feat: prioritize proven GPU framework combinations

README.md

@@ -48,26 +48,26 @@ Optional tuning:
 - `MODELHUB_MARKET_QUEUE_REFRESH_SECONDS` default `600`
 - `MODELHUB_MARKET_FRAMEWORK_REFRESH_SECONDS` default `21600`
 - `MODELHUB_MARKET_THROUGHPUT_WINDOW_HOURS` default `6`
-- `MODELHUB_MARKET_FRAMEWORK_MIN_SAMPLES` default `100`
+- `MODELHUB_MARKET_FRAMEWORK_MIN_SAMPLES` default `300`
 - `MODELSCOPE_PAGE_INTERVAL_SECONDS` default `0.25`
 - `MODELSCOPE_PAGE_CACHE_TTL_SECONDS` default `900`
 - `MODELHUB_AGENT_VERIFY_CACHE_TTL_SECONDS` default `900`
 
 ## Adaptive GPU Strategy
 
-When no explicit GPU override is supplied, the worker uses a queue-aware 50/30/20
-strategy generation:
+When no explicit GPU override is supplied, the worker uses a success-first 70/30
+strategy generation with no self-funded exploration:
 
-- 50%: the three long-term GPUs with the best Wilson lower confidence score and at least 100 terminal samples
-- 30%: exploration across every currently supported GPU
-- 20%: the top recent GPUs among the latest 1,000 terminal tasks
+- 70%: the three long-term GPUs with the best Wilson lower confidence score and at least 100 terminal samples
+- 30%: the top recent GPUs among the latest 1,000 terminal tasks
+- 0%: unvetted/all-GPU exploration; community-wide results provide the exploration signal
 
-The 50/30/20 category ratio remains exact across accepted tasks. Inside each
+The 70/30 category ratio remains exact across accepted tasks. Inside each
 category, weighted fair scheduling combines the category's historical rank with
 live public market data:
 
-- estimated backlog hours (`waiting / recent completions per hour`) instead of raw queue length
-- recent public success quality, scored with a Wilson lower confidence bound
+- recent public success quality, scored with a strongly weighted Wilson lower confidence bound
+- estimated backlog hours (`waiting / recent completions per hour`) as a bounded tie-breaker
 - machine availability, running workers, and advertised concurrency
 - a circuit breaker for unavailable or apparently stalled GPU pools
 
@@ -79,11 +79,12 @@ snapshot, uses a retry backoff, and never blocks normal submissions.
 For each compatible model/GPU pair, the worker also ranks the GPU's supported
 frameworks using ModelHub's public aggregate `modelCount` and `successCount` data,
 then blends in the worker's own GPU+framework outcomes with a capped weight.
-Only frameworks with at least 100 samples receive statistical priority; the
-legacy safe framework order remains the fallback. Framework statistics refresh
+Only frameworks with at least 300 public samples and a safe Wilson lower bound
+are eligible. Framework statistics refresh
 every 6 hours, so they do not add per-model API traffic. Newly published
 frameworks are discovered automatically, but receive no novelty bonus: they can
-win only when their confidence-adjusted success score is better. A new framework
+win only when their confidence-adjusted success score beats the best incumbent by
+at least 10%. A new framework
 is eligible only after the authenticated official build-config endpoint returns
 a complete config that passes local structure, placeholder, framework-name, and
 GPU-parallelism validation. Valid official configs are cached and refreshed with
@@ -91,9 +92,14 @@ the framework snapshot; local templates remain the fail-safe fallback.
 
 Only platform-accepted tasks count. After exactly 200 accepted tasks, the next
 poll cycle reloads all account history, generates a new immutable strategy snapshot,
-and resets the generation counters to 100/60/40 targets. The active snapshot and
+and resets the generation counters to 140/60 targets. The active snapshot and
 progress are stored in `.modelhub_state/gpu_strategy.json`.
 
+Five consecutive local failures open a 12-hour GPU/framework circuit breaker.
+A sub-20% success rate over the latest 20 terminal tasks opens a 6-hour breaker.
+Candidate shortages expand the model search window; they never unlock an
+unvetted GPU or framework.
+
 ModelScope HTTP 429 responses use exponential backoff and `Retry-After`. Successful
 pages remain cached, so a later cycle retries the failed page instead of restarting
 the whole pagination scan.
@@ -139,6 +145,10 @@ adds fail-closed model/GPU prechecks and persistent uniqueness exclusions.
 Version `2026.08.04.1` adds queue/throughput intelligence, GPU health circuit
 breaking, weighted-fair scheduling, live framework/config discovery, and
 confidence-ranked public-plus-local framework selection.
+Version `2026.08.05.1` removes self-funded GPU exploration, switches accepted
+traffic to 70/30 long-term/recent exploitation, raises the public framework gate
+to 300 samples, makes success dominate queue pressure, and adds recent local
+GPU/framework circuit breakers.
 
