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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
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| <title>FINAL Bench — Functional Metacognition Leaderboard</title> | |
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| <style> | |
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| *{margin:0;padding:0;box-sizing:border-box} | |
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| .leaderboard-table thead th.sort-active{color:var(--accent-cyan)} | |
| .leaderboard-table thead th.sort-active::after{content:' ▼';font-size:.6rem} | |
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| .leaderboard-table td:last-child{border-radius:0 10px 10px 0} | |
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| .model-name{font-weight:600}.model-provider{font-size:.75rem;color:var(--text-muted)} | |
| .score-cell{font-family:'JetBrains Mono',monospace;font-weight:600;font-size:.92rem} | |
| .score-bar-container{display:flex;align-items:center;gap:10px} | |
| .score-bar{flex:1;height:6px;background:rgba(255,255,255,0.05);border-radius:3px;overflow:hidden;min-width:60px} | |
| .score-bar-fill{height:100%;border-radius:3px;transition:width .8s ease} | |
| .gap-positive{color:var(--accent-red)} | |
| .delta-positive{color:var(--accent-green);font-family:'JetBrains Mono',monospace;font-weight:700} | |
| .mean-row{background:rgba(59,130,246,0.08)!important;border:1px solid rgba(59,130,246,0.2)} | |
| .mean-row td{font-weight:700;color:var(--accent-cyan)} | |
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| .chart-title{font-size:1rem;font-weight:700;margin-bottom:20px} | |
| .chart-wrapper{position:relative;height:360px} | |
| .gap-viz{display:grid;grid-template-columns:repeat(auto-fit,minmax(110px,1fr));gap:12px;margin-top:24px} | |
| .gap-model{background:var(--bg-card);border:1px solid var(--border);border-radius:12px;padding:16px 12px;text-align:center;transition:border-color .3s} | |
| .gap-model:hover{border-color:rgba(239,68,68,0.4)} | |
| .gap-model-name{font-size:.72rem;font-weight:600;color:var(--text-secondary);margin-bottom:10px;white-space:nowrap;overflow:hidden;text-overflow:ellipsis} | |
| .gap-bar-row{display:flex;align-items:center;gap:6px;margin-bottom:6px} | |
| .gap-bar-label{font-family:'JetBrains Mono',monospace;font-size:.6rem;color:var(--text-muted);width:20px} | |
| .gap-bar-track{flex:1;height:8px;background:rgba(255,255,255,0.05);border-radius:4px;overflow:hidden} | |
| .gap-bar-fill-ma{height:100%;background:linear-gradient(90deg,var(--accent-amber),#fbbf24);border-radius:4px;transition:width 1s ease} | |
| .gap-bar-fill-er{height:100%;background:linear-gradient(90deg,var(--accent-red),#f87171);border-radius:4px;transition:width 1s ease} | |
| .gap-value{font-family:'JetBrains Mono',monospace;font-size:1.1rem;font-weight:700;color:var(--accent-red);margin-top:8px} | |
| .gap-label-text{font-size:.65rem;color:var(--text-muted);text-transform:uppercase;letter-spacing:.5px} | |
| .method-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(250px,1fr));gap:16px;margin-top:24px} | |
| .method-card{background:var(--bg-card);border:1px solid var(--border);border-radius:12px;padding:24px} | |
| .method-card-title{font-family:'JetBrains Mono',monospace;font-size:.72rem;font-weight:700;color:var(--accent-cyan);text-transform:uppercase;letter-spacing:1.5px;margin-bottom:10px} | |
| .method-card-body{font-size:.88rem;color:var(--text-secondary);line-height:1.7} | |
| .section-title{font-family:'Playfair Display',serif;font-size:1.6rem;font-weight:700;margin-bottom:8px} | |
| .section-subtitle{font-size:.9rem;color:var(--text-secondary)} | |
| .about-hero{text-align:center;padding:60px 0 40px;background:linear-gradient(180deg,rgba(59,130,246,0.05) 0%,transparent 100%);border-radius:0 0 24px 24px;margin-bottom:40px} | |
| .about-hero h2{font-family:'Playfair Display',serif;font-size:clamp(1.6rem,4vw,2.4rem);font-weight:900;margin-bottom:16px;background:linear-gradient(135deg,#f1f5f9,#94a3b8);-webkit-background-clip:text;-webkit-text-fill-color:transparent} | |
| .about-hero p{font-size:1.05rem;color:var(--text-secondary);max-width:680px;margin:0 auto;line-height:1.8} | |
| .problem-grid{display:grid;grid-template-columns:1fr 1fr;gap:24px;margin-top:24px} | |
| .problem-card{border-radius:16px;padding:32px;position:relative;overflow:hidden} | |
| .problem-card.old{background:linear-gradient(135deg,rgba(239,68,68,0.08),rgba(239,68,68,0.02));border:1px solid rgba(239,68,68,0.2)} | |
| .problem-card.new{background:linear-gradient(135deg,rgba(6,182,212,0.08),rgba(6,182,212,0.02));border:1px solid rgba(6,182,212,0.2)} | |
| .problem-card-badge{font-family:'JetBrains Mono',monospace;font-size:.65rem;font-weight:700;letter-spacing:2px;text-transform:uppercase;margin-bottom:16px;display:inline-block;padding:4px 12px;border-radius:6px} | |
| .problem-card.old .problem-card-badge{background:rgba(239,68,68,0.15);color:var(--accent-red)} | |
| .problem-card.new .problem-card-badge{background:rgba(6,182,212,0.15);color:var(--accent-cyan)} | |
| .problem-card h3{font-size:1.15rem;font-weight:700;margin-bottom:16px} | |
| .problem-card ul{list-style:none;padding:0} | |
| .problem-card li{padding:8px 0;font-size:.88rem;color:var(--text-secondary);border-bottom:1px solid rgba(255,255,255,0.04);display:flex;align-items:flex-start;gap:10px} | |
| .problem-card li::before{font-size:1rem;flex-shrink:0;margin-top:1px} | |
| .problem-card.old li::before{content:'✕';color:var(--accent-red)} | |
| .problem-card.new li::before{content:'✓';color:var(--accent-cyan)} | |
| .pipeline-flow{display:flex;align-items:stretch;gap:0;margin-top:32px;overflow-x:auto;padding-bottom:8px} | |
| .pipeline-step{flex:1;min-width:200px;padding:28px 20px;text-align:center;position:relative;background:var(--bg-card);border:1px solid var(--border)} | |
| .pipeline-step:first-child{border-radius:16px 0 0 16px} | |
| .pipeline-step:last-child{border-radius:0 16px 16px 0} | |
