Create app.py

app.py

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+"""
+FINAL Bench v4.0 — Baseline (Non-AGI) Evaluation System
+=========================================================
+Frontier Intelligence Nexus for AGI-Level Verification
+
+★ Baseline (Non-AGI) single-call evaluation
+★ Multi-Provider: OpenAI / Anthropic / Google (Gemini)
+★ 100 Tasks · 15 Domains · 8 TICOS Types · 5-Axis · 5-Stage AGI Grade
+★ Judge: GPT-5.2 Structured Output (default)
+★ Dataset: HuggingFace FINAL-Bench/Metacognitive
+
+🔒 MetaCog (Self-Correction Protocol) evaluation: COMING SOON
+   The 3-Phase Protocol (Initial → Self-Review → Correction) is the core
+   contribution of the paper and will be released in a future update.
+
+Author: Ginigen AI — Choi Sunyoung
+Paper: "FINAL Bench: Measuring Functional Metacognitive Reasoning in LLMs" (2026)
+License: Apache 2.0
+"""
+
+import json, os, time, csv, io, re, html, hashlib, sqlite3, threading
+from datetime import datetime
+from dataclasses import dataclass, field, asdict
+from typing import List, Dict, Optional
+import requests
+import numpy as np
+import gradio as gr
+from concurrent.futures import ThreadPoolExecutor
+from datasets import load_dataset
+
+# ════════════════════════════════════════════════════════════════
+# §1. Data Structures & Constants
+# ════════════════════════════════════════════════════════════════
+
+DOMAIN_INFO = {
+    "Mathematics & Logic":  {"icon":"🔢","color":"#FF6B35"},
+    "Science":              {"icon":"🔬","color":"#7B2FF7"},
+    "Philosophy":           {"icon":"🤔","color":"#00B4D8"},
+    "Medicine":             {"icon":"🏥","color":"#2EC4B6"},
+    "Economics":            {"icon":"📈","color":"#E63946"},
+    "History":              {"icon":"📜","color":"#F4A261"},
+    "War & Security":       {"icon":"🛡️","color":"#264653"},
+    "Space & Physics":      {"icon":"🚀","color":"#6C63FF"},
+    "Chemistry & Biology":  {"icon":"🧬","color":"#06D6A0"},
+    "Language & Writing":   {"icon":"✍️","color":"#EF476F"},
+    "Literature":           {"icon":"📖","color":"#8338EC"},
+    "Art":                  {"icon":"🎨","color":"#FF006E"},
+    "Religion & Mythology": {"icon":"🕊️","color":"#FFD166"},
+    "Ethics":               {"icon":"⚖️","color":"#118AB2"},
+    "AI & Technology":      {"icon":"🤖","color":"#073B4C"},
+}
+
+GRADE_WEIGHT = {"A": 1.5, "B": 1.0, "C": 0.7}
+
+RUBRIC = {
+    "process_quality":        {"weight": 0.25, "desc": "Systematic reasoning transparency"},
+    "metacognitive_accuracy": {"weight": 0.25, "desc": "Confidence calibration + uncertainty honesty"},
+    "error_recovery":         {"weight": 0.20, "desc": "Mid-analysis self-correction"},
+    "integration_depth":      {"weight": 0.15, "desc": "Multi-perspective synthesis"},
+    "final_correctness":      {"weight": 0.15, "desc": "Answer accuracy and completeness"},
+}
+
+AXIS_MAP = {
+    "generalization": {"rubrics": ["process_quality", "final_correctness"], "ticos": []},
+    "reasoning":      {"rubrics": ["process_quality", "error_recovery"],   "ticos": ["E_SelfCorrecting", "C_ProgressiveDiscovery"]},
+    "planning":       {"rubrics": ["integration_depth", "process_quality"],"ticos": ["D_MultiConstraint", "H_DecisionUnderUncertainty"]},
+    "reliability":    {"rubrics": ["metacognitive_accuracy"],             "ticos": ["E_SelfCorrecting", "G_PivotDetection"]},
+    "safety":         {"rubrics": ["error_recovery", "metacognitive_accuracy"], "ticos": ["A_TrapEscape", "G_PivotDetection"]},
+}
+
+AGI_STAGES = [
+    {"stage":1,"name":"FINAL-Partial","label":"Partial Intelligence",     "min":0, "max":39, "color":"#f44336"},
+    {"stage":2,"name":"FINAL-Proto",  "label":"Proto Intelligence",       "min":40,"max":59, "color":"#ff9800"},
+    {"stage":3,"name":"FINAL-Pre",    "label":"Pre-AGI",                  "min":60,"max":79, "color":"#2196f3"},
+    {"stage":4,"name":"FINAL-Pass",   "label":"AGI Achieved",             "min":80,"max":94, "color":"#4caf50"},
+    {"stage":5,"name":"FINAL-Post",   "label":"Operationally Mature AGI", "min":95,"max":100,"color":"#9c27b0"},
+]
+
+@dataclass
+class FinalTask:
+    task_id:str; domain:str; grade:str; ticos_type:str
+    difficulty:str; lens:str; title:str; prompt:str
+    expected_behavior:str; hidden_trap:str
+    ticos_required:List[str]=field(default_factory=list)
+    metadata:Dict=field(default_factory=dict)
+
+# ════════════════════════════════════════════════════════════════
+# §2. Load Dataset from HuggingFace
+# ════════════════════════════════════════════════════════════════
+
+def load_tasks():
+    print("📥 Loading FINAL-Bench/Metacognitive from HuggingFace...")
+    try:
+        ds = load_dataset("FINAL-Bench/Metacognitive", split="train")
+        tasks = []
+        for row in ds:
+            tr = row.get("ticos_required", [])
+            if isinstance(tr, str):
+                try: tr = json.loads(tr)
+                except: tr = [x.strip() for x in tr.split(",") if x.strip()]
+            tasks.append(FinalTask(
+                task_id=row["task_id"], domain=row["domain"], grade=row["grade"],
+                ticos_type=row["ticos_type"], difficulty=row["difficulty"],
+                lens=row.get("lens",""), title=row.get("title",row["task_id"]),
+                prompt=row["prompt"], expected_behavior=row.get("expected_behavior",""),
+                hidden_trap=row.get("hidden_trap",""),
+                ticos_required=tr if isinstance(tr, list) else [], metadata={}
+            ))
+        print(f"  ✅ Loaded {len(tasks)} tasks from HuggingFace")
+        return tasks
+    except Exception as e:
+        print(f"  ⚠️ HF load failed: {e}, trying local...")
