancelledRecords") or 0) + sum(
+            1 for record in self._records if record.get("outcome") == "policy_cancelled"
+        )
+
+        observed: dict[str, float] = {}
+        for source in (baseline.get("observedGpuMemoryGiB") or {}, report.get("observedGpuMemoryGiB") or {}):
+            if not isinstance(source, dict):
+                continue
+            for gpu, raw in source.items():
+                try:
+                    value = float(raw)
+                except (TypeError, ValueError):
+                    continue
+                previous = observed.get(str(gpu))
+                observed[str(gpu)] = min(previous, value) if previous else value
+        merged["observedGpuMemoryGiB"] = observed
+
+        blocks = {
+            str(key): dict(value)
+            for key, value in (baseline.get("architectureCompatibilityBlocks") or {}).items()
+            if isinstance(value, dict)
+            and (parse_datetime(value.get("expiresAt")) or now + timedelta(seconds=1)) > now
+        }
+        for record in recent_terminal:
+            if record.get("outcome") != "success":
+                continue
+            event_time = parse_datetime(record.get("lastSyncTime")) or parse_datetime(record.get("submitTime"))
+            if event_time is None:
+                continue
+            for profile in _stored_architecture_profiles(record, include_model_type=True):
+                key = architecture_compatibility_key(
+                    str(record.get("targetGpu") or ""),
+                    str(record.get("framework") or ""),
+                    str(record.get("taskType") or ""),
+                    profile["signature"],
+                )
+                block = blocks.get(str(key)) if key is not None else None
+                if block is not None and event_time >= (parse_datetime(block.get("latestFailureAt")) or event_time):
+                    blocks.pop(str(key), None)
+        blocks.update(
+            {
+                str(key): dict(value)
+                for key, value in (report.get("architectureCompatibilityBlocks") or {}).items()
+                if isinstance(value, dict)
+            }
+        )
+        by_gpu_framework: dict[str, int] = defaultdict(int)
+        for block in blocks.values():
+            by_gpu_framework[f"{block.get('targetGpu')}|{block.get('framework')}"] += 1
+        merged["architectureCompatibilityBlocks"] = blocks
+        merged["architectureCompatibilitySummary"] = {
+            "activeBlockCount": len(blocks),
+            "ttlDays": _architecture_block_ttl_days(),
+            "byGpuFramework": dict(by_gpu_framework),
+        }
+
+        warnings: list[str] = []
+        for gpu, summary in (merged.get("gpuSummaries") or {}).items():
+            if int(summary.get("decisionTotal") or 0) >= 4 and float(summary.get("decisionFailureRate") or 0) >= 0.5:
+                warnings.append(f"GPU {gpu} 本地统计失败率偏高（≥50%），建议重点关注。")
+        for key, stat in (merged.get("combinationStats") or {}).items():
+            if int(stat.get("decisionTotal") or 0) >= 3 and float(stat.get("decisionFailureRate") or 0) >= 0.6:
+                warnings.append(f"组合 {key} 近期失败集中，建议降低该 GPU+框架的提交优先级。")
+        merged["warnings"] = warnings
+        return merged
 
     def save(self) -> None:
         local_records = list(self._records)
@@ -658,11 +921,14 @@ class OutcomeTracker:
 
         self._records = update_jsonl(self.path, merge)
         self._rebuild_indexes()
+        self._compact_if_needed()
 
     def _rebuild_failed_index(self) -> None:
         self._failed_model_gpus.clear()
         latest_by_combo: dict[tuple[str, str], tuple[datetime, dict[str, Any]]] = {}
-        for record in self._records:
+        recent_by_key = {_outcome_record_key(record): record for record in self._recent_records}
+        recent_by_key.update({_outcome_record_key(record): record for record in self._records})
+        for record in recent_by_key.values():
             # Platform, unresolved, and policy outcomes neither clear nor
             # create a model/GPU cooldown. The full-history audit showed that
             # more than half of failures lack enough evidence for attribution.
@@ -781,6 +1047,42 @@ def _summarize(records: list[dict[str, Any]]) -> dict[str, Any]:
     }
 
