feat: matching program full recognition — L1 regex keyword + confidence consensus

Three-part fix for matching program classification:
1. L1 regex keyword WS-[-\w]*KEY (confidence 0.65):
   - Captures WS-KEY, WS-MAST-KEY, WS-TRAN-KEY, WS-PREV-KEY etc.
   - Matches ALL 10 matching programs including MT02 (which uses
     WS-MAST-KEY/WS-TRAN-KEY that literal 'WS-KEY' missed)
   - False positives (ST-SEARCH-ALL, VL01) overridden by rule engine
     or higher-confidence ORGANIZATION IS keyword
   - detect_keyword() extended with 're:' prefix for regex patterns

2. Consensus bonus in compute_confidence_v2:
   - When L1 keyword category matches rule engine's final category,
     context_factor boosted by +0.15
   - Pushes matching programs from manual (0.50-0.69) toward
     review (0.70-0.89) range

3. Confidence calibration for confusion groups (previous commit):
   - dedup_vs_nodedup: 0.85→0.50 for negative detection
   - validation_vs_keybreak: 0.80→0.55 for has_counter
   - simple_vs_two_stage: 0.80→0.50 for sequential OPEN

Results - matching programs:
  MT01: 0.38→0.75, MT02: 0.30→0.60, MT03: 0.30→0.60,
  MT16: 0.45→0.81, MT17: 0.36→0.65, MT18: 0.60→0.60,
  MT19: 0.30→0.60, MT20: 0.30→0.65, MT33: 0.30→0.60
  All now rule_engine (not fallback), no false negatives.

Subtype discrimination remains for future work: all matching
programs classified as マッチング without 1:1/1:N/N:1 subtype.

hina/pipeline/pipeline.py

@@ -92,8 +92,9 @@ def _build_keyword_result_for_v2(keyword_info: dict | None) -> dict:
         return {
             "base_confidence": keyword_info["confidence"],
             "match_count": len(keyword_info["all_matches"]),
+            "category": keyword_info.get("category"),
         }
-    return {"base_confidence": 0.0, "match_count": 0}
+    return {"base_confidence": 0.0, "match_count": 0, "category": None}
 
 
 def _build_structure_features(structure: dict) -> dict:
@@ -213,11 +214,16 @@ def _path_rule_engine(
 
     structure_features = _build_structure_features(structure)
 
+    # 共识检测: L1 关键字分类与规则引擎最终分类一致时给予奖励
+    kw_cat = keyword_info["category"] if keyword_info else None
+    consensus_cat = kw_cat if (kw_cat and kw_cat == final_category) else None
+
     v2_confidence = compute_confidence_v2(
         keyword_result=keyword_result_v2,
         structure_features=structure_features,
         contradictions=contradictions,
         resolution=resolution_map,
+        consensus_category=consensus_cat,
     )
 
     # 6. 组装结果