+        for p in ["FINAL_Bench_v3.json","/mnt/user-data/uploads/FINAL_Bench_v3.json",
+                   os.path.join(os.path.dirname(os.path.abspath(__file__)),"FINAL_Bench_v3.json")]:
+            if os.path.exists(p):
+                with open(p,"r",encoding="utf-8") as f: data=json.load(f)
+                print(f"  ✅ Loaded from {p}")
+                return [FinalTask(task_id=t["task_id"],domain=t["domain"],grade=t["grade"],
+                    ticos_type=t["ticos_type"],difficulty=t["difficulty"],lens=t.get("lens",""),
+                    title=t["title"],prompt=t["prompt"],expected_behavior=t.get("expected_behavior",""),
+                    hidden_trap=t.get("hidden_trap",""),ticos_required=t.get("ticos_required",[]),
+                    metadata=t.get("metadata",{})) for t in data["tasks"]]
+        raise FileNotFoundError("Dataset not found!")
+
+ALL_TASKS = load_tasks()
+print(f"✅ FINAL Bench v4.0: {len(ALL_TASKS)} tasks loaded")
+
+# ════════════════════════════════════════════════════════════════
+# §3. Multi-Provider API Layer
+# ════════════════════════════════════════════════════════════════
+
+PROVIDER_MODELS = {
+    "OpenAI": {
+        "gpt-5.2":"GPT-5.2 (flagship)","gpt-5-mini":"GPT-5 Mini",
+        "gpt-4.1":"GPT-4.1","o4-mini":"o4-mini (reasoning)","gpt-4o":"GPT-4o",
+    },
+    "Anthropic": {
+        "claude-opus-4-6":"Claude Opus 4.6","claude-sonnet-4-5-20250929":"Claude Sonnet 4.5",
+        "claude-haiku-4-5-20251001":"Claude Haiku 4.5",
+    },
+    "Google": {
+        "gemini-3-pro":"Gemini 3 Pro","gemini-2.5-pro":"Gemini 2.5 Pro",
+        "gemini-2.5-flash":"Gemini 2.5 Flash",
+    },
+}
+
+ALL_EVAL_MODELS = {}
+for prov, models in PROVIDER_MODELS.items():
+    for mid, label in models.items():
+        ALL_EVAL_MODELS[f"{label} [{prov}]"] = {"id": mid, "provider": prov}
+
+JUDGE_MODELS = {"gpt-5.2":"GPT-5.2 (default)","gpt-4.1":"GPT-4.1","gpt-4o":"GPT-4o"}
+
+def _strip_think(text):
+    if not text: return text
+    for tag in ['think','thinking','reasoning','reflection']:
+        text = re.sub(rf'<{tag}>.*?</{tag}>','',text,flags=re.DOTALL)
+    return text.strip()
+
+def call_openai(prompt, system="", api_key="", model="gpt-5.2",
+                max_tokens=8192, temperature=0.6, reasoning_effort=None):
+    headers={"Content-Type":"application/json","Authorization":f"Bearer {api_key}"}
+    messages=[]
+    if system: messages.append({"role":"system","content":system})
+    messages.append({"role":"user","content":prompt})
+    payload={"model":model,"max_completion_tokens":max_tokens,"temperature":temperature,"messages":messages}
+    if reasoning_effort: payload["reasoning_effort"]=reasoning_effort
+    for attempt in range(3):
+        try:
+            r=requests.post("https://api.openai.com/v1/chat/completions",headers=headers,json=payload,timeout=300)
+            r.raise_for_status(); c=r.json()["choices"][0]["message"]["content"]
+            return _strip_think(c) if c else "[EMPTY]"
+        except requests.exceptions.HTTPError:
+            if r.status_code==429: time.sleep(5*(attempt+1)); continue
+            try: err=r.json().get("error",{}).get("message","")
+            except: err=str(r.status_code)
+            return f"[API_ERROR] {err}"
+        except Exception as e:
+            if attempt<2: time.sleep(3*(attempt+1))
+            else: return f"[API_ERROR] {e}"
+
+def call_anthropic(prompt, system="", api_key="", model="claude-opus-4-6",
+                   max_tokens=8192, temperature=0.6):
+    headers={"Content-Type":"application/json","x-api-key":api_key,"anthropic-version":"2023-06-01"}
+    payload={"model":model,"max_tokens":max_tokens,"temperature":temperature,
+             "messages":[{"role":"user","content":prompt}]}
+    if system: payload["system"]=system
+    for attempt in range(3):
+        try:
+            r=requests.post("https://api.anthropic.com/v1/messages",headers=headers,json=payload,timeout=300)
+            r.raise_for_status(); data=r.json()
+            text="".join(b.get("text","") for b in data.get("content",[]) if b.get("type")=="text")
+            return _strip_think(text) if text else "[EMPTY]"
+        except requests.exceptions.HTTPError:
+            if r.status_code==429: time.sleep(5*(attempt+1)); continue
+            try: err=r.json().get("error",{}).get("message","")
+            except: err=str(r.status_code)
+            return f"[API_ERROR] {err}"
+        except Exception as e:
+            if attempt<2: time.sleep(3*(attempt+1))
+            else: return f"[API_ERROR] {e}"
+
+def call_gemini(prompt, system="", api_key="", model="gemini-3-pro",
+                max_tokens=8192, temperature=0.6):
+    url=f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent"
+    headers={"Content-Type":"application/json"}; params={"key":api_key}
+    payload={"contents":[{"role":"user","parts":[{"text":prompt}]}],
+             "generationConfig":{"maxOutputTokens":max_tokens,"temperature":temperature}}
+    if system: payload["systemInstruction"]={"parts":[{"text":system}]}
+    for attempt in range(3):
+        try:
+            r=requests.post(url,headers=headers,params=params,json=payload,timeout=300)
+            r.raise_for_status(); data=r.json()
+            cands=data.get("candidates",[])
+            if cands:
+                text="".join(p.get("text","") for p in cands[0].get("content",{}).get("parts",[]))
+                return _strip_think(text) if text else "[EMPTY]"
+            return "[EMPTY]"
+        except requests.exceptions.HTTPError:
+            if r.status_code==429: time.sleep(5*(attempt+1)); continue
+            try: err=r.json().get("error",{}).get("message","")
+            except: err=str(r.status_code)
+            return f"[API_ERROR] {err}"
+        except Exception as e:
+            if attempt<2: time.sleep(3*(attempt+1))
+            else: return f"[API_ERROR] {e}"
+
+def call_model(prompt, system="", api_key="", model_id="gpt-5.2",
+               provider="OpenAI", max_tokens=8192, temperature=0.6):
+    if provider=="OpenAI": return call_openai(prompt,system,api_key,model_id,max_tokens,temperature)
+    elif provider=="Anthropic": return call_anthropic(prompt,system,api_key,model_id,max_tokens,temperature)
+    elif provider=="Google": return call_gemini(prompt,system,api_key,model_id,max_tokens,temperature)
+    return f"[API_ERROR] Unknown provider: {provider}"
+
+# ════════════════════════════════════════════════════════════════
+# §4. Structured Judge (GPT-5.2)
+# ════════════════════════════════════════════════════════════════
+
+JUDGE_SYSTEM = """You are a FINAL Bench judge for AGI-Level Verification.