 ## Deploy
 

modelhub_submmit_api/README.md

@@ -8,7 +8,7 @@ It currently supports:
 - continuous queue refill via `run_poll.sh`
 - multiple ModelHub tokens read from `KEY.md` and `KEYS.md`
 - automatic task/framework/template selection across the supported GPU catalog
-- adaptive long-term/exploration/recent GPU scheduling with a persistent local snapshot
+- adaptive long-term/recent GPU exploitation with a persistent local snapshot
 - live queue/throughput-aware GPU weighting and confidence-ranked framework selection
 
 ## Layout
@@ -74,19 +74,21 @@ bash run_poll.sh --dry-run
 ## Behavior
 
 - The runner auto-discovers all safe GPU/template combinations from the public submit catalog.
-- Automatic GPU selection uses exact 50/30/20 accepted-task scheduling: long-term
-  Wilson-ranked top 3 GPUs, all supported GPUs, and the top GPUs from the latest
-  1,000 terminal tasks.
+- Automatic GPU selection uses exact 70/30 accepted-task scheduling: long-term
+  Wilson-ranked top 3 GPUs and the top GPUs from the latest 1,000 terminal tasks.
+  There is no all-GPU exploration category.
 - Within each category, weighted-fair scheduling uses estimated queue backlog hours,
   recent public throughput/success, machine availability, and worker concurrency.
   Unavailable or stalled GPU pools are circuit-broken instead of continuing to absorb work.
 - Compatible frameworks are ranked by ModelHub public aggregate success statistics
-  plus capped local GPU+framework evidence, with a 100-sample public minimum and
-  Wilson confidence bounds. The legacy safe order is retained whenever evidence
-  is missing or too small.
+  plus capped local GPU+framework evidence, with a 300-sample public minimum and
+  Wilson confidence bounds. Missing or undersized public evidence receives zero
+  traffic rather than falling back to exploration.
 - New frameworks are discovered from the live catalog but get no novelty bonus.
   They are eligible only with a complete official build config that passes local
-  validation; cached local templates remain the fallback if live config sync fails.
+  validation and a confidence score at least 10% above the best incumbent.
+- Five consecutive local failures pause a GPU/framework pair for 12 hours; a
+  sub-20% rate over the latest 20 terminal tasks pauses it for 6 hours.
 - 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
@@ -163,7 +165,7 @@ Each run typically includes:
 
 Persistent local scheduler state is written under `.modelhub_state/`:
 
-- `gpu_strategy.json`: GPU ranks, generation progress, and 50/30/20 accepted counters
+- `gpu_strategy.json`: GPU ranks, generation progress, and 70/30 accepted counters
 - `market_intelligence.json`: cached public queue, throughput, health, and framework statistics
 - `account_capacity.json`: learned per-account active-task limits
 - `submission_exclusions.jsonl`: non-retryable model/GPU uniqueness rejections

modelhub_submmit_api/daily_runner.py

@@ -88,7 +88,7 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--skip-outcome-sync", action="store_true", help="Skip outcome sync from ModelHub before scanning")
     parser.add_argument("--skip-history-archive", action="store_true", help="Skip historical task archive download for this run")
     parser.add_argument("--dry-run", action="store_true", help="Plan the day without creating tasks")
-    parser.add_argument("--disable-gpu-strategy", action="store_true", help="Disable adaptive 50/30/20 GPU scheduling")
+    parser.add_argument("--disable-gpu-strategy", action="store_true", help="Disable adaptive 70/30 proven-GPU scheduling")
     parser.add_argument(
         "--disable-market-intelligence",
         action="store_true",

modelhub_submmit_api/gpu_strategy.py

@@ -17,13 +17,12 @@ DEFAULT_GPU_STRATEGY_PATH = Path(".modelhub_state/gpu_strategy.json")
 DEFAULT_REFRESH_SUBMISSIONS = 200
 DEFAULT_RECENT_TERMINAL_WINDOW = 1000
 DEFAULT_LONG_TERM_MIN_SAMPLES = 100
-STRATEGY_STATE_VERSION = 2
+STRATEGY_STATE_VERSION = 3
 
 LONG_TERM = "long_term"
-ALL_SUPPORTED = "all_supported"
 RECENT = "recent"
-CATEGORIES = (LONG_TERM, ALL_SUPPORTED, RECENT)
-CATEGORY_WEIGHTS = {LONG_TERM: 5, ALL_SUPPORTED: 3, RECENT: 2}
+CATEGORIES = (LONG_TERM, RECENT)
+CATEGORY_WEIGHTS = {LONG_TERM: 7, RECENT: 3}
 
 def _empty_category_counts() -> dict[str, int]:
     return {category: 0 for category in CATEGORIES}
@@ -354,7 +353,7 @@ class GPUStrategyManager:
         self.log(
             f"[strategy] {action} generation={self.state.get('generation', 0)} "
             f"accepted={self.state.get('acceptedSinceRefresh', 0)}/{self.state.get('refreshSubmissions', self.refresh_submissions)} "
-            f"categories={counts.get(LONG_TERM, 0)},{counts.get(ALL_SUPPORTED, 0)},{counts.get(RECENT, 0)} "
+            f"categories={counts.get(LONG_TERM, 0)},{counts.get(RECENT, 0)} "
             f"long={','.join(self.state.get('longTermGpus') or [])} recent={self.state.get('recentGpu') or 'n/a'}"
         )
 
@@ -377,20 +376,25 @@ class GPUStrategyManager:
                 recent = [recent_gpu]
 
         if category == LONG_TERM:
-            eligible = long_term
+            eligible = list(long_term)
             base_pattern = (5.0, 3.0, 2.0)
-        elif category == RECENT:
-            eligible = recent
-            base_pattern = (6.0, 3.0, 1.0)
         else:
-            eligible = supported
-            base_pattern = tuple(1.0 for _ in eligible)
+            eligible = list(recent)
+            base_pattern = (6.0, 3.0, 1.0)
+
+        if self.market_intelligence is not None:
+            try:
+                vetted = self.market_intelligence.eligible_gpus(supported)
+            except Exception:
+                vetted = supported
+            eligible = [gpu for gpu in eligible if gpu in vetted]
+            eligible.extend(gpu for gpu in vetted if gpu not in eligible)
         if not eligible:
             return []
 
         weights: dict[str, float] = {}
         for index, gpu in enumerate(eligible):
-            base = base_pattern[min(index, len(base_pattern) - 1)]
+            base = base_pattern[index] if index < len(base_pattern) else 0.5
             market_weight = 1.0
             if self.market_intelligence is not None:
                 try:
@@ -460,9 +464,13 @@ class GPUStrategyManager:
                     break
 
             if selected is None:
+                # With market intelligence enabled, an empty vetted pool means
+                # stop instead of silently turning fallback into exploration.
+                if self.market_intelligence is not None:
+                    break
                 # Unknown/custom GPUs can only appear when callers bypass the normal resolver.
                 selected = next((candidate for candidate in candidates if candidate_key(candidate) not in used), None)
-                actual_category = ALL_SUPPORTED
+                actual_category = planned_category
             if selected is None:
                 break
 