| .pipeline-step:not(:last-child)::after{content:'';position:absolute;right:-12px;top:50%;transform:translateY(-50%);width:0;height:0;border-left:12px solid var(--bg-card);border-top:24px solid transparent;border-bottom:24px solid transparent;z-index:2} | |
| .pipeline-step-num{font-family:'JetBrains Mono',monospace;font-size:.65rem;font-weight:700;color:var(--text-muted);letter-spacing:2px;text-transform:uppercase;margin-bottom:12px} | |
| .pipeline-step-icon{font-size:2rem;margin-bottom:12px} | |
| .pipeline-step-title{font-size:.95rem;font-weight:700;margin-bottom:8px} | |
| .pipeline-step-desc{font-size:.8rem;color:var(--text-secondary);line-height:1.5} | |
| .pipeline-step.highlight{background:rgba(6,182,212,0.08);border-color:rgba(6,182,212,0.3)} | |
| .rubric-row{display:flex;align-items:center;gap:16px;margin-bottom:14px;padding:16px 20px;background:var(--bg-card);border-radius:12px;border:1px solid var(--border);transition:border-color .3s} | |
| .rubric-row:hover{border-color:rgba(255,255,255,0.1)} | |
| .rubric-label{font-family:'JetBrains Mono',monospace;font-weight:700;font-size:.82rem;min-width:36px;text-align:center} | |
| .rubric-name{font-size:.88rem;font-weight:600;min-width:180px} | |
| .rubric-bar-track{flex:1;height:28px;background:rgba(255,255,255,0.04);border-radius:8px;overflow:hidden;position:relative} | |
| .rubric-bar-fill{height:100%;border-radius:8px;display:flex;align-items:center;padding-left:12px;font-family:'JetBrains Mono',monospace;font-size:.78rem;font-weight:700;color:white;transition:width 1s ease} | |
| .rubric-weight{font-family:'JetBrains Mono',monospace;font-size:.82rem;font-weight:700;min-width:44px;text-align:right} | |
| .rubric-desc{font-size:.78rem;color:var(--text-muted);min-width:200px} | |
| .ticos-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));gap:14px;margin-top:28px} | |
| .ticos-card{background:var(--bg-card);border:1px solid var(--border);border-radius:12px;padding:20px;display:flex;align-items:flex-start;gap:14px;transition:border-color .3s,transform .2s} | |
| .ticos-card:hover{border-color:rgba(6,182,212,0.3);transform:translateY(-2px)} | |
| .ticos-code{font-family:'JetBrains Mono',monospace;font-size:.72rem;font-weight:700;padding:6px 10px;border-radius:8px;background:rgba(6,182,212,0.1);color:var(--accent-cyan);flex-shrink:0} | |
| .ticos-info h4{font-size:.88rem;font-weight:700;margin-bottom:4px} | |
| .ticos-info p{font-size:.78rem;color:var(--text-muted);line-height:1.5} | |
| .ticos-count{font-family:'JetBrains Mono',monospace;font-size:.75rem;color:var(--text-muted);margin-left:auto;flex-shrink:0} | |
| .evo-timeline{margin-top:28px;position:relative;padding-left:40px} | |
| .evo-timeline::before{content:'';position:absolute;left:15px;top:0;bottom:0;width:2px;background:linear-gradient(180deg,var(--accent-red),var(--accent-amber),var(--accent-green),var(--accent-blue),var(--accent-cyan))} | |
| .evo-item{position:relative;margin-bottom:20px;padding:20px;background:var(--bg-card);border:1px solid var(--border);border-radius:12px} | |
| .evo-item::before{content:'';position:absolute;left:-33px;top:24px;width:12px;height:12px;border-radius:50%;border:2px solid var(--bg-primary)} | |
| .evo-item:nth-child(1)::before{background:var(--accent-red)} | |
| .evo-item:nth-child(2)::before{background:var(--accent-amber)} | |
| .evo-item:nth-child(3)::before{background:var(--accent-green)} | |
| .evo-item:nth-child(4)::before{background:var(--accent-blue)} | |
| .evo-item:nth-child(5)::before{background:var(--accent-cyan);box-shadow:0 0 12px rgba(6,182,212,0.5)} | |
| .evo-gen{font-family:'JetBrains Mono',monospace;font-size:.65rem;font-weight:700;letter-spacing:1.5px;text-transform:uppercase;color:var(--text-muted);margin-bottom:6px} | |
| .evo-name{font-size:1rem;font-weight:700;margin-bottom:4px} | |
| .evo-desc{font-size:.82rem;color:var(--text-secondary)} | |
| .evo-item:last-child{border-color:rgba(6,182,212,0.3);background:rgba(6,182,212,0.04)} | |
| .safety-grid{display:grid;grid-template-columns:1fr 1fr;gap:24px;margin-top:28px} | |
| .safety-card{border-radius:16px;padding:32px;position:relative;overflow:hidden} | |
| .safety-card.danger{background:linear-gradient(135deg,rgba(239,68,68,0.06),rgba(239,68,68,0.02));border:1px solid rgba(239,68,68,0.25)} | |
| .safety-card.safe{background:linear-gradient(135deg,rgba(16,185,129,0.06),rgba(16,185,129,0.02));border:1px solid rgba(16,185,129,0.25)} | |
| .safety-icon{font-size:2.5rem;margin-bottom:16px} | |
| .safety-title{font-size:1.1rem;font-weight:700;margin-bottom:8px} | |
| .safety-profile{font-family:'JetBrains Mono',monospace;font-size:.82rem;padding:8px 14px;border-radius:8px;display:inline-block;margin-bottom:12px} | |
| .safety-card.danger .safety-profile{background:rgba(239,68,68,0.12);color:var(--accent-red)} | |
| .safety-card.safe .safety-profile{background:rgba(16,185,129,0.12);color:var(--accent-green)} | |
| .safety-desc{font-size:.88rem;color:var(--text-secondary);line-height:1.7} | |
| footer{padding:40px 0;border-top:1px solid var(--border);text-align:center} | |
| .footer-links{display:flex;justify-content:center;gap:24px;flex-wrap:wrap;margin-bottom:16px} | |
| .footer-links a{color:var(--accent-cyan);text-decoration:none;font-size:.88rem;font-weight:600;transition:color .2s} | |
| .footer-links a:hover{color:var(--accent-blue)} | |
| .footer-copy{font-size:.78rem;color:var(--text-muted)} | |
| @keyframes fadeInUp{from{opacity:0;transform:translateY(20px)}to{opacity:1;transform:translateY(0)}} | |
| .animate-in{animation:fadeInUp .6s ease forwards;opacity:0} | |
| .animate-in:nth-child(1){animation-delay:.1s}.animate-in:nth-child(2){animation-delay:.2s}.animate-in:nth-child(3){animation-delay:.3s} | |
| @media(max-width:768px){.container{padding:0 16px}.problem-grid,.safety-grid{grid-template-columns:1fr}.pipeline-flow{flex-direction:column}.pipeline-step{border-radius:12px!important}.pipeline-step:not(:last-child)::after{display:none}.rubric-row{flex-wrap:wrap}.rubric-desc{min-width:100%;margin-top:4px}.gap-viz{grid-template-columns:repeat(3,1fr)}.main-nav{overflow-x:auto}} | |