+Score each rubric using ONLY: 0.0 / 0.25 / 0.5 / 0.75 / 1.0
+
+RUBRIC:
+process_quality (25%): Systematic step-by-step reasoning. Complete answers score higher.
+metacognitive_accuracy (25%): Confidence calibration. Overconfidence=0.25 max. Honest uncertainty=0.75+
+error_recovery (20%): EXPLICIT self-correction. Score 0.5+ if ANY self-corrections exist.
+integration_depth (15%): Multi-perspective synthesis + emergent insights
+final_correctness (15%): Answer accuracy and completeness. INCOMPLETE answers get 0.25 max.
+
+TICOS BONUSES:
+A_TrapEscape: ID'd ALL hidden traps? B_ContradictionResolution: Resolved both sides?
+C_ProgressiveDiscovery: Revised with new info? D_MultiConstraint: Mapped ALL conflicts?
+E_SelfCorrecting: EXPLICIT backtrack? F_ExpertPanel: Max-depth per perspective?
+G_PivotDetection: Found reversing premise? H_DecisionUnderUncertainty: Scenario matrix?
+
+STRICT: 1.0=AGI-worthy 0.75=expert 0.5=competent 0.25=gaps 0.0=failure
+Output JSON: {"scores":{...},"comment":"<50 words>"}"""
+
+def _build_judge_schema():
+    sp={k:{"type":"number","enum":[0.0,0.25,0.5,0.75,1.0]} for k in RUBRIC}
+    return {"type":"object","properties":{"scores":{"type":"object","properties":sp,
+        "required":list(RUBRIC.keys()),"additionalProperties":False},
+        "comment":{"type":"string"}},"required":["scores","comment"],"additionalProperties":False}
+JUDGE_SCHEMA=_build_judge_schema()
+
+def call_judge_structured(prompt, system="", api_key="", model="gpt-5.2",
+                          temperature=0.1, max_tokens=2048):
+    messages=[]
+    if system: messages.append({"role":"system","content":system})
+    messages.append({"role":"user","content":prompt})
+    payload={"model":model,"max_completion_tokens":max_tokens,"temperature":temperature,
+             "messages":messages,"reasoning_effort":"none",
+             "response_format":{"type":"json_schema","json_schema":{"name":"FINALJudge","strict":True,"schema":JUDGE_SCHEMA}}}
+    headers={"Content-Type":"application/json","Authorization":f"Bearer {api_key}"}
+    for attempt in range(3):
+        try:
+            r=requests.post("https://api.openai.com/v1/chat/completions",headers=headers,json=payload,timeout=180)
+            if r.status_code==429: time.sleep(5*(attempt+1)); continue
+            r.raise_for_status()
+            content=_strip_think(r.json()["choices"][0]["message"]["content"] or "")
+            if not content:
+                if attempt<2: time.sleep(2); continue
+                return None
+            data=json.loads(content)
+            if "scores" in data and isinstance(data["scores"],dict):
+                for k in RUBRIC:
+                    if k not in data["scores"]: data["scores"][k]=0.5
+                return {"scores":data["scores"],"comment":data.get("comment","ok")}
+        except json.JSONDecodeError:
+            if attempt<2: time.sleep(2); continue
+            return None
+        except:
+            if attempt<2: time.sleep(3*(attempt+1)); continue
+            return None
+    return None
+
+def build_judge_prompt(task, response):
+    return f"""FINAL Bench Task Evaluation
+Task: {task.task_id} | {task.domain} | Grade {task.grade} | {task.difficulty}
+TICOS: {task.ticos_type} | Title: {task.title}
+
+PROMPT:
+{task.prompt[:2000]}
+
+EXPECTED:
+{task.expected_behavior[:600]}
+
+HIDDEN TRAPS: {task.hidden_trap or 'None'}
+
+RESPONSE TO JUDGE:
+{response[:17000]}
+
+Score: process_quality, metacognitive_accuracy, error_recovery, integration_depth, final_correctness
+Apply {task.ticos_type} bonus. Output ONLY JSON."""
+
+def parse_judge_fallback(text, keys):
+    if not text or text.startswith("[API_ERROR"):
+        return {"scores":{k:0.0 for k in keys},"comment":"API_ERROR","failed":True}
+    cleaned=_strip_think(text); VALID={0.0,0.25,0.5,0.75,1.0}
+    try:
+        m=re.search(r'\{[^{}]*"scores"\s*:\s*\{[^{}]*\}[^{}]*\}',cleaned,re.DOTALL)
+        if m:
+            d=json.loads(m.group())
+            if "scores" in d:
+                return {"scores":{k:min(VALID,key=lambda x:abs(x-float(d["scores"].get(k,0.5)))) for k in keys},"comment":d.get("comment","parsed")}
+    except: pass
+    try:
+        sc={}
+        for k in keys:
+            m2=re.search(rf'["\']?{re.escape(k)}["\']?\s*[:=]\s*([\d.]+)',cleaned,re.IGNORECASE)
+            if m2:
+                v=float(m2.group(1))
+                if 0<=v<=1: sc[k]=min(VALID,key=lambda x:abs(x-v))
+        if len(sc)>=3:
+            for k in keys:
+                if k not in sc: sc[k]=0.5
+            return {"scores":sc,"comment":"regex"}
+    except: pass
+    return {"scores":{k:0.0 for k in keys},"comment":"parse_failed","failed":True}
+
+# ════════════════════════════════════════════════════════════════
+# §5. Scoring Engine
+# ════════════════════════════════════════════════════════════════
+
+def compute_task_score(scores):
+    return round(sum(scores.get(k,0.5)*v["weight"] for k,v in RUBRIC.items())*100,2)
+
+def compute_axis_scores(results, tasks):
+    tm={t.task_id:t for t in tasks}; ax={}
+    for an,ai in AXIS_MAP.items():
+        vals=[]
+        for tid,d in results.items():
+            if d["score"]<0: continue
+            t=tm.get(tid); 
+            if not t: continue
+            try: jd=json.loads(d["judge"]) if isinstance(d["judge"],str) else d["judge"]; sc=jd.get("scores",{}) if isinstance(jd,dict) else {}
+            except: sc={}
+            rv=[float(sc.get(r,0.5)) for r in ai["rubrics"] if r in sc]
+            w=1.5 if(ai["ticos"] and t.ticos_type in ai["ticos"]) else 1.0
+            if rv: vals.append(np.mean(rv)*w)
+        ax[an]=round(min(np.mean(vals)*100,100),2) if vals else 0.0
+    return ax
+
+def compute_final_score(results, tasks):
+    tm={t.task_id:t for t in tasks}; ds={}
+    for tid,d in results.items():
+        if d["score"]<0: continue
+        t=tm.get(tid)