@@ -508,9 +516,9 @@ class GPUStrategyManager:
             for category in CATEGORIES
         }
         for candidate in candidates:
-            category = str(candidate.get("strategyCategory") or ALL_SUPPORTED)
+            category = str(candidate.get("strategyCategory") or LONG_TERM)
             if category not in category_counts:
-                category = ALL_SUPPORTED
+                category = LONG_TERM
             category_counts[category] += 1
             gpu = str(candidate.get("targetGpu") or "")
             if gpu in gpu_category_counts[category]:

modelhub_submmit_api/main.py

@@ -68,7 +68,7 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--disable-gpu-strategy",
         action="store_true",
-        help="Disable adaptive 50/30/20 GPU scheduling and keep the legacy candidate order",
+        help="Disable adaptive 70/30 proven-GPU scheduling and keep the legacy candidate order",
     )
     parser.add_argument(
         "--disable-market-intelligence",
@@ -274,22 +274,14 @@ def choose_candidate_for_gpu(
         except ValueError:
             pass
         if market_intelligence is not None:
-            discovered = market_intelligence.compatible_discovered_frameworks(
+            compatible_frameworks = market_intelligence.selectable_frameworks(
                 task_type=task_type,
                 target_gpu=target_gpu,
+                incumbent_frameworks=compatible_frameworks,
                 inspection=inspection,
             )
-            compatible_frameworks.extend(
-                framework for framework in discovered if framework not in compatible_frameworks
-            )
         if not compatible_frameworks:
             continue
-        if market_intelligence is not None:
-            compatible_frameworks = market_intelligence.rank_frameworks(
-                task_type=task_type,
-                target_gpu=target_gpu,
-                compatible_frameworks=compatible_frameworks,
-            )
 
         for framework in compatible_frameworks:
             config_params = (
@@ -451,7 +443,12 @@ def process_model_for_candidates(
             market_intelligence=market_intelligence,
         )
         if best is None:
-            skipped.append({"repoId": model.repo_id, "targetGpu": target_gpu, "reason": "no_compatible_auto_template_or_framework"})
+            reason = (
+                "no_publicly_vetted_compatible_framework"
+                if market_intelligence is not None
+                else "no_compatible_auto_template_or_framework"
+            )
+            skipped.append({"repoId": model.repo_id, "targetGpu": target_gpu, "reason": reason})
             continue
         record = candidate_to_record(best)
         if market_intelligence is not None:
@@ -706,6 +703,7 @@ def run_submission(
     target_gpus = resolve_target_gpus(args, template_selector, selected_task_types)
     if not target_gpus:
         raise RuntimeError("No auto-submittable GPUs are available for the selected task types")
+    submission_target_gpus = list(target_gpus)
 
     hf_discovery = hf_discovery or HuggingFaceDiscovery(base_url=args.hf_base_url)
     if modelhub_client is None:
@@ -809,6 +807,7 @@ def run_submission(
             "dryRun": bool(args.dry_run),
             "selectedTaskTypes": selected_task_types,
             "targetGpus": target_gpus,
+            "submissionEligibleGpus": submission_target_gpus,
             "dailyTarget": args.daily_target,
             "unlimitedDailyTarget": unlimited_daily_target,
             "submittedTodayBeforeRun": daily_snapshot["totalCount"],
@@ -893,6 +892,14 @@ def run_submission(
         except Exception:
             market_intelligence.set_local_outcome_stats(None)
         market_summary = market_intelligence.summary()
+        submission_target_gpus = market_intelligence.eligible_gpus(target_gpus)
+        market_summary["eligibleGpus"] = submission_target_gpus
+        market_summary["shadowOnlyGpus"] = [gpu for gpu in target_gpus if gpu not in submission_target_gpus]
+        print(
+            f"[market] eligible_gpus={','.join(submission_target_gpus) or 'none'} "
+            f"shadow_only={','.join(market_summary['shadowOnlyGpus']) or 'none'}",
+            flush=True,
+        )
 
     if strategy_enabled:
         strategy_manager = GPUStrategyManager(
@@ -930,7 +937,7 @@ def run_submission(
 
     scan_limit = resolve_scan_limit(
         args,
-        target_gpu_count=len(target_gpus),
+        target_gpu_count=len(submission_target_gpus),
         remaining_daily_quota=remaining_daily_quota,
         platform_available_slots=platform_available_slots,
     )
@@ -986,7 +993,7 @@ def run_submission(
             hf_discovery=hf_discovery,
             modelhub_client=modelhub_client,
             template_selector=template_selector,
-            target_gpus=target_gpus,
+            target_gpus=submission_target_gpus,
             selected_task_types=selected_task_types,
             outcome_tracker=outcome_tracker,
             submission_exclusion_store=submission_exclusion_store,
@@ -1204,6 +1211,7 @@ def run_submission(
         "dryRun": bool(args.dry_run),
         "selectedTaskTypes": selected_task_types,
         "targetGpus": target_gpus,
+        "submissionEligibleGpus": submission_target_gpus,
         "maxSubmitsPerRun": int(getattr(args, "max_submits_per_run", 0) or 0),
         "dailyTarget": args.daily_target,
         "unlimitedDailyTarget": unlimited_daily_target,

modelhub_submmit_api/market_intelligence.py

@@ -12,12 +12,15 @@ from common import parse_datetime, read_json, utc_now, write_json
 
