From d8fa0af825ef967020dad9070a23d9ae07fc219c Mon Sep 17 00:00:00 2001
From: ai_coder <ai_coder@local.home>
Date: Fri, 16 Oct 2026 15:16:36 +0000
Subject: [PATCH] fix: agent arena matching and score extraction
 (net_improvement)

---
 app/main.py | 22 +++++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/app/main.py b/app/main.py
index 94ec997..e8934f0 100644
--- a/app/main.py
+++ b/app/main.py
@@ -119,7 +119,11 @@ def arena_match(model_id: str, arena: dict[str, dict]) -> Optional[dict]:
     # 1) точное совпадение нормализованного имени
     bn = _norm(base)
     for k, v in arena.items():
-        if _norm(k) == bn:
+        # убираем вендор-хвост " Anthropic · Proprietary" из названия модели арены
+        model_name_only = v.get("model", k).split(" · ")[0]
+        model_name_only = re.sub(r"\s+(Google|Anthropic|OpenAI|Meta|Nvidia|Mistral|DeepSeek|Alibaba)$", "", model_name_only, flags=re.I)
+        an = _norm(model_name_only)
+        if an == bn:
             return v
 
     # 2) без суффиксов тиров
@@ -127,15 +131,20 @@ def arena_match(model_id: str, arena: dict[str, dict]) -> Optional[dict]:
     cleaned = re.sub(r"-(thinking|tiered|disabled|low|medium|high|xhigh|max|ultra|mini)", "", cleaned)
     cn = _norm(cleaned)
     for k, v in arena.items():
-        if _norm(k) == cn:
+        model_name_only = v.get("model", k).split(" · ")[0]
+        model_name_only = re.sub(r"\s+(Google|Anthropic|OpenAI|Meta|Nvidia|Mistral|DeepSeek|Alibaba)$", "", model_name_only, flags=re.I)
+        # убираем скобки тиров типа (Max), (High)
+        m_cleaned = re.sub(r"\s*\([^)]+\)", "", model_name_only)
+        if _norm(m_cleaned) == cn:
             return v
 
     # 3) containment
     for candidate in (base, cleaned):
         gk = _norm_keep(candidate)
-        if len(gk) >= 12 and (re.search(r"\d", gk) or "-" in gk):
+        if len(gk) >= 10 and (re.search(r"\d", gk) or "-" in gk):
             for k, v in arena.items():
-                am = _norm_keep(k)
+                model_name_only = v.get("model", k).split(" · ")[0]
+                am = _norm_keep(model_name_only)
                 if gk == am or am.endswith("-" + gk) or ("-" + gk) in am or (gk in am and len(gk) >= 12):
                     return v
     return None
@@ -171,6 +180,9 @@ def sync_models():
         ta = arena_match(mid, arena_text)
         ga = arena_match(mid, arena_agent)
 
+        # у agent-борда главная метрика — net_improvement, а не score
+        agent_score = ga.get("net_improvement") or ga.get("score") if ga else None
+
         db.execute("""
             INSERT INTO models (id, provider, name, arena_text_score, arena_text_rank,
                                 arena_agent_score, arena_agent_rank, updated_at)
@@ -185,7 +197,7 @@ def sync_models():
             mid, provider, name,
             ta["score"] if ta else None,
             ta["rank"] if ta else None,
-            ga["score"] if ga else None,
+            agent_score,
             ga["rank"] if ga else None,
             now,
         ))