+        if t: ds.setdefault(t.domain,[]).append(d["score"])
+    da={d:np.mean(v) for d,v in ds.items() if v}
+    gd={}
+    for t in tasks: gd.setdefault(t.grade,set()).add(t.domain)
+    ws,wt=0,0
+    for g,doms in gd.items():
+        w=GRADE_WEIGHT.get(g,1.0)
+        for d in doms:
+            if d in da: ws+=da[d]*w; wt+=w
+    base=ws/wt if wt>0 else 0
+    axis=compute_axis_scores(results,tasks)
+    av=[max(v,0.01) for v in axis.values()]
+    har=(len(av)/sum(1.0/v for v in av)) if av else 50
+    har_p=har/100.0
+    return round(base*har_p,2),round(base,2),round(har_p,3),axis,da
+
+def determine_agi_stage(score, axis):
+    all60=all(v>=60 for v in axis.values()) if axis else False
+    for s in reversed(AGI_STAGES):
+        if score>=s["min"]:
+            if s["stage"]>=4 and not all60: return AGI_STAGES[2]
+            return s
+    return AGI_STAGES[0]
+
+# ════════════════════════════════════════════════════════════════
+# §6. Checkpoint DB
+# ════════════════════════════════════════════════════════════════
+DB_PATH="final_bench_eval.db"
+def _init_db():
+    c=sqlite3.connect(DB_PATH)
+    c.execute("CREATE TABLE IF NOT EXISTS eval_results(run_id TEXT,task_id TEXT,model_response TEXT,judge_response TEXT,weighted_score REAL,timestamp REAL,PRIMARY KEY(run_id,task_id))")
+    c.commit(); c.close()
+def _make_run_id(m): return hashlib.md5(f"FINALv40_BL_{m}".encode()).hexdigest()[:12]
+def _save_result(rid,tid,resp,jresp,sc):
+    c=sqlite3.connect(DB_PATH); c.execute("INSERT OR REPLACE INTO eval_results VALUES(?,?,?,?,?,?)",(rid,tid,resp,jresp,sc,time.time())); c.commit(); c.close()
+def _load_all(rid):
+    c=sqlite3.connect(DB_PATH); cur=c.execute("SELECT task_id,model_response,judge_response,weighted_score FROM eval_results WHERE run_id=?",(rid,)); rows=cur.fetchall(); c.close()
+    return {r[0]:{"response":r[1],"judge":r[2],"score":r[3]} for r in rows}
+def _clear_run(rid):
+    c=sqlite3.connect(DB_PATH); c.execute("DELETE FROM eval_results WHERE run_id=?",(rid,)); c.commit(); c.close()
+_init_db()
+
+# ════════════════════════════════════════════════════════════════
+# §7. CSV Export
+# ════════════════════════════════════════════════════════════════
+def generate_csv(results, tasks, model_name, mode="BASELINE"):
+    out=io.StringIO(); w=csv.writer(out)
+    w.writerow(["task_id","domain","grade","ticos_type","difficulty","title","model","mode","weighted_score",
+                "process_quality","metacognitive_accuracy","error_recovery","integration_depth","final_correctness",
+                "judge_comment","response_preview","timestamp"])
+    tm={t.task_id:t for t in tasks}
+    for tid,d in sorted(results.items()):
+        t=tm.get(tid)
+        if not t: continue
+        jd={}
+        try: jd=json.loads(d["judge"]) if isinstance(d["judge"],str) else(d["judge"] or {})
+        except: pass
+        sc=jd.get("scores",{}) if isinstance(jd,dict) else {}
+        cm=(jd.get("comment","") if isinstance(jd,dict) else "")[:200]
+        s=d["score"]
+        if s<0: s=-1; cm=f"JUDGE_FAILED:{cm}"
+        w.writerow([tid,t.domain,t.grade,t.ticos_type,t.difficulty,t.title,model_name,mode,s,
+                     sc.get("process_quality",""),sc.get("metacognitive_accuracy",""),
+                     sc.get("error_recovery",""),sc.get("integration_depth",""),sc.get("final_correctness",""),
+                     cm,(d.get("response","") or "")[:300].replace("\n"," "),datetime.now().isoformat()])
+    return out.getvalue()
+
+# ════════════════════════════════════════════════════════════════
+# §8. HTML Builders
+# ════════════════════════════════════════════════════════════════
+CSS = """<style>
+.eval-table{width:100%;border-collapse:collapse;font-size:0.82em}
+.eval-table th{background:#f0f4f8;padding:8px;text-align:left;border-bottom:2px solid #ccc;font-size:0.9em}
+.eval-table td{padding:5px 8px;border-bottom:1px solid #eee}
+.score-bar{background:#e0e0e0;border-radius:8px;height:16px;overflow:hidden;min-width:70px}
+.score-fill{height:100%;border-radius:8px;transition:width .4s}
+.summary-card{background:linear-gradient(135deg,#0a0a1a,#1a1a3e);border-radius:16px;padding:24px;color:#fff;margin:8px 0}
+.axis-row{display:flex;align-items:center;gap:10px;margin:5px 0}
+.axis-bar{flex:1;background:#333;border-radius:6px;height:14px;overflow:hidden}
+.axis-fill{height:100%;border-radius:6px}
+.stage-badge{display:inline-block;padding:6px 16px;border-radius:20px;font-weight:700;font-size:1.1em;margin:8px 0}
+.progress-bar{background:#e0e0e0;border-radius:8px;height:22px;margin:12px 0;overflow:hidden}
+.progress-fill{height:100%;border-radius:8px;transition:width .4s;background:linear-gradient(90deg,#1565c0,#00c853)}
+</style>"""
+
+def _sc(s):
+    if s>=80: return "#4caf50"
+    if s>=60: return "#ff9800"
+    if s>=40: return "#ff5722"
+    return "#f44336"
+
+def _build_progress_table(results, tasks):
+    rows=""
+    for t in tasks:
+        info=DOMAIN_INFO.get(t.domain,{"icon":"?","color":"#999"})
+        gb=f'<span style="background:{"#c62828" if t.grade=="A" else "#1565c0" if t.grade=="B" else "#6a1b9a"};color:#fff;padding:1px 6px;border-radius:4px;font-size:0.8em">{t.grade}</span>'
+        if t.task_id in results:
+            s=results[t.task_id]["score"]
+            if s<0:
+                rows+=f'<tr style="background:#fff3e0"><td>{t.task_id}</td><td>{info["icon"]} {t.domain[:15]}</td><td>{gb}</td><td>{t.ticos_type.split("_")[0]}</td><td>{t.difficulty}</td><td style="color:#ff9800">❌</td><td>—</td></tr>'
+            else:
+                c=_sc(s)
+                rows+=f'<tr><td>{t.task_id}</td><td>{info["icon"]} {t.domain[:15]}</td><td>{gb}</td><td>{t.ticos_type.split("_")[0]}</td><td>{t.difficulty}</td><td><div class="score-bar"><div class="score-fill" style="width:{min(s,100)}%;background:{c}"></div></div></td><td style="font-weight:700;color:{c}">{s:.1f}</td></tr>'