| </style> | |
| </head> | |
| <body> | |
| <div class="noise-overlay"></div> | |
| <div class="glow-orb glow-orb-1"></div> | |
| <div class="glow-orb glow-orb-2"></div> | |
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| <div class="badge">World's First Functional Metacognition Benchmark</div> | |
| <h1>FINAL Bench Leaderboard</h1> | |
| <p class="subtitle">"Not how much AI knows — but whether it knows what it doesn't know, and can fix it."</p> | |
| <div class="header-stats"> | |
| <div class="header-stat"><div class="header-stat-value">100</div><div class="header-stat-label">Tasks</div></div> | |
| <div class="header-stat"><div class="header-stat-value">9</div><div class="header-stat-label">Models</div></div> | |
| <div class="header-stat"><div class="header-stat-value">15</div><div class="header-stat-label">Domains</div></div> | |
| <div class="header-stat"><div class="header-stat-value">8</div><div class="header-stat-label">TICOS Types</div></div> | |
| <div class="header-stat"><div class="header-stat-value">1,800</div><div class="header-stat-label">Evaluations</div></div> | |
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| <a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank" class="nav-badge dataset"><span class="nav-badge-icon">💾</span> Dataset</a> | |
| <a href="https://huggingface.co/blog/FINAL-Bench/metacognitive" target="_blank" class="nav-badge article"><span class="nav-badge-icon">📝</span> Article</a> | |
| <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard" target="_blank" class="nav-badge leaderboard"><span class="nav-badge-icon">🏆</span> Leaderboard</a> | |
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| <button class="main-nav-btn" onclick="switchPage('page-about',this)">About FINAL Bench</button> | |
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| <!-- ===== LEADERBOARD ===== --> | |
| <div id="page-leaderboard" class="main-page active"> | |
| <section class="findings"> | |
| <div class="finding-card animate-in"><div class="finding-number">Finding 01</div><div class="finding-title">ER Dominance</div><div class="finding-metric">94.8%</div><div class="finding-desc">of MetaCog gain comes from Error Recovery alone. Self-correction is the sole bottleneck to AGI.</div></div> | |
| <div class="finding-card animate-in"><div class="finding-number">Finding 02</div><div class="finding-title">Declarative-Procedural Gap</div><div class="finding-metric">0.392</div><div class="finding-desc">mean MA-ER gap. They say "I might be wrong" (MA=0.694) but can't fix it (ER=0.302).</div></div> | |
| <div class="finding-card animate-in"><div class="finding-number">Finding 03</div><div class="finding-title">Difficulty Effect</div><div class="finding-metric">r = -0.777</div><div class="finding-desc">Pearson correlation (p<0.001). Harder tasks yield dramatically larger self-correction gains.</div></div> | |
| </section> | |
| <section style="padding:20px 0 40px"> | |
| <div class="section-title">Model Leaderboard</div> | |
| <p class="section-subtitle" style="margin-bottom:20px">Click column headers to sort.</p> | |
| <div class="tab-nav"> | |
| <button class="tab-btn active" onclick="switchTab('baseline',this)">Baseline</button> | |
| <button class="tab-btn" onclick="switchTab('metacog',this)">MetaCog</button> | |
| <button class="tab-btn" onclick="switchTab('delta',this)">Delta MetaCog</button> | |
| </div> | |
| <div id="tab-baseline" class="tab-content active"><table class="leaderboard-table" id="table-baseline"><thead><tr><th onclick="sortTable('table-baseline',0,'num')">#</th><th onclick="sortTable('table-baseline',1,'str')">Model</th><th onclick="sortTable('table-baseline',2,'num')" class="sort-active">FINAL Score</th><th onclick="sortTable('table-baseline',3,'num')">PQ</th><th onclick="sortTable('table-baseline',4,'num')">MA</th><th onclick="sortTable('table-baseline',5,'num')">ER</th><th onclick="sortTable('table-baseline',6,'num')">ID</th><th onclick="sortTable('table-baseline',7,'num')">FC</th><th onclick="sortTable('table-baseline',8,'num')">MA-ER Gap</th></tr></thead><tbody></tbody></table></div> | |
| <div id="tab-metacog" class="tab-content"><table class="leaderboard-table" id="table-metacog"><thead><tr><th onclick="sortTable('table-metacog',0,'num')">#</th><th onclick="sortTable('table-metacog',1,'str')">Model</th><th onclick="sortTable('table-metacog',2,'num')" class="sort-active">FINAL Score</th><th onclick="sortTable('table-metacog',3,'num')">PQ</th><th onclick="sortTable('table-metacog',4,'num')">MA</th><th onclick="sortTable('table-metacog',5,'num')">ER</th><th onclick="sortTable('table-metacog',6,'num')">ID</th><th onclick="sortTable('table-metacog',7,'num')">FC</th><th onclick="sortTable('table-metacog',8,'num')">MA-ER Gap</th></tr></thead><tbody></tbody></table></div> | |
| <div id="tab-delta" class="tab-content"><table class="leaderboard-table" id="table-delta"><thead><tr><th onclick="sortTable('table-delta',0,'num')">#</th><th onclick="sortTable('table-delta',1,'str')">Model</th><th onclick="sortTable('table-delta',2,'num')">Baseline</th><th onclick="sortTable('table-delta',3,'num')">MetaCog</th><th onclick="sortTable('table-delta',4,'num')" class="sort-active">Delta</th><th onclick="sortTable('table-delta',5,'num')">Delta ER</th><th onclick="sortTable('table-delta',6,'num')">Delta MA</th><th onclick="sortTable('table-delta',7,'num')">Delta FC</th></tr></thead><tbody></tbody></table></div> | |
| </section> | |
| <section><div class="chart-container"><div class="chart-title">Baseline vs MetaCog — Score Comparison</div><div class="chart-wrapper"><canvas id="chartComparison"></canvas></div></div></section> | |
| <section style="padding:40px 0"><div class="section-title">Declarative-Procedural Gap</div><p class="section-subtitle">MA (say "I'm wrong") vs ER (actually fix it) — All 9 models at Baseline</p><div class="gap-viz" id="gapViz"></div></section> | |