 
 DEFAULT_MARKET_INTELLIGENCE_PATH = Path(".modelhub_state/market_intelligence.json")
-MARKET_STATE_VERSION = 2
+MARKET_STATE_VERSION = 3
 DEFAULT_QUEUE_REFRESH_SECONDS = 600
 DEFAULT_FRAMEWORK_REFRESH_SECONDS = 21_600
 DEFAULT_THROUGHPUT_WINDOW_HOURS = 6
 DEFAULT_FETCH_WORKERS = 4
-DEFAULT_FRAMEWORK_MIN_SAMPLES = 100
+DEFAULT_FRAMEWORK_MIN_SAMPLES = 300
+DEFAULT_GPU_MIN_RECENT_TERMINALS = 20
+DEFAULT_FRAMEWORK_MIN_WILSON = 0.05
+NEW_FRAMEWORK_PROMOTION_MARGIN = 1.10
 ERROR_RETRY_SECONDS = 300
 
 
@@ -73,6 +76,7 @@ def _neutral_gpu_stats(gpus: list[str]) -> dict[str, dict[str, Any]]:
             "healthFactor": 1.0,
             "queueWeight": 1.0,
             "selectionWeight": 1.0,
+            "submissionEligible": None,
             "error": "market_data_unavailable",
         }
         for gpu in gpus
@@ -156,6 +160,45 @@ class MarketIntelligenceManager:
             return False
         return list(state.get("supportedGpus") or []) == gpus and list(state.get("taskTypes") or []) == task_types
 
+    @staticmethod
+    def _catalog_compatible(state: dict[str, Any] | None, gpus: list[str], task_types: list[str]) -> bool:
+        if not state:
+            return False
+        return list(state.get("supportedGpus") or []) == gpus and list(state.get("taskTypes") or []) == task_types
+
+    @staticmethod
+    def _rescore_cached_gpu_stats(gpu_stats: dict[str, dict[str, Any]]) -> None:
+        usable = [
+            item
+            for item in gpu_stats.values()
+            if item.get("backlogHours") is not None and item.get("recentWilsonLowerBound") is not None
+        ]
+        finite_backlogs = [
+            float(item["backlogHours"])
+            for item in usable
+            if float(item["backlogHours"]) < 9_999.0
+        ]
+        median_backlog = statistics.median(finite_backlogs) if finite_backlogs else 24.0
+        max_wilson = max((float(item.get("recentWilsonLowerBound") or 0.0) for item in usable), default=0.0)
+        for item in usable:
+            backlog = max(0.25, float(item.get("backlogHours") or 9_999.0))
+            wilson = float(item.get("recentWilsonLowerBound") or 0.0)
+            health_factor = float(item.get("healthFactor") or 1.0)
+            queue_factor = _clamp((max(0.25, median_backlog) / backlog) ** 0.15, 0.70, 1.30)
+            quality_ratio = wilson / max_wilson if max_wilson > 0 else 0.0
+            quality_factor = _clamp(2.5 * (quality_ratio**2.2), 0.05, 2.5)
+            item["queueFactor"] = queue_factor
+            item["qualityFactor"] = quality_factor
+            item["queueWeight"] = _clamp(queue_factor * health_factor, 0.05, 2.0)
+            item["selectionWeight"] = _clamp(queue_factor * quality_factor * health_factor, 0.02, 3.0)
+            item["submissionEligible"] = bool(
+                item.get("canVerify") is not False
+                and health_factor >= 0.5
+                and int(item.get("recentTerminal") or 0) >= DEFAULT_GPU_MIN_RECENT_TERMINALS
+                and int(item.get("recentSuccess") or 0) > 0
+                and wilson >= DEFAULT_FRAMEWORK_MIN_WILSON
+            )
+
     def _base_state(self, gpus: list[str], task_types: list[str], now: datetime) -> dict[str, Any]:
         return {
             "version": MARKET_STATE_VERSION,
@@ -185,7 +228,14 @@ class MarketIntelligenceManager:
         gpus = list(dict.fromkeys(gpu for gpu in supported_gpus if gpu))
         tasks = list(dict.fromkeys(task for task in task_types if task))
         loaded = self._load()
-        state = loaded if self._compatible(loaded, gpus, tasks) else self._base_state(gpus, tasks, now)
+        if self._compatible(loaded, gpus, tasks):
+            state = loaded
+        elif int((loaded or {}).get("version") or 0) == 2 and self._catalog_compatible(loaded, gpus, tasks):
+            state = dict(loaded or {})
+            state["version"] = MARKET_STATE_VERSION
+            self._rescore_cached_gpu_stats(state.get("gpuStats") or {})
+        else:
+            state = self._base_state(gpus, tasks, now)
 