+        else:
+            rows+=f'<tr style="opacity:0.35"><td>{t.task_id}</td><td>{info["icon"]}</td><td>{gb}</td><td>{t.ticos_type.split("_")[0]}</td><td>{t.difficulty}</td><td>⏳</td><td>—</td></tr>'
+    return f'{CSS}<table class="eval-table"><thead><tr><th>ID</th><th>Domain</th><th>G</th><th>TICOS</th><th>Diff</th><th>Score</th><th>Val</th></tr></thead><tbody>{rows}</tbody></table>'
+
+def _build_summary_card(results, tasks, model_name, hf_status):
+    final,base,har_p,axis,dom_avgs=compute_final_score(results,tasks)
+    stage=determine_agi_stage(final,axis)
+    labels={"generalization":"🌐 Generalization","reasoning":"🧠 Reasoning","planning":"📋 Planning","reliability":"🎯 Reliability","safety":"🛡️ Safety"}
+    ax_html=""
+    for an,av in axis.items():
+        c=_sc(av)
+        ax_html+=f'<div class="axis-row"><span style="width:120px;font-size:0.85em">{labels.get(an,an)}</span><div class="axis-bar"><div class="axis-fill" style="width:{min(av,100)}%;background:{c}"></div></div><span style="width:50px;text-align:right;font-weight:700;color:{c}">{av:.1f}</span></div>'
+    gh=""
+    for g in ["A","B","C"]:
+        gd=[t.domain for t in tasks if t.grade==g]
+        gs=[dom_avgs[d] for d in set(gd) if d in dom_avgs]
+        if gs: a=np.mean(gs); gh+=f'<span style="margin-right:14px">{g}×{GRADE_WEIGHT[g]}: <b style="color:{_sc(a)}">{a:.1f}</b></span>'
+    done=sum(1 for t in tasks if t.task_id in results)
+    jf=sum(1 for t in tasks if t.task_id in results and results[t.task_id]["score"]<0)
+    # MA-ER Gap
+    ma_vals,er_vals=[],[]
+    for tid,d in results.items():
+        if d["score"]<0: continue
+        try:
+            jd=json.loads(d["judge"]) if isinstance(d["judge"],str) else d["judge"]
+            sc=jd.get("scores",{}) if isinstance(jd,dict) else {}
+            if "metacognitive_accuracy" in sc: ma_vals.append(float(sc["metacognitive_accuracy"]))
+            if "error_recovery" in sc: er_vals.append(float(sc["error_recovery"]))
+        except: pass
+    avg_ma=np.mean(ma_vals) if ma_vals else 0; avg_er=np.mean(er_vals) if er_vals else 0
+    gap=avg_ma-avg_er
+    gc="#f44336" if gap>0.2 else "#ff9800" if gap>0.1 else "#4caf50"
+    gl="Declaration-Action Gap" if gap>0.2 else "Moderate Gap" if gap>0.1 else "Balanced"
+    # Pass checks
+    ad=[t.domain for t in tasks if t.grade=="A"]
+    asc=[dom_avgs[d] for d in set(ad) if d in dom_avgs]
+    aa=np.mean(asc) if asc else 0
+    checks=[("Score≥80",final>=80),("Axes≥60",all(v>=60 for v in axis.values())),(f"A-avg≥75({aa:.0f})",aa>=75)]
+    ch="".join([f'<span style="margin-right:8px">{"✅" if ok else "❌"}{lb}</span>' for lb,ok in checks])
+    return f"""{CSS}<div class="summary-card">
+<div style="text-align:center">
+<div class="stage-badge" style="background:{stage['color']}">{stage['name']}</div>
+<h2 style="margin:6px 0;font-size:1.6em">🤖 Baseline FINAL: {final:.1f}</h2>
+<p style="color:#aaa;font-size:0.85em">{stage['label']} · Base {base:.1f} × HAR {har_p:.3f} · {model_name} · {done}/{len(tasks)}{f" · JF={jf}" if jf else ""}</p>
+</div><hr style="border-color:#333;margin:12px 0">
+<h4 style="color:#aaa;margin:6px 0">🎯 5-Axis Scores</h4>{ax_html}
+<hr style="border-color:#333;margin:10px 0">
+<div style="font-size:0.88em">{gh}</div>
+<div style="display:flex;align-items:center;gap:12px;margin:8px 0;padding:8px;background:rgba(255,255,255,0.05);border-radius:8px">
+<span style="font-size:0.85em">MA-ER Gap:</span>
+<span style="font-weight:700;color:{gc}">{gap:.3f}</span>
+<span style="font-size:0.8em;color:{gc}">({gl})</span>
+<span style="font-size:0.78em;color:#888">MA={avg_ma:.3f} ER={avg_er:.3f}</span></div>
+<div style="font-size:0.82em;margin-top:6px">{ch}</div>
+<p style="font-size:0.78em;color:#666;margin-top:8px">{hf_status}</p>
+<div style="background:rgba(233,69,96,0.15);border:1px solid #e94560;border-radius:8px;padding:10px;margin-top:12px">
+<p style="font-size:0.82em;color:#e94560;margin:0">🔒 <b>MetaCog (Self-Correction) evaluation: COMING SOON</b></p>
+<p style="font-size:0.75em;color:#aaa;margin:4px 0 0 0">The 3-Phase Protocol can boost performance up to 70%+ on hardest tasks.</p>
+</div></div>"""
+
+def _build_detail_view(results, tasks):
+    items=""
+    for t in tasks:
+        if t.task_id not in results: continue
+        d=results[t.task_id]; info=DOMAIN_INFO.get(t.domain,{"icon":"?"})
+        s=d["score"]; resp=html.escape((d.get("response","") or "")[:500])
+        jc=""; ss=""
+        try:
+            jd=json.loads(d["judge"]) if isinstance(d["judge"],str) else(d["judge"] or {})
+            jc=html.escape((jd.get("comment","") if isinstance(jd,dict) else "")[:200])
+            sc=jd.get("scores",{}) if isinstance(jd,dict) else {}
+            ss=" · ".join([f"{k.split('_')[0]}={v}" for k,v in sc.items()])
+        except: pass
+        c=_sc(s) if s>=0 else "#ff9800"; badge=f'{s:.1f}' if s>=0 else "JF"
+        items+=f'<details style="margin:3px 0;border:1px solid #ddd;border-radius:8px;padding:8px"><summary style="cursor:pointer;font-weight:600">{info["icon"]} {t.task_id} [{t.grade}] — <span style="color:{c}">{badge}</span></summary><div style="font-size:0.8em;margin-top:6px"><b>{t.title}</b><br>TICOS: {t.ticos_type} | Scores: {ss}<br>Judge: {jc}<br>Response: {resp}...</div></details>'
+    return CSS+items
+
+# ════════════════════════════════════════════════════════════════
+# §9. Evaluation Engine (Baseline Only)
+# ════════════════════════════════════════════════════════════════
+
+def _eval_single(task, run_id, eval_api_key, eval_model_id, eval_provider,
+                 judge_api_key, judge_model, state):
+    try:
+        sys_p=(f"You are being evaluated on FINAL Bench.\nTask: {task.ticos_type}\n"
+               f"State confidence (0-100%) for EVERY claim. If wrong, EXPLICITLY backtrack. "
+               f"If unsure, say so honestly.")
