fix: restore stage-based Alpha check classification

backend/app/alphas.py

@@ -7,7 +7,7 @@ from datetime import datetime
 from sqlalchemy import or_, select, update
 
 from .models import Alpha, Research, ResearchTag, SelfCorrelation, now
-from .platform_checks import split_checks, submission_limits
+from .platform_checks import check_result, split_checks, submission_limits
 from .research.provenance import source_alpha_ids
 
 METRIC_FIELDS = (
@@ -20,16 +20,17 @@ def failed_checks(checks):
     """Return failed Alpha check names, excluding submission limits and local correlation."""
     return [
         check.get("name") if isinstance(check.get("name"), str) else "未命名检查"
-        for check in split_checks(checks)[0] if isinstance(check, dict) and check.get("result") == "FAIL"
+        for check in split_checks(checks)[0] if isinstance(check, dict) and check_result(check) == "FAIL"
     ] if isinstance(checks, list) else []
 
 
-def snapshot_columns(settings, metrics, checks):
+def snapshot_columns(settings, metrics, checks, *, checked=False):
     """Derive list fields from a platform snapshot, preserving missing metrics as null.
 
     Submission limits are excluded. Only explicit Alpha FAIL results count.
-    Empty, malformed and unfinished Alpha checks are
-    pending; all known checks passing without PROD_CORRELATION is only a pre-check.
+    Sync snapshots with no failures are PRE_CHECK; a completed explicit /check
+    with no failures is PASS. WARNING/PENDING do not count as failures, matching
+    the legacy workflow. Empty, malformed or unknown results remain PENDING.
     No submission eligibility or activity eligibility is inferred here.
     """
     settings = settings if isinstance(settings, dict) else {}
@@ -40,10 +41,10 @@ def snapshot_columns(settings, metrics, checks):
     by_name = {check["name"]: check for check in valid if isinstance(check.get("name"), str)}
     if failures:
         check_type = "FAIL_1" if failures == 1 else "FAIL_2"
-    elif not checks or len(valid) != len(checks) or any(check.get("result") != "PASS" for check in valid):
+    elif not checks or len(valid) != len(checks) or any(check_result(check) not in ("PASS", "WARNING", "PENDING") for check in valid):
         check_type = "PENDING"
     else:
-        check_type = "PASS" if "PROD_CORRELATION" in by_name else "PRE_CHECK"
+        check_type = "PASS" if checked else "PRE_CHECK"
     # /check values are freshest; submitted snapshots also expose a scalar in IS.
     prod_correlation = number(by_name.get("PROD_CORRELATION", {}).get("value"))
     if prod_correlation is None:
@@ -66,12 +67,13 @@ def snapshot_columns(settings, metrics, checks):
     }
 
 
-def check_summary(checks):
+def check_summary(checks, *, check_type):
     """Separate cached Alpha findings from submission limits; infer no live eligibility."""
     return {
-        "check_type": snapshot_columns({}, {}, checks)["check_type"],
+        "check_type": check_type,
         "failed_checks": failed_checks(checks),
         "submission_limits": submission_limits(checks),
+        "meaning": "PRE_CHECK 为同步无失败项；PASS 为主动检查完成且无失败项。PENDING/WARNING 不算失败，不代表全部检查项 PASS 或当前可提交",
     }
 
 

backend/app/platform_checks.py

@@ -1,6 +1,12 @@
 """Classify platform evidence without discarding unknown checks or inferring eligibility."""
 
 
+def check_result(check):
+    """Normalize known upstream result casing without rewriting the raw evidence."""
+    value = check.get("result") if isinstance(check, dict) else None
+    return value.upper() if isinstance(value, str) else None
+
+
 def is_submission_limit(check):
     """Recognize only the confirmed account-limit check; unknown names remain Alpha checks."""
     return isinstance(check, dict) and check.get("name") == "REGULAR_SUBMISSION"
@@ -16,8 +22,8 @@ def split_checks(checks):
 def submission_limits(checks):
     """Summarize the observed limit, never the account's current allowance or reset time."""
     _, limits = split_checks(checks)
-    status = "blocked" if any(c.get("result") == "FAIL" for c in limits) else (
-        "not_blocked" if limits and all(c.get("result") == "PASS" for c in limits) else "unknown"
+    status = "blocked" if any(check_result(c) == "FAIL" for c in limits) else (
+        "not_blocked" if limits and all(check_result(c) == "PASS" for c in limits) else "unknown"
     )
     return {"status": status, "checks": limits,
             "meaning": "仅反映缓存观测时的提交限制，不代表当前额度或正式提交资格"}

backend/app/research_access/service.py

@@ -222,7 +222,7 @@ class ResearchAccess:
         return {"alpha_id": args.alpha_id, "snapshot": submission_fingerprint(context),
                 **context, "descriptions": {key: item["description"] for key, item in context["sections"].items()},
                 "can_check": alpha.status == "UNSUBMITTED" and await correlation_allows_check(self.db, args.alpha_id),
-                "checks": alpha.checks, "check_summary": check_summary(alpha.checks), "source": "local_cache", "production_submission": False,
+                "checks": alpha.checks, "check_summary": check_summary(alpha.checks, check_type=alpha.check_type), "source": "local_cache", "production_submission": False,
                 "job_id": job.id if job else None, "job_status": job.status if job else None,
                 "checked_at": job.checkpoint.get("checked_at") if job else None}
 

backend/app/submission.py

@@ -207,7 +207,7 @@ def router(runner, ai):
             return {
                 "snapshot": fingerprint(context),
                 "checks": alpha.checks,
-                "check_summary": check_summary(alpha.checks),
+                "check_summary": check_summary(alpha.checks, check_type=alpha.check_type),
                 "sections": context["sections"],
                 "descriptions": {key: item["description"] for key, item in context["sections"].items()},
                 "model": config.description_model,
@@ -355,7 +355,7 @@ async def run_check(runner, job_id, payload):
         alpha.checks = sanitize(checks)
         alpha.is_metrics = {**alpha.is_metrics, "checks": alpha.checks}
         alpha.raw = {**alpha.raw, "is": {**(alpha.raw.get("is") or {}), "checks": alpha.checks}}
-        for key, value in snapshot_columns(alpha.settings, alpha.is_metrics, alpha.checks).items():
+        for key, value in snapshot_columns(alpha.settings, alpha.is_metrics, alpha.checks, checked=True).items():
             setattr(alpha, key, value)
         db.add(JobItem(job_id=job_id, alpha_id=alpha_id))
         job.processed = 1