         queue_due = not _fresh(
             state.get("queueUpdatedAt"),
@@ -355,9 +405,16 @@ class MarketIntelligenceManager:
         result = _neutral_gpu_stats(gpus)
         for gpu, stats in fetched.items():
             backlog = max(0.25, float(stats["backlogHours"]))
-            queue_factor = _clamp(math.sqrt(max(0.25, median_backlog) / backlog), 0.35, 1.75)
+            # Queue pressure is now a tie-breaker between proven GPUs, not a way
+            # for a fast but unreliable pool to outrank a successful one.
+            queue_factor = _clamp(
+                (max(0.25, median_backlog) / backlog) ** 0.15,
+                0.70,
+                1.30,
+            )
             wilson = float(stats["recentWilsonLowerBound"])
-            quality_factor = 1.0 if max_wilson <= 0 else 0.6 + 1.4 * (wilson / max_wilson)
+            quality_ratio = wilson / max_wilson if max_wilson > 0 else 0.0
+            quality_factor = _clamp(2.5 * (quality_ratio**2.2), 0.05, 2.5)
             health_factor = 1.0
             if stats.get("canVerify") is False:
                 health_factor = 0.05
@@ -379,7 +436,14 @@ class MarketIntelligenceManager:
             stats["qualityFactor"] = quality_factor
             stats["healthFactor"] = health_factor
             stats["queueWeight"] = _clamp(queue_factor * health_factor, 0.05, 2.0)
-            stats["selectionWeight"] = _clamp(queue_factor * quality_factor * health_factor, 0.05, 3.0)
+            stats["selectionWeight"] = _clamp(queue_factor * quality_factor * health_factor, 0.02, 3.0)
+            stats["submissionEligible"] = bool(
+                stats.get("canVerify") is not False
+                and health_factor >= 0.5
+                and int(stats.get("recentTerminal") or 0) >= DEFAULT_GPU_MIN_RECENT_TERMINALS
+                and int(stats.get("recentSuccess") or 0) > 0
+                and wilson >= DEFAULT_FRAMEWORK_MIN_WILSON
+            )
             result[gpu] = stats
 
         for gpu, error in errors.items():
@@ -472,10 +536,22 @@ class MarketIntelligenceManager:
         return result
 
     def gpu_weight(self, gpu: str, *, category: str) -> float:
+        del category
         stats = ((self.state or {}).get("gpuStats") or {}).get(gpu) or {}
-        if category == "all_supported":
-            return max(0.05, float(stats.get("queueWeight") or 1.0))
-        return max(0.05, float(stats.get("selectionWeight") or 1.0))
+        return max(0.02, float(stats.get("selectionWeight") or 1.0))
+
+    def eligible_gpus(self, supported_gpus: list[str]) -> list[str]:
+        stats = (self.state or {}).get("gpuStats") or {}
+        ranked: list[tuple[float, int, str]] = []
+        for index, gpu in enumerate(supported_gpus):
+            item = stats.get(gpu) or {}
+            eligibility = item.get("submissionEligible")
+            # Missing live data falls back to the scheduler's already-proven
+            # long/recent pools. Explicitly failed public gates never do.
+            if eligibility is False:
+                continue
+            ranked.append((-float(item.get("selectionWeight") or 1.0), index, gpu))
+        return [gpu for _weight, _index, gpu in sorted(ranked)]
 
     def gpu_metadata(self, gpu: str) -> dict[str, Any]:
         stats = ((self.state or {}).get("gpuStats") or {}).get(gpu) or {}
@@ -487,6 +563,7 @@ class MarketIntelligenceManager:
             "queueBacklogHours": stats.get("backlogHours"),
             "publicRecentSuccessRate": stats.get("recentSuccessRate"),
             "publicThroughputPerHour": stats.get("throughputPerHour"),
+            "marketSubmissionEligible": stats.get("submissionEligible"),
             "marketDataStale": bool(stats.get("stale", False)),
         }
 
@@ -529,17 +606,41 @@ class MarketIntelligenceManager:
         local_samples = local_success + local_failure
         local_score = _wilson_lower_bound(local_success, local_samples)
 
-        public_qualified = public_samples >= self.framework_min_samples
+        recent_item = (self.local_outcome_stats.get("recentCombinationStats") or {}).get(local_key) or {}
+        recent_success = max(0, int(recent_item.get("successCount") or 0))
+        recent_failure = max(0, int(recent_item.get("failureCount") or 0))
+        recent_samples = recent_success + recent_failure
+        recent_rate = recent_success / recent_samples if recent_samples else None
+        consecutive_failures = max(0, int(recent_item.get("consecutiveFailures") or 0))
+        last_terminal_at = parse_datetime(recent_item.get("lastTerminalAt"))
+        circuit_reason = None
+        circuit_until = None
+        if last_terminal_at is not None and consecutive_failures >= 5:
+            circuit_reason = "five_consecutive_local_failures"
+            circuit_until = last_terminal_at + timedelta(hours=12)
+        elif last_terminal_at is not None and recent_samples >= 20 and recent_rate is not None and recent_rate < 0.20:
+            circuit_reason = "recent_local_success_below_20_percent"
+            circuit_until = last_terminal_at + timedelta(hours=6)
+        circuit_open = bool(circuit_until is not None and circuit_until > utc_now())
+        if not circuit_open:
+            circuit_reason = None
+            circuit_until = None
+
+        public_qualified = (
+            public_samples >= self.framework_min_samples
+            and public_score >= DEFAULT_FRAMEWORK_MIN_WILSON
+        )
         local_qualified = local_samples >= 20
         if public_qualified:
-            local_weight = min(0.35, local_samples / (local_samples + 50.0)) if local_samples >= 5 else 0.0
-            combined = public_score * (1.0 - local_weight) + local_score * local_weight
-        elif local_qualified:
-            combined = local_score
+            evidence_success = recent_success if recent_samples >= 5 else local_success
+            evidence_samples = recent_samples if recent_samples >= 5 else local_samples
+            evidence_score = _wilson_lower_bound(evidence_success, evidence_samples)
+            local_weight = min(0.60, evidence_samples / (evidence_samples + 100.0)) if evidence_samples >= 5 else 0.0
+            combined = public_score * (1.0 - local_weight) + evidence_score * local_weight
         else:
             combined = 0.0
         return {
-            "qualified": public_qualified or local_qualified,
+            "qualified": public_qualified and not circuit_open,
             "publicQualified": public_qualified,
             "localQualified": local_qualified,
             "combinedScore": combined,
@@ -548,8 +649,55 @@ class MarketIntelligenceManager:
             "localSamples": local_samples,
             "localSuccessRate": local_success / local_samples if local_samples else None,
             "localScore": local_score if local_samples else None,
+            "recentLocalSamples": recent_samples,
+            "recentLocalSuccessRate": recent_rate,
+            "consecutiveLocalFailures": consecutive_failures,
+            "circuitOpen": circuit_open,
+            "circuitReason": circuit_reason,
+            "circuitUntil": circuit_until.isoformat() if circuit_until else None,
         }
 