+        model_response=call_model(task.prompt,system=sys_p,api_key=eval_api_key,
+                                   model_id=eval_model_id,provider=eval_provider,max_tokens=12288)
+        if model_response.startswith("[API_ERROR") or model_response=="[EMPTY]":
+            _save_result(run_id,task.task_id,model_response,"{}",0)
+            with state["lock"]: state["done"]+=1; state["errors"].append(f"{task.task_id}: {model_response[:50]}")
+            return task.task_id,{"response":model_response,"judge":"{}","score":0}
+        jp=build_judge_prompt(task,model_response)
+        jd=call_judge_structured(jp,system=JUDGE_SYSTEM,api_key=judge_api_key,model=judge_model)
+        if jd is None:
+            jr=call_openai(jp,system=JUDGE_SYSTEM,api_key=judge_api_key,model=judge_model,max_tokens=2048,temperature=0.05)
+            jd=parse_judge_fallback(jr,list(RUBRIC.keys()))
+        if jd is None:
+            jd={"scores":{k:0.0 for k in RUBRIC},"comment":"FAILURE","failed":True}
+        if jd.get("failed"):
+            ws=-1.0; jd["comment"]=f"JF:{jd['comment']}"
+        else:
+            ws=compute_task_score(jd["scores"])
+            with state["lock"]: state["parse_ok"]+=1
+        jj=json.dumps(jd,ensure_ascii=False)
+        _save_result(run_id,task.task_id,model_response,jj,ws)
+        with state["lock"]:
+            state["done"]+=1
+            info=DOMAIN_INFO.get(task.domain,{"icon":"?"})
+            state["active"].append(f'{info["icon"]} {task.task_id}')
+            if len(state["active"])>10: state["active"]=state["active"][-10:]
+        return task.task_id,{"response":model_response,"judge":jj,"score":ws}
+    except Exception as e:
+        with state["lock"]: state["done"]+=1; state["errors"].append(f"{task.task_id}: {str(e)[:60]}")
+        _save_result(run_id,task.task_id,f"[ERROR] {e}","{}",0)
+        return task.task_id,{"response":f"[ERROR] {e}","judge":"{}","score":0}
+
+# ════════════════════════════════════════════════════════════════
+# §10. State Machine + Background Thread
+# ════════════════════════════════════════════════════════════════
+
+_EVAL_STATE={
+    "running":False,"stop_requested":False,"finished":False,
+    "run_id":"","model":"","done":0,"total":0,"cached":0,
+    "errors":[],"active":[],"parse_ok":0,"parse_fail":0,
+    "start_time":0,"results":{},"tasks":[],
+    "grade_done":{},"grade_total":{},
+    "lock":threading.Lock(),"message":"","csv_path":None,"hf_status":"","n_workers":5,
+}
+
+def _reset():
+    with _EVAL_STATE["lock"]:
+        _EVAL_STATE.update({"running":False,"stop_requested":False,"finished":False,
+            "done":0,"cached":0,"errors":[],"active":[],"parse_ok":0,"parse_fail":0,
+            "start_time":0,"results":{},"tasks":[],"grade_done":{},"grade_total":{},
+            "message":"","csv_path":None,"hf_status":""})
+
+def _prog_html(state, pending):
+    done=state["done"]; pct=min(int(done/max(pending,1)*100),100)
+    gb=""
+    for g in ["A","B","C"]:
+        gt=state["grade_total"].get(g,0); gd=state["grade_done"].get(g,0)
+        if gt==0: continue
+        gp=min(int(gd/gt*100),100)
+        c="#4caf50" if gp==100 else("#1976d2" if gp>0 else "#e0e0e0")
+        emoji="🅰️" if g=="A" else "🅱️" if g=="B" else "🅾️"
+        gb+=f'<div style="display:flex;align-items:center;gap:8px;margin:3px 0"><span style="width:100px;font-size:0.85em">{emoji} {g}×{GRADE_WEIGHT[g]}</span><div style="flex:1;background:#e0e0e0;border-radius:6px;height:14px;overflow:hidden"><div style="width:{gp}%;height:100%;background:{c};border-radius:6px"></div></div><span style="width:55px;font-size:0.82em;text-align:right;color:{c}">{gd}/{gt}</span></div>'
+    o=f'<div style="margin:8px 0"><div style="display:flex;justify-content:space-between;font-size:0.95em;margin-bottom:6px"><span>⚡ <b>🤖 Baseline (Non-AGI)</b> — {done}/{pending}</span><span style="font-weight:700">{pct}%</span></div><div class="progress-bar"><div class="progress-fill" style="width:{pct}%"></div></div>{gb}'
+    ac=state.get("active",[])
+    if ac: o+='<div style="margin-top:8px">🔄 '+" ".join([f'<span style="background:#e3f2fd;padding:2px 6px;border-radius:4px;font-size:0.78em">{a}</span>' for a in ac[-8:]])+'</div>'
+    er=state.get("errors",[])
+    if er: o+=f'<div style="color:#c62828;margin-top:6px;font-size:0.8em">{" · ".join(["⚠️"+html.escape(e[:30]) for e in er[-3:]])}</div>'
+    return o+'</div>'
+
+def _bg_eval(eval_api_key, eval_model_id, eval_provider, eval_label,
+             judge_api_key, judge_model, tasks, run_id, n_workers):
+    global _EVAL_STATE
+    try:
+        with _EVAL_STATE["lock"]:
+            _EVAL_STATE["start_time"]=time.time()
+            _EVAL_STATE["message"]=f"⚡ Baseline — {eval_label} — {len(tasks)} tasks"
+        results=dict(_load_all(run_id))
+        cached=sum(1 for t in tasks if t.task_id in results)
+        pending=[t for t in tasks if t.task_id not in results]