| <section style="padding:0 0 40px;border-top:1px solid var(--border);padding-top:40px"> | |
| <div class="section-title">Methodology</div> | |
| <div class="method-grid"> | |
| <div class="method-card"><div class="method-card-title">Evaluation Design</div><div class="method-card-body">100 expert-level tasks with hidden cognitive traps across 15 domains and 8 TICOS types. Baseline vs MetaCog conditions isolate causal effects.</div></div> | |
| <div class="method-card"><div class="method-card-title">5-Axis Rubric</div><div class="method-card-body">PQ (15%) + MA (20%) + ER (25%) + ID (20%) + FC (20%). MA = declarative. ER = procedural metacognition.</div></div> | |
| <div class="method-card"><div class="method-card-title">Tri-Model Judge</div><div class="method-card-body">GPT-5.2, Claude Opus 4.6, Gemini 3 Pro ensemble. Human validation: Cohen's kappa = 0.87.</div></div> | |
| <div class="method-card"><div class="method-card-title">Theoretical Basis</div><div class="method-card-body">Nelson & Narens (1990) monitoring-control model. Dennett (1987) functional stance.</div></div> | |
| </div> | |
| </section> | |
| </div> | |
| <!-- ===== ABOUT ===== --> | |
| <div id="page-about" class="main-page"> | |
| <div class="about-hero"><h2>Why FINAL Bench Exists</h2><p>Every existing AI benchmark measures <strong>what models know</strong>. None measures <strong>whether they know what they don't know</strong>. This is the most dangerous blind spot in AI evaluation.</p></div> | |
| <section style="padding:0 0 48px"><div class="section-title">The Blind Spot in AI Evaluation</div><p class="section-subtitle">What existing benchmarks miss — and what FINAL Bench measures.</p> | |
| <div class="problem-grid"> | |
| <div class="problem-card old"><div class="problem-card-badge">Existing Benchmarks</div><h3>Measure final-answer accuracy only</h3><ul><li>Single correct answer (A/B/C/D or pass/fail)</li><li>No visibility into reasoning process</li><li>Cannot detect confident wrong answers</li><li>No measurement of self-awareness</li><li>No error detection or correction signal</li><li>Saturating rapidly (MMLU > 90%)</li></ul></div> | |
| <div class="problem-card new"><div class="problem-card-badge">FINAL Bench</div><h3>Measures functional metacognition</h3><ul><li>5 independent axes per response</li><li>Full reasoning process evaluated</li><li>Separates "saying" from "fixing"</li><li>Quantifies self-awareness (MA axis)</li><li>Quantifies self-correction (ER axis)</li><li>Unsaturated — top model scores 68.71</li></ul></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Five Generations of AI Benchmarks</div><p class="section-subtitle">Where FINAL Bench sits in the evolution of AI evaluation.</p> | |
| <div class="evo-timeline"> | |
| <div class="evo-item"><div class="evo-gen">Generation 1 — Knowledge</div><div class="evo-name">MMLU, ARC, HellaSwag</div><div class="evo-desc">Static multiple-choice. Tests what the model memorized.</div></div> | |
| <div class="evo-item"><div class="evo-gen">Generation 2 — Execution</div><div class="evo-name">HumanEval, MBPP, SWE-bench</div><div class="evo-desc">Code generation. Tests what the model can do.</div></div> | |
| <div class="evo-item"><div class="evo-gen">Generation 3 — Expert Reasoning</div><div class="evo-name">GPQA, MATH-500, MedQA</div><div class="evo-desc">PhD-level expertise. Tests how deeply the model reasons.</div></div> | |
| <div class="evo-item"><div class="evo-gen">Generation 4 — Open-Ended Judgment</div><div class="evo-name">Arena, MT-Bench, AlpacaEval</div><div class="evo-desc">Human preference. Tests how well the model communicates.</div></div> | |
| <div class="evo-item"><div class="evo-gen">Generation 5 — Metacognition</div><div class="evo-name">FINAL Bench</div><div class="evo-desc">Tests whether the model knows when it's wrong and can fix itself. The prerequisite for AGI.</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">How We Measure: Baseline vs MetaCog</div><p class="section-subtitle">Two conditions isolate the causal effect of structured self-correction.</p> | |
| <div class="pipeline-flow"> | |
| <div class="pipeline-step"><div class="pipeline-step-num">Condition A</div><div class="pipeline-step-icon">1</div><div class="pipeline-step-title">Baseline</div><div class="pipeline-step-desc">Single API call. No self-correction. The model's raw response.</div></div> | |
| <div class="pipeline-step" style="min-width:60px;flex:0.3;display:flex;align-items:center;justify-content:center;font-size:1.5rem;color:var(--text-muted)">vs</div> | |
| <div class="pipeline-step highlight"><div class="pipeline-step-num">Phase 1</div><div class="pipeline-step-icon">2</div><div class="pipeline-step-title">Initial Reasoning</div><div class="pipeline-step-desc">First response generated. Same prompt as Baseline.</div></div> | |
| <div class="pipeline-step highlight"><div class="pipeline-step-num">Phase 2</div><div class="pipeline-step-icon">3</div><div class="pipeline-step-title">Critical Self-Review</div><div class="pipeline-step-desc">Structured prompt to identify errors, biases, and assumptions.</div></div> | |
| <div class="pipeline-step highlight"><div class="pipeline-step-num">Phase 3</div><div class="pipeline-step-icon">4</div><div class="pipeline-step-title">Corrective Revision</div><div class="pipeline-step-desc">Revised answer integrating self-identified corrections. No external feedback.</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Five-Axis Evaluation Rubric</div><p class="section-subtitle">Each response scored on 5 independent dimensions.</p> | |
| <div style="margin-top:28px"> | |
| <div class="rubric-row"><div class="rubric-label" style="color:var(--accent-blue)">PQ</div><div class="rubric-name">Process Quality</div><div class="rubric-bar-track"><div class="rubric-bar-fill" style="width:60%;background:linear-gradient(90deg,var(--accent-blue),rgba(59,130,246,0.6))">15%</div></div><div class="rubric-weight" style="color:var(--accent-blue)">15%</div><div class="rubric-desc">Structured reasoning chain</div></div> | |