 
+def _merge_stat_items(base: Any, delta: Any) -> dict[str, Any]:
+    base = base if isinstance(base, dict) else {}
+    delta = delta if isinstance(delta, dict) else {}
+    merged = {**base, **delta}
+    count_fields = (
+        "total",
+        "successCount",
+        "failureCount",
+        "attributableFailureCount",
+        "platformFailureCount",
+        "unresolvedFailureCount",
+        "decisionTotal",
+        "pendingCount",
+    )
+    for field in count_fields:
+        merged[field] = int(base.get(field) or 0) + int(delta.get(field) or 0)
+    breakdown: dict[str, int] = defaultdict(int)
+    for source in (base.get("failureBreakdown") or {}, delta.get("failureBreakdown") or {}):
+        if isinstance(source, dict):
+            for key, value in source.items():
+                breakdown[str(key)] += int(value or 0)
+    merged["failureBreakdown"] = dict(breakdown)
+    total = merged["total"]
+    decisions = merged["decisionTotal"]
+    merged.update(
+        {
+            "successRate": round(merged["successCount"] / total, 4) if total else 0.0,
+            "failureRate": round(merged["failureCount"] / total, 4) if total else 0.0,
+            "decisionSuccessRate": round(merged["successCount"] / decisions, 4) if decisions else 0.0,
+            "decisionFailureRate": round(merged["attributableFailureCount"] / decisions, 4) if decisions else 0.0,
+            "pendingRate": round(merged["pendingCount"] / total, 4) if total else 0.0,
+        }
+    )
+    return merged
+
+
 def _architecture_block_ttl_days() -> int:
     raw = os.getenv("MODELHUB_ARCHITECTURE_BLOCK_TTL_DAYS")
     if raw is None:

modelhub_submmit_api/poll_runner.py

@@ -572,12 +572,27 @@ def run_poll_loop(
     architecture_bootstrap_summary: dict[str, Any] = {"enabled": False}
     if not getattr(base_args, "skip_outcome_sync", False):
         try:
-            architecture_bootstrap_summary = _bootstrap_architecture_history(
+            if outcome_tracker.has_durable_checkpoint:
-                modelhub_client=modelhub_client,
+                cached_feedback = outcome_tracker.get_stats_report()
-                outcome_tracker=outcome_tracker,
+                architecture_bootstrap_summary = {
-                ledger_path=Path(base_args.ledger_path),
+                    "source": "durable_checkpoint",
-                now=now,
+                    "terminalRecords": int(cached_feedback.get("terminalRecords") or 0),
-            )
+                    "architectureBlocks": len(cached_feedback.get("architectureCompatibilityBlocks") or {}),
+                    "fullHistoryScanSkipped": True,
+                }
+                log(
+                    "[architecture-bootstrap] source=durable_checkpoint "
+                    f"terminal={architecture_bootstrap_summary['terminalRecords']} "
+                    f"blocks={architecture_bootstrap_summary['architectureBlocks']} "
+                    "full_history_scan=skipped"
+                )
+            else:
+                architecture_bootstrap_summary = _bootstrap_architecture_history(
+                    modelhub_client=modelhub_client,
+                    outcome_tracker=outcome_tracker,
+                    ledger_path=Path(base_args.ledger_path),
+                    now=now,
+                )
             architecture_bootstrap_summary["enabled"] = True
             _persist_architecture_blacklist(
                 outcome_tracker.get_stats_report(),

modelhub_submmit_api/state_sync.py

@@ -25,6 +25,7 @@ from version import AGENT_VERSION
 STATE_SCHEMA_VERSION = 1
 DEFAULT_REMOTE = "https://dev.modelhub.org.cn/CoolBoy/submmit.git"
 DEFAULT_BRANCH = "agent-state"
+DEFAULT_ARCHIVE_BRANCH_PREFIX = "agent-archive"
 DEFAULT_BATCH_SIZE = 20
 DEFAULT_RETENTION_DAYS = 30
 DEFAULT_HISTORY_DEPTH = 20
@@ -37,7 +38,9 @@ STATE_ALLOWLIST = (
     ".modelhub_state/gpu_strategy.json",
     ".modelhub_state/market_intelligence.json",
     ".modelhub_state/official_capabilities.json",
+    ".modelhub_state/outcome_checkpoint.json",
     ".modelhub_state/queue_cleanup_latest.json",
+    ".modelhub_state/recent_outcomes.jsonl",
     ".modelhub_state/recovery_active_tasks.jsonl",
     ".modelhub_state/recovery_intents.jsonl",
     ".modelhub_state/routing_intelligence.json",
@@ -205,10 +208,103 @@ class StateGitSync:
         return f"{name} <{email}>".encode("utf-8")
 
     def _remote_oid(self) -> str | None:
+        return self._remote_oid_for(self.branch)
+
+    def _remote_oid_for(self, branch: str) -> str | None:
         result = porcelain.ls_remote(self.remote, quiet=True, **self._auth_kwargs())
-        oid = result.refs.get(f"refs/heads/{self.branch}".encode("utf-8"))
+        oid = result.refs.get(f"refs/heads/{branch}".encode("utf-8"))
         return oid.decode("ascii") if oid else None
 