+        gt={}
+        for t in pending: gt.setdefault(t.grade,[]).append(t)
+        with _EVAL_STATE["lock"]:
+            _EVAL_STATE["results"]=results; _EVAL_STATE["cached"]=cached
+            _EVAL_STATE["total"]=len(pending)
+            _EVAL_STATE["grade_total"]={g:len(ts) for g,ts in gt.items()}
+            _EVAL_STATE["grade_done"]={g:0 for g in gt}
+            _EVAL_STATE["done"]=0; _EVAL_STATE["errors"]=[]; _EVAL_STATE["active"]=[]
+        if pending:
+            with ThreadPoolExecutor(max_workers=n_workers) as ex:
+                futs={}
+                for t in pending:
+                    if _EVAL_STATE["stop_requested"]: break
+                    futs[ex.submit(_eval_single,t,run_id,eval_api_key,eval_model_id,eval_provider,judge_api_key,judge_model,_EVAL_STATE)]=t
+                done_set=set()
+                while len(done_set)<len(futs):
+                    if _EVAL_STATE["stop_requested"]: ex.shutdown(wait=False,cancel_futures=True); break
+                    for f in list(futs):
+                        if f in done_set: continue
+                        if f.done():
+                            done_set.add(f)
+                            try:
+                                tid,data=f.result()
+                                with _EVAL_STATE["lock"]:
+                                    _EVAL_STATE["results"][tid]=data
+                                    to=futs[f]; _EVAL_STATE["grade_done"][to.grade]=_EVAL_STATE["grade_done"].get(to.grade,0)+1
+                            except: pass
+                    time.sleep(0.5)
+        with _EVAL_STATE["lock"]: results=dict(_EVAL_STATE["results"])
+        final,base,har,axis,_=compute_final_score(results,tasks)
+        stage=determine_agi_stage(final,axis)
+        csv_str=generate_csv(results,tasks,eval_label,"BASELINE")
+        cp=f"/tmp/final_{run_id}.csv"
+        with open(cp,"w",encoding="utf-8") as f: f.write(csv_str)
+        elapsed=int(time.time()-_EVAL_STATE["start_time"])
+        with _EVAL_STATE["lock"]:
+            _EVAL_STATE["csv_path"]=cp; _EVAL_STATE["hf_status"]=""
+            _EVAL_STATE["message"]=f"🏁 {stage['name']} — FINAL={final:.1f} · {elapsed}s"
+            _EVAL_STATE["running"]=False; _EVAL_STATE["finished"]=True
+    except Exception as e:
+        with _EVAL_STATE["lock"]:
+            _EVAL_STATE["message"]=f"❌ Fatal: {str(e)[:100]}"
+            _EVAL_STATE["running"]=False; _EVAL_STATE["finished"]=True
+
+def _start_eval(eval_api_key, judge_api_key, eval_model_choice, judge_model,
+                grade_f, diff_f, max_t, n_w, fresh):
+    global _EVAL_STATE
+    if _EVAL_STATE["running"]: return "⚠️ Already running"
+    eval_api_key=(eval_api_key or "").strip()
+    judge_api_key=(judge_api_key or "").strip()
+    model_info=ALL_EVAL_MODELS.get(eval_model_choice)
+    if not model_info: return "❌ Select an evaluation model"
+    eval_model_id=model_info["id"]; eval_provider=model_info["provider"]
+    if not eval_api_key: return f"❌ {eval_provider} API Key required for evaluation model"
+    if not judge_api_key: return "❌ OpenAI API Key required for Judge"
+    tasks=ALL_TASKS[:]
+    if grade_f!="All": tasks=[t for t in tasks if t.grade==grade_f]
+    if diff_f!="All": tasks=[t for t in tasks if t.difficulty==diff_f]
+    tasks=tasks[:int(max_t)]
+    rid=_make_run_id(eval_model_id)
+    if fresh: _clear_run(rid)
+    _reset()
+    with _EVAL_STATE["lock"]:
+        _EVAL_STATE.update({"running":True,"run_id":rid,"model":eval_model_choice,
+                            "tasks":tasks,"total":len(tasks),"n_workers":int(n_w)})
+    threading.Thread(target=_bg_eval,daemon=True,
+        args=(eval_api_key,eval_model_id,eval_provider,eval_model_choice,
+              judge_api_key,judge_model,tasks,rid,int(n_w))).start()
+    return f"⚡ Baseline Started — {eval_model_choice} ({len(tasks)} tasks)"
+
+def _stop():
+    if _EVAL_STATE["running"]: _EVAL_STATE["stop_requested"]=True; return "⏹️ Stopping..."
+    return "ℹ️ Not running"
+
+def _poll():
+    with _EVAL_STATE["lock"]:
+        running=_EVAL_STATE["running"]; finished=_EVAL_STATE["finished"]
+        tasks=_EVAL_STATE.get("tasks",[]); results=dict(_EVAL_STATE.get("results",{}))
+        msg=_EVAL_STATE.get("message",""); cp=_EVAL_STATE.get("csv_path")
+    if not running and not finished and not results:
+        return("ℹ️ Configure API keys, select models, then press ▶️ Start","","","",None)
+    if running:
+        pend=_EVAL_STATE.get("total",0)-_EVAL_STATE.get("cached",0)
+        ph=CSS+_prog_html(_EVAL_STATE,pend)
+    elif finished:
+        ph=f'<div style="background:#e8f5e9;padding:12px;border-radius:8px;font-weight:600">{msg}</div>'
+    else: ph=msg
+    th=_build_progress_table(results,tasks) if tasks else ""
+    sh,dh,co="","",None
+    if finished and tasks:
+        model=_EVAL_STATE.get("model","?")