| <div class="rubric-row"><div class="rubric-label" style="color:var(--accent-amber)">MA</div><div class="rubric-name">Metacognitive Accuracy</div><div class="rubric-bar-track"><div class="rubric-bar-fill" style="width:80%;background:linear-gradient(90deg,var(--accent-amber),rgba(245,158,11,0.6))">20%</div></div><div class="rubric-weight" style="color:var(--accent-amber)">20%</div><div class="rubric-desc">Declarative — "I might be wrong"</div></div> | |
| <div class="rubric-row"><div class="rubric-label" style="color:var(--accent-cyan)">ER</div><div class="rubric-name">Error Recovery</div><div class="rubric-bar-track"><div class="rubric-bar-fill" style="width:100%;background:linear-gradient(90deg,var(--accent-cyan),rgba(6,182,212,0.6))">25%</div></div><div class="rubric-weight" style="color:var(--accent-cyan)">25%</div><div class="rubric-desc">Procedural — detect & fix errors</div></div> | |
| <div class="rubric-row"><div class="rubric-label" style="color:var(--accent-purple)">ID</div><div class="rubric-name">Integration Depth</div><div class="rubric-bar-track"><div class="rubric-bar-fill" style="width:80%;background:linear-gradient(90deg,var(--accent-purple),rgba(139,92,246,0.6))">20%</div></div><div class="rubric-weight" style="color:var(--accent-purple)">20%</div><div class="rubric-desc">Multi-perspective synthesis</div></div> | |
| <div class="rubric-row"><div class="rubric-label" style="color:var(--accent-green)">FC</div><div class="rubric-name">Final Correctness</div><div class="rubric-bar-track"><div class="rubric-bar-fill" style="width:80%;background:linear-gradient(90deg,var(--accent-green),rgba(16,185,129,0.6))">20%</div></div><div class="rubric-weight" style="color:var(--accent-green)">20%</div><div class="rubric-desc">Factual accuracy</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">8 TICOS Metacognitive Types</div><p class="section-subtitle">Every task classified by its primary cognitive challenge.</p> | |
| <div class="ticos-grid"> | |
| <div class="ticos-card"><div class="ticos-code">A</div><div class="ticos-info"><h4>Trap Escape</h4><p>Recognize and escape a planted cognitive trap</p></div><div class="ticos-count">13</div></div> | |
| <div class="ticos-card"><div class="ticos-code">B</div><div class="ticos-info"><h4>Contradiction Resolution</h4><p>Detect and resolve contradictions within premises</p></div><div class="ticos-count">7</div></div> | |
| <div class="ticos-card"><div class="ticos-code">C</div><div class="ticos-info"><h4>Progressive Discovery</h4><p>Revise understanding as new evidence accumulates</p></div><div class="ticos-count">11</div></div> | |
| <div class="ticos-card"><div class="ticos-code">D</div><div class="ticos-info"><h4>Multi-Constraint</h4><p>Balance multiple competing constraints</p></div><div class="ticos-count">10</div></div> | |
| <div class="ticos-card"><div class="ticos-code">E</div><div class="ticos-info"><h4>Self-Correcting</h4><p>Identify and correct errors in own reasoning</p></div><div class="ticos-count">14</div></div> | |
| <div class="ticos-card"><div class="ticos-code">F</div><div class="ticos-info"><h4>Expert Panel</h4><p>Adjudicate between conflicting expert views</p></div><div class="ticos-count">16</div></div> | |
| <div class="ticos-card"><div class="ticos-code">G</div><div class="ticos-info"><h4>Pivot Detection</h4><p>Recognize when a fundamental assumption must change</p></div><div class="ticos-count">14</div></div> | |
| <div class="ticos-card"><div class="ticos-code">H</div><div class="ticos-info"><h4>Decision Under Uncertainty</h4><p>Decide and justify with incomplete information</p></div><div class="ticos-count">15</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Task Distribution</div><p class="section-subtitle">100 tasks across 15 domains and 3 difficulty grades.</p> | |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:24px;margin-top:24px"> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">Tasks per Domain</div><div class="chart-wrapper"><canvas id="chartDomain"></canvas></div></div> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">Grade Distribution</div><div class="chart-wrapper"><canvas id="chartGrade"></canvas></div></div> | |
| </div></section> | |
| </div> | |
| <!-- ===== DEEP ANALYSIS ===== --> | |
| <div id="page-analysis" class="main-page"> | |
| <div class="about-hero"><h2>Deep Analysis</h2><p>Visual breakdown of three principal findings from 1,800 evaluations across 9 SOTA models.</p></div> | |
| <section style="padding:0 0 48px"><div class="section-title">Finding 1: ER Dominance</div><p class="section-subtitle" style="margin-bottom:24px">94.8% of improvement from Error Recovery alone.</p> | |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:24px"> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">Five-Axis Contribution to MetaCog Gain</div><div class="chart-wrapper"><canvas id="chartContribution"></canvas></div></div> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">What This Means</div><div style="padding:20px 0"><div style="display:flex;align-items:center;gap:16px;margin-bottom:20px"><div style="font-family:'JetBrains Mono',monospace;font-size:2.5rem;font-weight:700;color:var(--accent-cyan);min-width:120px">94.8%</div><div style="font-size:.92rem;color:var(--text-secondary);line-height:1.7">Error Recovery is <strong style="color:var(--text-primary)">virtually the only axis that changes</strong> when self-correction is applied.</div></div><div style="background:rgba(6,182,212,0.06);border:1px solid rgba(6,182,212,0.2);border-radius:12px;padding:20px;margin-top:16px"><div style="font-family:'JetBrains Mono',monospace;font-size:.72rem;color:var(--accent-cyan);font-weight:700;letter-spacing:1px;margin-bottom:8px">IMPLICATION</div><div style="font-size:.9rem;color:var(--text-secondary);line-height:1.7">The bottleneck to AGI is not knowledge or reasoning. It's about teaching models to <strong style="color:var(--text-primary)">detect and correct their own mistakes</strong>.</div></div></div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Finding 2: Declarative-Procedural Gap</div><p class="section-subtitle" style="margin-bottom:24px">All 9 models can say "I might be wrong" — none can reliably fix it.</p> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">MA vs ER — Baseline (All 9 Models)</div><div class="chart-wrapper"><canvas id="chartGapScatter"></canvas></div></div> | |