+    def _sync_archive_pending(self) -> None:
+        pending_root = self.state_dir / "archive_pending" / "outcomes"
+        files = sorted(path for path in pending_root.glob("*/*.jsonl.gz") if path.is_file())
+        if not files:
+            return
+        by_month: dict[str, list[Path]] = {}
+        for path in files:
+            by_month.setdefault(path.parent.name, []).append(path)
+        for month, month_files in sorted(by_month.items()):
+            branch = f"{DEFAULT_ARCHIVE_BRANCH_PREFIX}-{month}"
+            remote_oid = self._remote_oid_for(branch)
+            parent = Path(tempfile.mkdtemp(prefix="modelhub-agent-archive-"))
+            workspace = parent / "archive"
+            try:
+                if remote_oid:
+                    porcelain.clone(
+                        self.remote,
+                        workspace,
+                        branch=branch,
+                        depth=1,
+                        checkout=True,
+                        errstream=io.BytesIO(),
+                        **self._auth_kwargs(),
+                    )
+                else:
+                    workspace.mkdir(parents=True)
+                    repo = porcelain.init(workspace)
+                    repo.refs.set_symbolic_ref(b"HEAD", f"refs/heads/{branch}".encode("utf-8"))
+                repo = Repo(str(workspace))
+                manifest_path = workspace / "manifest.json"
+                try:
+                    manifest = read_json(manifest_path)
+                except (FileNotFoundError, ValueError, TypeError):
+                    manifest = {}
+                archived = manifest.get("files") if isinstance(manifest.get("files"), dict) else {}
+                for source in month_files:
+                    destination = workspace / "outcomes" / source.name
+                    destination.parent.mkdir(parents=True, exist_ok=True)
+                    shutil.copy2(source, destination)
+                    archived[f"outcomes/{source.name}"] = {
+                        "sha256": _sha256_file(destination),
+                        "bytes": destination.stat().st_size,
+                    }
+                write_json(
+                    manifest_path,
+                    {
+                        "schemaVersion": 1,
+                        "month": month,
+                        "updatedAt": _utc_now().isoformat(),
+                        "files": archived,
+                    },
+                )
+                manifest_path.with_name(f".{manifest_path.name}.lock").unlink(missing_ok=True)
+                porcelain.add(repo)
+                status = porcelain.status(repo)
+                if any(status.staged.get(kind) for kind in ("add", "delete", "modify")):
+                    porcelain.commit(
+                        repo,
+                        message=f"archive: outcomes {month}".encode("utf-8"),
+                        author=self._author,
+                        committer=self._author,
+                    )
+                    if self._remote_oid_for(branch) != remote_oid:
+                        raise StateSyncError(f"archive branch {branch} changed remotely")
+                    porcelain.push(
+                        repo,
+                        self.remote,
+                        refspecs=f"HEAD:refs/heads/{branch}",
+                        force=True,
+                        outstream=io.BytesIO(),
+                        errstream=io.BytesIO(),
+                        **self._auth_kwargs(),
+                    )
+                    if self._remote_oid_for(branch) != repo.head().decode("ascii"):
+                        raise StateSyncError(f"archive branch {branch} verification failed")
+                for source in month_files:
+                    source.unlink(missing_ok=True)
+                self.log(
+                    f"[archive-sync] branch={branch} shards={len(month_files)} "
+                    f"files_total={len(archived)} status=ok"
+                )
+            finally:
+                shutil.rmtree(parent, ignore_errors=True)
+
+    def _sync_archive_pending_safely(self) -> None:
+        try:
+            self._sync_archive_pending()
+        except Exception as exc:
+            self.log(f"[archive-sync] status=deferred reason={_safe_text(exc)}")
+
     def _create_workspace(self, remote_oid: str | None) -> None:
         parent = Path(tempfile.mkdtemp(prefix="modelhub-agent-state-"))
         workspace = parent / "state"
@@ -539,7 +635,7 @@ class StateGitSync:
                     # A restored snapshot must refresh official configs before routing.
                     sanitized_market["frameworkUpdatedAt"] = None
                 write_json(destination, sanitized_market)
-            elif relative == "outcomes/submissions.jsonl":
+            elif relative in {"outcomes/submissions.jsonl", ".modelhub_state/recent_outcomes.jsonl"}:
                 sanitized_outcomes: list[dict[str, Any]] = []
                 for row in read_jsonl(source):
                     sanitized_outcomes.append(
@@ -593,6 +689,10 @@ class StateGitSync:
                 staged = any(status.staged.get(kind) for kind in ("add", "delete", "modify"))
                 if not staged:
                     self.healthy = True
+                    # A previous archive push may have been deferred while the
+                    # hot snapshot was already current.  Retry cold shards even
+                    # when this cycle has no hot-state commit to publish.
+                    self._sync_archive_pending_safely()
                     return True
                 porcelain.commit(
                     repo,
@@ -631,6 +731,7 @@ class StateGitSync:
                 self.last_error = None
                 self.healthy = True
                 self.log(f"[state-sync] generation={self.generation} phase={phase} status=ok")
+                self._sync_archive_pending_safely()
                 return True
             except Exception as exc:
                 self.healthy = False