+    def selectable_frameworks(
+        self,
+        *,
+        task_type: str,
+        target_gpu: str,
+        incumbent_frameworks: list[str],
+        inspection: Any,
+    ) -> list[str]:
+        discovered = self.compatible_discovered_frameworks(
+            task_type=task_type,
+            target_gpu=target_gpu,
+            inspection=inspection,
+        )
+        candidates = list(dict.fromkeys([*incumbent_frameworks, *discovered]))
+        vetted = [
+            framework
+            for framework in candidates
+            if self._framework_evidence(task_type, target_gpu, framework)["qualified"]
+        ]
+        incumbent_scores = [
+            float(self._framework_evidence(task_type, target_gpu, framework)["combinedScore"])
+            for framework in vetted
+            if framework in incumbent_frameworks
+        ]
+        incumbent_best = max(incumbent_scores, default=0.0)
+
+        promoted: list[str] = []
+        for framework in vetted:
+            if framework in incumbent_frameworks:
+                promoted.append(framework)
+                continue
+            evidence = self._framework_evidence(task_type, target_gpu, framework)
+            if incumbent_best > 0 and float(evidence["combinedScore"]) < incumbent_best * NEW_FRAMEWORK_PROMOTION_MARGIN:
+                continue
+            promoted.append(framework)
+        return self.rank_frameworks(
+            task_type=task_type,
+            target_gpu=target_gpu,
+            compatible_frameworks=promoted,
+        )
+
     def compatible_discovered_frameworks(
         self,
         *,
@@ -609,10 +757,16 @@ class MarketIntelligenceManager:
             "frameworkMarketWilsonLowerBound": item.get("wilsonLowerBound"),
             "frameworkLocalSamples": evidence["localSamples"],
             "frameworkLocalSuccessRate": evidence["localSuccessRate"],
+            "frameworkRecentLocalSamples": evidence["recentLocalSamples"],
+            "frameworkRecentLocalSuccessRate": evidence["recentLocalSuccessRate"],
+            "frameworkConsecutiveLocalFailures": evidence["consecutiveLocalFailures"],
             "frameworkCombinedScore": evidence["combinedScore"],
             "frameworkMarketQualified": evidence["publicQualified"],
             "frameworkLocalQualified": evidence["localQualified"],
             "frameworkEvidenceQualified": evidence["qualified"],
+            "frameworkCircuitOpen": evidence["circuitOpen"],
+            "frameworkCircuitReason": evidence["circuitReason"],
+            "frameworkCircuitUntil": evidence["circuitUntil"],
             "frameworkOfficialConfigValid": bool(item.get("officialConfigValid", False)),
             "frameworkOfficialConfigStale": bool(item.get("officialConfigStale", False)),
             "frameworkConfigSource": "modelhub_live" if item.get("officialConfigValid") else "local_template",

modelhub_submmit_api/outcome_tracker.py

@@ -153,6 +153,28 @@ class OutcomeTracker:
             f"{gpu}|{fw}|{tt}": {"targetGpu": gpu, "framework": fw, "taskType": tt, **_summarize(records)}
             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():
+            recent = sorted(records, key=_outcome_record_timestamp, reverse=True)[:20]
+            consecutive_failures = 0
+            for record in recent:
+                if record.get("outcome") != "failed":
+                    break
+                consecutive_failures += 1
+            last_terminal_at = None
+            if recent:
+                last_terminal_at = (
+                    parse_datetime(recent[0].get("lastSyncTime"))
+                    or parse_datetime(recent[0].get("submitTime"))
+                )
+            recent_combination_stats[f"{gpu}|{fw}|{tt}"] = {
+                "targetGpu": gpu,
+                "framework": fw,
+                "taskType": tt,
+                **_summarize(recent),
+                "consecutiveFailures": consecutive_failures,
+                "lastTerminalAt": last_terminal_at.isoformat() if last_terminal_at else None,
+            }
 
         warnings: list[str] = []
         for gpu, summary in gpu_summaries.items():
@@ -172,6 +194,7 @@ class OutcomeTracker:
             "gpuSummaries": gpu_summaries,
             "frameworkSummaries": framework_summaries,
             "combinationStats": combination_stats,
+            "recentCombinationStats": recent_combination_stats,
             "totals": _summarize(terminal),
             "warnings": warnings,
         }
@@ -275,6 +298,11 @@ def _summarize(records: list[dict[str, Any]]) -> dict[str, Any]:
     }
 