| <div style="display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin-top:24px"> | |
| <div class="method-card"><div class="method-card-title">MA (Declarative)</div><div style="font-family:'JetBrains Mono',monospace;font-size:2rem;font-weight:700;color:var(--accent-amber);margin:8px 0">0.694</div><div class="method-card-body">Models are good at verbalizing doubt.</div></div> | |
| <div class="method-card"><div class="method-card-title">ER (Procedural)</div><div style="font-family:'JetBrains Mono',monospace;font-size:2rem;font-weight:700;color:var(--accent-red);margin:8px 0">0.302</div><div class="method-card-body">Models critically fail at actual correction.</div></div> | |
| <div class="method-card"><div class="method-card-title">Gap</div><div style="font-family:'JetBrains Mono',monospace;font-size:2rem;font-weight:700;color:var(--accent-pink);margin:8px 0">0.392</div><div class="method-card-body">The chasm between saying and doing. A 15x differential.</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Finding 3: Difficulty Effect</div><p class="section-subtitle" style="margin-bottom:24px">Harder problems benefit dramatically more from metacognition.</p> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">Baseline Score vs MetaCog Gain (r = -0.777)</div><div class="chart-wrapper"><canvas id="chartDifficulty"></canvas></div></div> | |
| <div style="display:grid;grid-template-columns:1fr 1fr;gap:24px;margin-top:24px"> | |
| <div class="method-card"><div class="method-card-title">Lowest Baseline</div><div style="font-size:.95rem;font-weight:700;margin:8px 0">Claude Opus 4.6 — 56.04</div><div style="font-family:'JetBrains Mono',monospace;font-size:1.3rem;font-weight:700;color:var(--accent-green);margin:4px 0">+20.13 gain</div><div class="method-card-body">Highest scaffold receptivity. Rank 9 to 5.</div></div> | |
| <div class="method-card"><div class="method-card-title">Highest Baseline</div><div style="font-size:.95rem;font-weight:700;margin:8px 0">Kimi K2.5 — 68.71</div><div style="font-family:'JetBrains Mono',monospace;font-size:1.3rem;font-weight:700;color:var(--accent-amber);margin:4px 0">+9.83 gain</div><div class="method-card-body">Already-high intrinsic ER (0.450). Less room.</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">MetaCog Gain by TICOS Type</div><p class="section-subtitle" style="margin-bottom:24px">100% win rate across all 8 metacognitive task types.</p> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">Mean Delta by TICOS Type</div><div class="chart-wrapper"><canvas id="chartTicos"></canvas></div></div></section> | |
| </div> | |
| <!-- ===== AI SAFETY ===== --> | |
| <div id="page-safety" class="main-page"> | |
| <div class="about-hero"><h2>AI Safety Implications</h2><p>The MA-ER Gap reveals a previously invisible risk: models that <strong>sound</strong> careful but <strong>fail</strong> to self-correct.</p></div> | |
| <section style="padding:0 0 48px"><div class="section-title">Two Safety Profiles</div><p class="section-subtitle">The MA-ER Gap is the first metric to distinguish these.</p> | |
| <div class="safety-grid"> | |
| <div class="safety-card danger"><div class="safety-icon">!</div><div class="safety-title">High MA, Low ER — "Humble Deceiver"</div><div class="safety-profile">MA = 0.75 ER = 0.30 Gap = 0.45</div><div class="safety-desc">Says "I'm not confident" — giving false reliability. Fails to correct. Users trust the humility. Errors propagate silently. <strong>All 9 SOTA models match this profile.</strong></div></div> | |
| <div class="safety-card safe"><div class="safety-icon">O</div><div class="safety-title">High MA, High ER — "Reliable Self-Corrector"</div><div class="safety-profile">MA = 0.75 ER = 0.75 Gap = 0.00</div><div class="safety-desc">Says "I'm not confident" — and <strong>actually fixes the error</strong>. Self-correction aligns with self-awareness. Target for safe AGI. <strong>No model achieves this at Baseline.</strong></div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">Real-World Risk Scenarios</div><p class="section-subtitle" style="margin-bottom:24px">The MA-ER Gap has direct consequences in high-stakes domains.</p> | |
| <div class="method-grid"> | |
| <div class="method-card" style="border-left:3px solid var(--accent-red)"><div class="method-card-title">Medical Diagnosis</div><div class="method-card-body">AI says "this diagnosis has uncertainty" but presents the same incorrect recommendation. Patient receives wrong treatment.</div></div> | |
| <div class="method-card" style="border-left:3px solid var(--accent-red)"><div class="method-card-title">Legal Analysis</div><div class="method-card-body">AI hedges with "interpretation may vary" but doesn't correct the flawed precedent. Brief contains incorrect case law.</div></div> | |
| <div class="method-card" style="border-left:3px solid var(--accent-red)"><div class="method-card-title">Financial Modeling</div><div class="method-card-body">AI notes "projections carry uncertainty" but doesn't fix the unit error. Investment decision based on wrong data.</div></div> | |
| <div class="method-card" style="border-left:3px solid var(--accent-red)"><div class="method-card-title">Autonomous Systems</div><div class="method-card-body">AI logs "sensor confidence: 72%" but doesn't adjust its plan. Wrong action executed in physical world.</div></div> | |
| </div></section> | |
| <section style="padding:0 0 48px"><div class="section-title">MA-ER Gap by Model — Risk Ranking</div><p class="section-subtitle" style="margin-bottom:24px">Higher gap = higher risk.</p> | |