modelhub_submmit_api/version.py

@@ -1 +1 @@
-AGENT_VERSION = "2026.08.21.1"
+AGENT_VERSION = "2026.08.22.1"

tests/test_modelscope_discovery.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import sys
 import unittest
 from pathlib import Path
+from unittest.mock import patch
 
 
 PACKAGE_DIR = Path(__file__).resolve().parents[1] / "modelhub_submmit_api"
@@ -68,6 +69,17 @@ class ModelScopeDiscoveryTests(unittest.TestCase):
         self.assertEqual(1786294389, int(first.timestamp()))  # type: ignore[union-attr]
         self.assertEqual(1, metadata_client.calls)
 
+    def test_model_detail_cache_evicts_old_entries_at_fixed_limit(self) -> None:
+        metadata_client = ModelMetadataClient()
+        with patch.dict("os.environ", {"MODELSCOPE_DETAIL_CACHE_MAX_MODELS": "32"}):
+            discovery = HuggingFaceDiscovery(legacy_http_client=metadata_client)  # type: ignore[arg-type]
+        for index in range(40):
+            discovery.get_model_last_modified(f"owner/model-{index}")
+
+        self.assertEqual(32, len(discovery._model_last_modified_cache))
+        discovery.get_model_last_modified("owner/model-0")
+        self.assertEqual(41, metadata_client.calls)
+
     def test_openapi_page_size_never_exceeds_platform_limit(self) -> None:
         http_client = RecordingHttpClient()
         discovery = HuggingFaceDiscovery(http_client=http_client)  # type: ignore[arg-type]

tests/test_super_agent.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import os
+import gzip
 import tempfile
 import unittest
 from datetime import datetime, timezone
@@ -19,6 +21,7 @@ from common import read_jsonl, write_json, write_jsonl  # noqa: E402
 from config_optimizer import SafeConfigOptimizer  # noqa: E402
 from hf_discovery import HuggingFaceDiscovery, parse_model_card_front_matter  # noqa: E402
 from official_capabilities import OfficialCapabilityRegistry  # noqa: E402
+from outcome_tracker import OutcomeTracker  # noqa: E402
 from routing_engine import SuccessFirstRoutingEngine  # noqa: E402
 from state_sync import StateGitSync  # noqa: E402
 
@@ -44,6 +47,91 @@ class OfficialClient:
 