 
+def _outcome_record_timestamp(record: dict[str, Any]) -> float:
+    timestamp = parse_datetime(record.get("lastSyncTime")) or parse_datetime(record.get("submitTime"))
+    return timestamp.timestamp() if timestamp else 0.0
+
+
 def _outcome_record_key(record: dict[str, Any]) -> str:
     task_id = record.get("taskId")
     if task_id is not None:

modelhub_submmit_api/poll_runner.py

@@ -67,7 +67,7 @@ def build_parser() -> argparse.ArgumentParser:
     parser.add_argument("--skip-outcome-sync", action="store_true", help="Skip outcome sync from ModelHub before scanning")
     parser.add_argument("--skip-history-archive", action="store_true", help="Skip historical task archive download for this run")
     parser.add_argument("--dry-run", action="store_true", help="Plan the day without creating tasks")
-    parser.add_argument("--disable-gpu-strategy", action="store_true", help="Disable adaptive 50/30/20 GPU scheduling")
+    parser.add_argument("--disable-gpu-strategy", action="store_true", help="Disable adaptive 70/30 proven-GPU scheduling")
     parser.add_argument(
         "--disable-market-intelligence",
         action="store_true",

modelhub_submmit_api/version.py

@@ -1 +1 @@
-AGENT_VERSION = "2026.08.04.1"
+AGENT_VERSION = "2026.08.05.1"

tests/test_gpu_strategy.py

@@ -14,7 +14,6 @@ if str(PACKAGE_DIR) in sys.path:
 sys.path.insert(0, str(PACKAGE_DIR))
 
 from gpu_strategy import (  # noqa: E402
-    ALL_SUPPORTED,
     LONG_TERM,
     RECENT,
     GPUStrategyManager,
@@ -55,7 +54,15 @@ class WeightedMarket:
         return {"gpu-a": 3.0, "gpu-b": 0.1, "gpu-c": 0.1, "gpu-d": 0.1}[gpu]
 
     def gpu_metadata(self, gpu: str) -> dict:
-        return {"marketWeight": self.gpu_weight(gpu, category=ALL_SUPPORTED)}
+        return {"marketWeight": self.gpu_weight(gpu, category=LONG_TERM)}
+
+    def eligible_gpus(self, supported_gpus: list[str]) -> list[str]:
+        return list(supported_gpus)
+
+
+class VettedMarket(WeightedMarket):
+    def eligible_gpus(self, supported_gpus: list[str]) -> list[str]:
+        return [gpu for gpu in supported_gpus if gpu in {"gpu-b", "gpu-c"}]
 
 
 class GPUStrategyTests(unittest.TestCase):
@@ -80,14 +87,14 @@ class GPUStrategyTests(unittest.TestCase):
         self.assertNotIn("tiny", snapshot["longTermGpus"])
 
     def test_weighted_category_planner_is_exact_over_200_accepts(self) -> None:
-        counts = {LONG_TERM: 0, ALL_SUPPORTED: 0, RECENT: 0}
+        counts = {LONG_TERM: 0, RECENT: 0}
         for _ in range(200):
             category = choose_next_category(counts)
             counts[category] += 1
 
-        self.assertEqual({LONG_TERM: 100, ALL_SUPPORTED: 60, RECENT: 40}, counts)
+        self.assertEqual({LONG_TERM: 140, RECENT: 60}, counts)
 
-    def test_candidate_order_uses_50_30_20_for_first_strategy_generation(self) -> None:
+    def test_candidate_order_uses_70_30_without_exploration(self) -> None:
         supported = ["gpu-a", "gpu-b", "gpu-c", "gpu-d"]
         snapshot = build_strategy_snapshot([], supported_gpus=supported)
         manager = GPUStrategyManager("unused.json")
@@ -101,9 +108,9 @@ class GPUStrategyTests(unittest.TestCase):
         ordered = manager.order_candidates(candidates)
         categories = Counter(candidate["strategyCategory"] for candidate in ordered[:200])
 
-        self.assertEqual(100, categories[LONG_TERM])
-        self.assertEqual(60, categories[ALL_SUPPORTED])
-        self.assertEqual(40, categories[RECENT])
+        self.assertEqual(140, categories[LONG_TERM])
+        self.assertEqual(60, categories[RECENT])
+        self.assertNotIn("all_supported", categories)
 
     def test_refresh_happens_only_after_200_accepted_submissions(self) -> None:
         with tempfile.TemporaryDirectory() as temporary_dir:
@@ -126,7 +133,7 @@ class GPUStrategyTests(unittest.TestCase):
             self.assertEqual(200, manager.state["acceptedTotal"])
             self.assertEqual(0, manager.state["acceptedSinceRefresh"])
 
-    def test_market_weights_change_gpu_mix_without_changing_50_30_20_split(self) -> None:
+    def test_market_weights_change_gpu_mix_without_changing_70_30_split(self) -> None:
         supported = ["gpu-a", "gpu-b", "gpu-c", "gpu-d"]
         manager = GPUStrategyManager("unused.json", market_intelligence=WeightedMarket())
         manager.state = build_strategy_snapshot([], supported_gpus=supported)
@@ -138,15 +145,26 @@ class GPUStrategyTests(unittest.TestCase):
 
         ordered = manager.order_candidates(candidates)[:200]
         category_counts = Counter(candidate["strategyCategory"] for candidate in ordered)
-        exploration_counts = Counter(
-            candidate["targetGpu"]
-            for candidate in ordered
-            if candidate["strategyCategory"] == ALL_SUPPORTED
-        )
+        gpu_counts = Counter(candidate["targetGpu"] for candidate in ordered)
 