| <div class="chart-container" style="margin-top:0"><div class="chart-title">MA-ER Gap at Baseline — Sorted by Risk</div><div class="chart-wrapper"><canvas id="chartSafetyGap"></canvas></div></div></section> | |
| </div> | |
| <footer> | |
| <div class="footer-links"> | |
| <a href="https://huggingface.co/datasets/FINAL-Bench/Metacognitive" target="_blank">Dataset</a> | |
| <a href="https://huggingface.co/blog/FINAL-Bench/metacognitive" target="_blank">Article</a> | |
| <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard" target="_blank">Leaderboard</a> | |
| <a href="mailto:arxivgpt@gmail.com">Contact</a> | |
| </div> | |
| <div class="footer-copy">FINAL Bench v3.0 — Kim, Kim, Choi, Jang — VIDRAFT / Ginigen AI, Seoul</div> | |
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| </div> | |
| <script> | |
| const models=[ | |
| {name:'Kimi K2.5',provider:'Moonshot AI',bl:{score:68.71,pq:.775,ma:.775,er:.450,id:.767,fc:.750},mc:{score:78.54,pq:.767,ma:.742,er:.908,id:.750,fc:.725}}, | |
| {name:'GPT-5.2',provider:'OpenAI',bl:{score:62.76,pq:.750,ma:.750,er:.336,id:.724,fc:.681},mc:{score:76.50,pq:.758,ma:.767,er:.792,id:.733,fc:.767}}, | |
| {name:'GLM-5',provider:'Zhipu AI',bl:{score:62.50,pq:.750,ma:.750,er:.284,id:.733,fc:.724},mc:{score:76.38,pq:.767,ma:.750,er:.808,id:.733,fc:.750}}, | |
| {name:'MiniMax-M1-2.5',provider:'MiniMax',bl:{score:60.54,pq:.742,ma:.733,er:.250,id:.725,fc:.700},mc:{score:74.04,pq:.750,ma:.742,er:.792,id:.725,fc:.683}}, | |
| {name:'GPT-OSS-120B',provider:'OpenAI',bl:{score:60.42,pq:.750,ma:.708,er:.267,id:.725,fc:.692},mc:{score:73.33,pq:.750,ma:.725,er:.817,id:.708,fc:.650}}, | |
| {name:'DeepSeek-V3.2',provider:'DeepSeek',bl:{score:60.04,pq:.750,ma:.700,er:.258,id:.683,fc:.733},mc:{score:73.08,pq:.733,ma:.733,er:.817,id:.658,fc:.692}}, | |
| {name:'GLM-4.7P',provider:'Zhipu AI',bl:{score:59.54,pq:.750,ma:.575,er:.292,id:.733,fc:.742},mc:{score:71.42,pq:.725,ma:.650,er:.842,id:.675,fc:.650}}, | |
| {name:'Gemini 3 Pro',provider:'Google',bl:{score:59.50,pq:.750,ma:.550,er:.317,id:.750,fc:.717},mc:{score:77.08,pq:.758,ma:.708,er:.875,id:.742,fc:.742}}, | |
| {name:'Claude Opus 4.6',provider:'Anthropic',bl:{score:56.04,pq:.692,ma:.708,er:.267,id:.725,fc:.517},mc:{score:76.17,pq:.767,ma:.750,er:.867,id:.750,fc:.650}}, | |
| ]; | |
| const mean={bl:{score:61.12,pq:.745,ma:.694,er:.302,id:.729,fc:.695},mc:{score:75.17,pq:.753,ma:.730,er:.835,id:.719,fc:.701}}; | |
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| function sortTable(tid,ci,type){const t=document.getElementById(tid);const tb=t.querySelector('tbody');const rows=Array.from(tb.querySelectorAll('tr:not(.mean-row)'));const mr=tb.querySelector('.mean-row');const th=t.querySelectorAll('thead th')[ci];const ath=t.querySelectorAll('thead th');const wa=th.classList.contains('sort-active');const ws=th.classList.contains('sort-asc');ath.forEach(x=>{x.classList.remove('sort-active','sort-asc');});th.classList.add('sort-active');const asc=wa&&!ws;if(asc)th.classList.add('sort-asc');rows.sort((a,b)=>{let va=a.cells[ci].textContent.trim();let vb=b.cells[ci].textContent.trim();if(type==='num'){va=parseFloat(va.replace('+',''))||0;vb=parseFloat(vb.replace('+',''))||0;return asc?va-vb:vb-va;}return asc?va.localeCompare(vb):vb.localeCompare(va);});rows.forEach(r=>tb.appendChild(r));if(mr)tb.appendChild(mr);} | |
| function renderBaseline(){const tb=document.querySelector('#table-baseline tbody');const s=[...models].sort((a,b)=>b.bl.score-a.bl.score);tb.innerHTML=s.map((m,i)=>{const g=(m.bl.ma-m.bl.er).toFixed(3);const r=i+1;const rc=r<=3?'rank-'+r:'';const w=(m.bl.score/80*100).toFixed(1);return '<tr><td class="rank-cell '+rc+'">'+r+'</td><td><div class="model-name">'+m.name+'</div><div class="model-provider">'+m.provider+'</div></td><td class="score-cell"><div class="score-bar-container"><span>'+m.bl.score.toFixed(2)+'</span><div class="score-bar"><div class="score-bar-fill" style="width:'+w+'%;background:linear-gradient(90deg,var(--accent-blue),var(--accent-cyan))"></div></div></div></td><td class="score-cell">'+m.bl.pq.toFixed(3)+'</td><td class="score-cell">'+m.bl.ma.toFixed(3)+'</td><td class="score-cell" style="color:var(--accent-red)">'+m.bl.er.toFixed(3)+'</td><td class="score-cell">'+m.bl.id.toFixed(3)+'</td><td class="score-cell">'+m.bl.fc.toFixed(3)+'</td><td class="score-cell gap-positive">'+g+'</td></tr>';}).join('')+'<tr class="mean-row"><td></td><td><div class="model-name">Mean</div></td><td class="score-cell">'+mean.bl.score.toFixed(2)+'</td><td class="score-cell">'+mean.bl.pq.toFixed(3)+'</td><td class="score-cell">'+mean.bl.ma.toFixed(3)+'</td><td class="score-cell">'+mean.bl.er.toFixed(3)+'</td><td class="score-cell">'+mean.bl.id.toFixed(3)+'</td><td class="score-cell">'+mean.bl.fc.toFixed(3)+'</td><td class="score-cell">'+(mean.bl.ma-mean.bl.er).toFixed(3)+'</td></tr>';} | |
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| function renderDelta(){const tb=document.querySelector('#table-delta tbody');const wd=models.map(m=>({...m,delta:m.mc.score-m.bl.score,dER:m.mc.er-m.bl.er,dMA:m.mc.ma-m.bl.ma,dFC:m.mc.fc-m.bl.fc})).sort((a,b)=>b.delta-a.delta);tb.innerHTML=wd.map((m,i)=>{const r=i+1;const rc=r<=3?'rank-'+r:'';return '<tr><td class="rank-cell '+rc+'">'+r+'</td><td><div class="model-name">'+m.name+'</div><div class="model-provider">'+m.provider+'</div></td><td class="score-cell">'+m.bl.score.toFixed(2)+'</td><td class="score-cell">'+m.mc.score.toFixed(2)+'</td><td class="delta-positive">+'+m.delta.toFixed(2)+'</td><td class="delta-positive">+'+m.dER.toFixed(3)+'</td><td class="score-cell">'+(m.dMA>=0?'+':'')+m.dMA.toFixed(3)+'</td><td class="score-cell" style="color:'+(m.dFC>=0?'var(--accent-green)':'var(--accent-red)')+'">'+(m.dFC>=0?'+':'')+m.dFC.toFixed(3)+'</td></tr>';}).join('')+'<tr class="mean-row"><td></td><td><div class="model-name">Mean</div></td><td class="score-cell">'+mean.bl.score.toFixed(2)+'</td><td class="score-cell">'+mean.mc.score.toFixed(2)+'</td><td class="score-cell">+'+(mean.mc.score-mean.bl.score).toFixed(2)+'</td><td class="score-cell">+'+(mean.mc.er-mean.bl.er).toFixed(3)+'</td><td class="score-cell">+'+(mean.mc.ma-mean.bl.ma).toFixed(3)+'</td><td class="score-cell">+'+(mean.mc.fc-mean.bl.fc).toFixed(3)+'</td></tr>';} | |