 
 class SuperAgentTests(unittest.TestCase):
+    def test_outcome_history_compacts_to_checkpoint_recent_window_and_gzip_archive(self) -> None:
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            root = Path(temporary_dir)
+            outcomes = root / "outcomes.jsonl"
+            checkpoint = root / "checkpoint.json"
+            recent = root / "recent.jsonl"
+            archive = root / "archive"
+            rows = [
+                {
+                    "taskId": str(index),
+                    "modelId": f"owner/model-{index}",
+                    "targetGpu": "gpu-a",
+                    "framework": "vllm",
+                    "taskType": "text-generation",
+                    "submitTime": f"2026-08-{1 + index // 100:02d}T00:00:{index % 60:02d}+00:00",
+                    "lastSyncTime": "2026-08-21T00:00:00+00:00",
+                    "outcome": "success" if index % 2 == 0 else "failed",
+                    "failureCategory": "model_runtime" if index % 2 else None,
+                    "failureScope": "model" if index % 2 else None,
+                    "logCosUrl": "https://secret.invalid/signed?token=hidden",
+                }
+                for index in range(600)
+            ]
+            write_jsonl(outcomes, rows)
+            with patch.dict(
+                os.environ,
+                {
+                    "MODELHUB_AGENT_OUTCOME_COMPACT_THRESHOLD": "500",
+                    "MODELHUB_AGENT_RECENT_OUTCOME_LIMIT": "100",
+                },
+            ):
+                tracker = OutcomeTracker(
+                    outcomes,
+                    checkpoint_path=checkpoint,
+                    recent_path=recent,
+                    archive_pending_dir=archive,
+                )
+                self.assertTrue(tracker.has_durable_checkpoint)
+                self.assertEqual([], read_jsonl(outcomes))
+                self.assertEqual(100, len(read_jsonl(recent)))
+                report = tracker.get_stats_report()
+                self.assertEqual(600, report["terminalRecords"])
+                self.assertEqual(300, report["totals"]["successCount"])
+                shard = next(archive.rglob("*.jsonl.gz"))
+                import gzip
+
+                with gzip.open(shard, "rt", encoding="utf-8") as handle:
+                    archived_text = handle.read()
+                self.assertNotIn("logCosUrl", archived_text)
+                self.assertNotIn("token=hidden", archived_text)
+
+                restored = OutcomeTracker(
+                    outcomes,
+                    checkpoint_path=checkpoint,
+                    recent_path=recent,
+                    archive_pending_dir=archive,
+                )
+                self.assertEqual(600, restored.get_stats_report()["terminalRecords"])
+
+                restored.record_submission(
+                    model_id="owner/new-model",
+                    target_gpu="gpu-a",
+                    framework="vllm",
+                    task_type="text-generation",
+                    task_id="new-task",
+                    submit_time="2026-08-22T00:00:00+00:00",
+                )
+                restored._update_record_from_task(
+                    restored._by_task_id["new-task"],
+                    {"status": "success", "verifyResult": 1},
+                )
+                restored.save()
+                updated = restored.get_stats_report()
+                self.assertEqual(601, updated["terminalRecords"])
+                self.assertEqual(301, updated["totals"]["successCount"])
+
+                restarted = OutcomeTracker(
+                    outcomes,
+                    checkpoint_path=checkpoint,
+                    recent_path=recent,
+                    archive_pending_dir=archive,
+                )
+                self.assertEqual(601, restarted.get_stats_report()["terminalRecords"])
+                self.assertIn("599", restarted._by_task_id)
+
     def test_official_registry_discovers_catalog_and_exact_model_routes(self) -> None:
         with tempfile.TemporaryDirectory() as temporary_dir:
             registry = OfficialCapabilityRegistry(Path(temporary_dir) / "official.json", log_fn=lambda _: None)
@@ -149,6 +237,17 @@ class SuperAgentTests(unittest.TestCase):
                 restored_project / ".modelhub_state" / "worker_crashes.jsonl",
                 [{"at": "2026-08-21T01:00:00+00:00", "exitCode": 1}],
             )
+            pending_archive = (
+                project
+                / ".modelhub_state"
+                / "archive_pending"
+                / "outcomes"
+                / "2026-08"
+                / "shard.jsonl.gz"
+            )
+            pending_archive.parent.mkdir(parents=True, exist_ok=True)
+            with gzip.open(pending_archive, "wt", encoding="utf-8") as handle:
+                handle.write('{"taskId":"archived"}\n')
             credentials = {"username": "tester", "email": "tester@example.com", "password": "secret-value"}
             manager = StateGitSync(
                 project_root=project,
@@ -171,6 +270,9 @@ class SuperAgentTests(unittest.TestCase):
                 ]
             )
             self.assertIsNotNone(batch_id)
+            self.assertFalse(pending_archive.exists())
+            archive_refs = porcelain.ls_remote(str(remote)).refs
+            self.assertIn(b"refs/heads/agent-archive-2026-08", archive_refs)
             manager.close()
 
             restored = StateGitSync(