-        self.assertEqual({LONG_TERM: 100, ALL_SUPPORTED: 60, RECENT: 40}, category_counts)
-        self.assertGreater(exploration_counts["gpu-a"], exploration_counts["gpu-b"])
-        self.assertTrue(all(exploration_counts[gpu] > 0 for gpu in supported))
+        self.assertEqual({LONG_TERM: 140, RECENT: 60}, category_counts)
+        self.assertGreater(gpu_counts["gpu-a"], gpu_counts["gpu-b"])
+        self.assertNotIn("all_supported", category_counts)
+
+    def test_unvetted_gpus_receive_zero_submissions(self) -> None:
+        supported = ["gpu-a", "gpu-b", "gpu-c", "gpu-d"]
+        manager = GPUStrategyManager("unused.json", market_intelligence=VettedMarket())
+        manager.state = build_strategy_snapshot([], supported_gpus=supported)
+        candidates = [
+            {"repoId": f"owner/model-{model}", "targetGpu": gpu}
+            for model in range(50)
+            for gpu in supported
+        ]
+
+        ordered = manager.order_candidates(candidates)
+
+        self.assertTrue(ordered)
+        self.assertEqual({"gpu-b", "gpu-c"}, {candidate["targetGpu"] for candidate in ordered})
 
 
 if __name__ == "__main__":

tests/test_market_intelligence.py

@@ -13,6 +13,7 @@ if str(PACKAGE_DIR) in sys.path:
 sys.path.insert(0, str(PACKAGE_DIR))
 
 from main import choose_candidate_for_gpu  # noqa: E402
+from common import write_json  # noqa: E402
 from market_intelligence import MarketIntelligenceManager  # noqa: E402
 from models import HFModelSummary, ModelInspection  # noqa: E402
 from template_selector import TemplateSelector  # noqa: E402
@@ -158,6 +159,52 @@ class MarketIntelligenceTests(unittest.TestCase):
             )
             self.assertEqual(first_call_count, client.calls)
 
+    def test_version_two_snapshot_is_migrated_without_losing_public_evidence(self) -> None:
+        with tempfile.TemporaryDirectory() as temporary_dir:
+            path = Path(temporary_dir) / "market.json"
+            now = datetime.now(timezone.utc)
+            write_json(
+                path,
+                {
+                    "version": 2,
+                    "supportedGpus": ["fast"],
+                    "taskTypes": ["text-generation"],
+                    "queueUpdatedAt": now.isoformat(),
+                    "frameworkUpdatedAt": now.isoformat(),
+                    "queueError": None,
+                    "frameworkError": None,
+                    "gpuStats": {
+                        "fast": {
+                            "gpu": "fast",
+                            "canVerify": True,
+                            "recentTerminal": 100,
+                            "recentSuccess": 80,
+                            "recentWilsonLowerBound": 0.70,
+                            "backlogHours": 10.0,
+                            "healthFactor": 1.0,
+                        }
+                    },
+                    "frameworkStats": {
+                        "text-generation": {
+                            "fast": {
+                                "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.20}
+                            }
+                        }
+                    },
+                },
+            )
+            client = FailingMarketClient()
+            manager = MarketIntelligenceManager(path, log_fn=lambda _message: None)
+            state = manager.prepare(
+                client,
+                supported_gpus=["fast"],
+                task_types=["text-generation"],
+                now=now + timedelta(seconds=30),
+            )
+            self.assertEqual(3, state["version"])
+            self.assertTrue(state["gpuStats"]["fast"]["submissionEligible"])
+            self.assertEqual(0, client.calls)
+
     def test_framework_ranking_uses_confidence_bound_and_ignores_tiny_samples(self) -> None:
         manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
         manager.state = {
@@ -332,6 +379,76 @@ class MarketIntelligenceTests(unittest.TestCase):
         assert candidate is not None
         self.assertEqual("vllm", candidate.framework)
 
+    def test_unproven_public_framework_receives_no_submission(self) -> None:
+        manager = MarketIntelligenceManager("unused.json")
+        manager.state = {
+            "frameworkStats": {
+                "text-generation": {
+                    "Cambricon_mlu-370-x4": {
+                        "vllm": {"modelCount": 299, "wilsonLowerBound": 0.50},
+                    }
+                }
+            }
+        }
+        candidate = choose_candidate_for_gpu(
+            model=HFModelSummary(
+                repo_id="owner/model",
+                downloads=100,
+                last_modified=None,
+                pipeline_tag="text-generation",
+            ),
+            inspection=ModelInspection(repo_id="owner/model", weight_files=["model.safetensors"]),
+            template_selector=TemplateSelector(),
+            task_types=["text-generation"],
+            target_gpu="Cambricon_mlu-370-x4",
+            market_intelligence=manager,
+        )
+        self.assertIsNone(candidate)
+
+    def test_five_consecutive_local_failures_circuit_break_framework(self) -> None:
+        manager = MarketIntelligenceManager("unused.json", framework_min_samples=100)
+        manager.state = {
+            "frameworkStats": {
+                "text-generation": {
+                    "Iluvatar_bi-100": {
+                        "vllm": {"modelCount": 1000, "wilsonLowerBound": 0.40},
+                        "transformers": {"modelCount": 1000, "wilsonLowerBound": 0.20},
+                    }
+                }
+            }
+        }
+        manager.set_local_outcome_stats(
+            {
+                "combinationStats": {},
+                "recentCombinationStats": {
+                    "Iluvatar_bi-100|vllm|text-generation": {
+                        "successCount": 0,
+                        "failureCount": 5,
+                        "consecutiveFailures": 5,
+                        "lastTerminalAt": datetime.now(timezone.utc).isoformat(),
+                    }
+                },
+            }
+        )
+        candidate = choose_candidate_for_gpu(
+            model=HFModelSummary(
+                repo_id="owner/model",
+                downloads=100,
+                last_modified=None,
+                pipeline_tag="text-generation",
+            ),
+            inspection=ModelInspection(repo_id="owner/model", weight_files=["model.safetensors"]),
+            template_selector=TemplateSelector(),
+            task_types=["text-generation"],
+            target_gpu="Iluvatar_bi-100",
+            market_intelligence=manager,
+        )
+        self.assertIsNotNone(candidate)
+        assert candidate is not None
+        self.assertEqual("transformers", candidate.framework)
+        metadata = manager.framework_metadata("text-generation", "Iluvatar_bi-100", "vllm")
+        self.assertTrue(metadata["frameworkCircuitOpen"])
+
 
 if __name__ == "__main__":
     unittest.main()