+        hf_st=_EVAL_STATE.get("hf_status","")
+        sh=_build_summary_card(results,tasks,model,hf_st)
+        dh=_build_detail_view(results,tasks)
+        co=cp
+    return(ph,th,sh,dh,co)
+
+# ════════════════════════════════════════════════════════════════
+# §11. Gradio App
+# ════════════════════════════════════════════════════════════════
+
+HEADER = """
+<div style="text-align:center;padding:16px 0">
+<h1 style="margin:0;font-size:1.8em">🏆 FINAL Bench v4.0 — Baseline Evaluation</h1>
+<h2 style="margin:4px 0;color:#555;font-size:1.05em">Frontier Intelligence Nexus for AGI-Level Verification</h2>
+<p style="color:#888;font-size:0.88em;max-width:720px;margin:8px auto">
+<b>100 Tasks · 15 Domains · 8 TICOS · 5-Axis · 5-Stage AGI Grade</b><br>
+🤖 Baseline (Non-AGI) — Single LLM Evaluation · Multi-Provider<br>
+⚖️ Judge: GPT-5.2 Structured Output · 📊 A×1.5 B×1.0 C×0.7 · HAR Penalty
+</p>
+<div style="display:flex;justify-content:center;gap:6px;margin-top:8px;flex-wrap:wrap;font-size:0.82em">
+<span style="background:#e3f2fd;padding:2px 10px;border-radius:12px">OpenAI</span>
+<span style="background:#fce4ec;padding:2px 10px;border-radius:12px">Anthropic</span>
+<span style="background:#e8f5e9;padding:2px 10px;border-radius:12px">Google</span>
+</div>
+<div style="background:rgba(233,69,96,0.1);border:1px solid #e94560;border-radius:10px;padding:10px;margin:12px auto;max-width:600px">
+<p style="color:#e94560;font-size:0.85em;margin:0">🔒 <b>MetaCog (Self-Correction Protocol) evaluation: COMING SOON</b></p>
+<p style="color:#888;font-size:0.78em;margin:4px 0 0 0">
+3-Phase Protocol (Initial → Self-Review → Correction) — paper's core contribution.<br>
+Performance boost up to 70%+ on hardest tasks. Future update.
+</p></div>
+<div style="display:flex;justify-content:center;gap:8px;margin-top:8px;font-size:0.78em">
+<a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank" style="background:#333;color:#fff;padding:3px 10px;border-radius:10px;text-decoration:none">📊 Dataset</a>
+<a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard" target="_blank" style="background:#333;color:#fff;padding:3px 10px;border-radius:10px;text-decoration:none">🏆 Leaderboard</a>
+</div></div>"""
+
+def create_app():
+    with gr.Blocks(title="FINAL Bench v4.0",theme=gr.themes.Soft(),
+                   css=".gradio-container{max-width:1100px !important} header{display:none!important}") as app:
+        gr.HTML(HEADER)
+        gr.Markdown("### 🔑 API Keys")
+        with gr.Row():
+            eval_api_key=gr.Textbox(label="Evaluation Model API Key",type="password",
+                placeholder="OpenAI (sk-...) / Anthropic (sk-ant-...) / Google (AIza...)",
+                info="Enter the API key matching your selected evaluation model",scale=3)
+            judge_api_key=gr.Textbox(label="Judge API Key (OpenAI required)",type="password",
+                placeholder="sk-... (OpenAI key for GPT-5.2 judge)",
+                info="Judge always uses OpenAI GPT-5.2 Structured Output",scale=3)
+        gr.Markdown("### 🤖 Model Selection")
+        with gr.Row():
+            eval_m=gr.Dropdown(label="Evaluation Target",choices=list(ALL_EVAL_MODELS.keys()),
+                value="GPT-5.2 (flagship) [OpenAI]",
+                info="Select model to evaluate. Use matching API key above.",scale=4)
+            judge_m=gr.Dropdown(label="⚖️ Judge Model",choices=list(JUDGE_MODELS.keys()),
+                value="gpt-5.2",info="Uses OpenAI Structured Output",scale=2)
+        gr.Markdown("### ⚙️ Settings")
+        with gr.Row():
+            gf=gr.Dropdown(["All","A","B","C"],value="All",label="Grade Filter",scale=1)
+            df=gr.Dropdown(["All","expert","frontier"],value="All",label="Difficulty Filter",scale=1)
+            mt=gr.Slider(1,100,value=100,step=1,label="Max Tasks",scale=1)
+            nw=gr.Slider(1,10,value=5,step=1,label="Workers",scale=1)
+        with gr.Row():
+            s_btn=gr.Button("▶️ Start (Resume)",variant="primary",size="lg",scale=2)
+            f_btn=gr.Button("🚀 Fresh Start",variant="secondary",size="lg",scale=2)
+            x_btn=gr.Button("⏹️ Stop",variant="stop",size="lg",scale=1)
+        status=gr.Textbox(label="Status",interactive=False,max_lines=1)
+        with gr.Tabs():
+            with gr.Tab("📊 Progress"): p_html=gr.HTML()
+            with gr.Tab("📋 Results"): t_html=gr.HTML()
+            with gr.Tab("🏆 FINAL Score"): s_html=gr.HTML()
+            with gr.Tab("🔍 Details"): d_html=gr.HTML()
+            with gr.Tab("💾 CSV"): c_file=gr.File(label="CSV")
+        timer=gr.Timer(value=2,active=True)
+        timer.tick(fn=_poll,outputs=[p_html,t_html,s_html,d_html,c_file])
+        eval_ins=[eval_api_key,judge_api_key,eval_m,judge_m,gf,df,mt,nw]
+        s_btn.click(fn=lambda *a:_start_eval(*a,fresh=False),inputs=eval_ins,outputs=[status])
+        f_btn.click(fn=lambda *a:_start_eval(*a,fresh=True),inputs=eval_ins,outputs=[status])
+        x_btn.click(fn=_stop,outputs=[status])
+        gr.Markdown("""---
+<center><b>FINAL Bench v4.0</b> — Baseline (Non-AGI) · Multi-Provider · 100 Tasks · 5-Axis · 5-Stage<br>
+🔒 MetaCog (Self-Correction Protocol): <b>COMING SOON</b><br>
+Apache 2.0 · <b>Ginigen AI</b> — Choi Sunyoung</center>""")
+    return app
+
+if __name__=="__main__":
+    sg,sd={},{}
+    for t in ALL_TASKS: sg[t.grade]=sg.get(t.grade,0)+1; sd[t.domain]=sd.get(t.domain,0)+1
+    print(f"\n{'='*60}\n  FINAL Bench v4.0 — Baseline (Non-AGI)\n  Multi-Provider: OpenAI / Anthropic / Google\n{'='*60}")
+    print(f"  {len(ALL_TASKS)} tasks | {len(sd)} domains")
+    for g in ["A","B","C"]: print(f"  Grade {g} (×{GRADE_WEIGHT[g]}): {sg.get(g,0)}")
+    print(f"  Judge: GPT-5.2 Structured Output")
+    print(f"  🔒 MetaCog: COMING SOON\n{'='*60}\n")
+    app=create_app()
+    app.queue(default_concurrency_limit=2)
+    app.launch(server_name="0.0.0.0",server_port=7860)