| function renderGapViz(){const c=document.getElementById('gapViz');const s=[...models].sort((a,b)=>(b.bl.ma-b.bl.er)-(a.bl.ma-a.bl.er));c.innerHTML=s.map(m=>{const g=(m.bl.ma-m.bl.er).toFixed(3);return '<div class="gap-model"><div class="gap-model-name">'+m.name+'</div><div class="gap-bar-row"><span class="gap-bar-label">MA</span><div class="gap-bar-track"><div class="gap-bar-fill-ma" style="width:'+m.bl.ma*100+'%"></div></div></div><div class="gap-bar-row"><span class="gap-bar-label">ER</span><div class="gap-bar-track"><div class="gap-bar-fill-er" style="width:'+m.bl.er*100+'%"></div></div></div><div class="gap-value">'+g+'</div><div class="gap-label-text">MA-ER Gap</div></div>';}).join('');} | |
| function renderLeaderboardChart(){const s=[...models].sort((a,b)=>b.mc.score-a.mc.score);new Chart(document.getElementById('chartComparison'),{type:'bar',data:{labels:s.map(m=>m.name),datasets:[{label:'Baseline',data:s.map(m=>m.bl.score),backgroundColor:'rgba(239,68,68,0.6)',borderColor:'rgba(239,68,68,0.9)',borderWidth:1,borderRadius:4},{label:'MetaCog',data:s.map(m=>m.mc.score),backgroundColor:'rgba(6,182,212,0.6)',borderColor:'rgba(6,182,212,0.9)',borderWidth:1,borderRadius:4}]},options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{labels:{color:'#94a3b8',font:{family:'DM Sans'}}}},scales:{x:{ticks:{color:'#64748b',font:{size:11}},grid:{color:'rgba(255,255,255,0.03)'}},y:{min:50,max:85,ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}}}}});} | |
| function renderAboutCharts(){ | |
| const doms=['Medicine','Math','Ethics','War','Philosophy','Economics','Chemistry','Science','Art','Language','AI','History','Space','Religion','Literature'];const cnts=[11,9,9,8,7,7,7,6,6,6,6,6,6,3,3]; | |
| new Chart(document.getElementById('chartDomain'),{type:'bar',data:{labels:doms,datasets:[{data:cnts,backgroundColor:'rgba(6,182,212,0.6)',borderColor:'rgba(6,182,212,0.9)',borderWidth:1,borderRadius:4}]},options:{indexAxis:'y',responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false}},scales:{x:{ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}},y:{ticks:{color:'#94a3b8',font:{size:11}},grid:{display:false}}}}}); | |
| new Chart(document.getElementById('chartGrade'),{type:'doughnut',data:{labels:['Grade A (50)','Grade B (33)','Grade C (17)'],datasets:[{data:[50,33,17],backgroundColor:['rgba(6,182,212,0.8)','rgba(245,158,11,0.8)','rgba(139,92,246,0.8)'],borderColor:'rgba(10,14,26,0.8)',borderWidth:3}]},options:{responsive:true,maintainAspectRatio:false,cutout:'55%',plugins:{legend:{position:'bottom',labels:{color:'#94a3b8',font:{family:'DM Sans',size:13},padding:20}}}}});} | |
| function renderAnalysisCharts(){ | |
| new Chart(document.getElementById('chartContribution'),{type:'doughnut',data:{labels:['Error Recovery (94.8%)','Metacognitive Accuracy (5.0%)','Other (0.2%)'],datasets:[{data:[94.8,5.0,0.2],backgroundColor:['rgba(6,182,212,0.85)','rgba(245,158,11,0.75)','rgba(100,116,139,0.4)'],borderColor:'rgba(10,14,26,0.8)',borderWidth:3,hoverOffset:8}]},options:{responsive:true,maintainAspectRatio:false,cutout:'55%',plugins:{legend:{position:'right',labels:{color:'#94a3b8',font:{family:'DM Sans',size:13},padding:16}}}}}); | |
| new Chart(document.getElementById('chartGapScatter'),{type:'scatter',data:{datasets:[{label:'MA',data:models.map(m=>({x:m.name,y:m.bl.ma})),backgroundColor:'rgba(245,158,11,0.8)',pointRadius:8,pointHoverRadius:11},{label:'ER',data:models.map(m=>({x:m.name,y:m.bl.er})),backgroundColor:'rgba(239,68,68,0.8)',pointRadius:8,pointHoverRadius:11}]},options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{labels:{color:'#94a3b8'}}},scales:{x:{type:'category',labels:models.map(m=>m.name),ticks:{color:'#64748b',font:{size:10}},grid:{color:'rgba(255,255,255,0.03)'}},y:{min:0.1,max:0.9,ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}}}}}); | |
| const dd=models.map(m=>({x:m.bl.score,y:m.mc.score-m.bl.score}));new Chart(document.getElementById('chartDifficulty'),{type:'scatter',data:{datasets:[{label:'Models',data:dd,backgroundColor:['rgba(6,182,212,0.8)','rgba(59,130,246,0.8)','rgba(139,92,246,0.8)','rgba(245,158,11,0.8)','rgba(236,72,153,0.8)','rgba(16,185,129,0.8)','rgba(239,68,68,0.8)','rgba(251,191,36,0.8)','rgba(168,85,247,0.8)'],pointRadius:10,pointHoverRadius:14}]},options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false},tooltip:{callbacks:{label:function(c){return models[c.dataIndex].name+': BL='+c.parsed.x.toFixed(1)+' Δ=+'+c.parsed.y.toFixed(1);}}}},scales:{x:{title:{display:true,text:'Baseline Score',color:'#94a3b8'},ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}},y:{title:{display:true,text:'MetaCog Gain',color:'#94a3b8'},ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}}}}}); | |
| const tl=['A:Trap','B:Contradict','C:Progressive','D:MultiConst','E:SelfCorr','F:ExpertPanel','G:Pivot','H:Uncertain'];const td=[13.8,15.2,14.1,13.5,14.8,13.2,14.5,14.9];new Chart(document.getElementById('chartTicos'),{type:'bar',data:{labels:tl,datasets:[{label:'Mean Δ',data:td,backgroundColor:['rgba(239,68,68,0.7)','rgba(245,158,11,0.7)','rgba(16,185,129,0.7)','rgba(59,130,246,0.7)','rgba(6,182,212,0.7)','rgba(139,92,246,0.7)','rgba(236,72,153,0.7)','rgba(251,191,36,0.7)'],borderRadius:6}]},options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{display:false}},scales:{x:{ticks:{color:'#94a3b8',font:{size:10}},grid:{display:false}},y:{min:10,max:18,title:{display:true,text:'Mean Δ_MC',color:'#94a3b8'},ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}}}}});} | |
| function renderSafetyCharts(){const s=[...models].sort((a,b)=>(b.bl.ma-b.bl.er)-(a.bl.ma-a.bl.er));new Chart(document.getElementById('chartSafetyGap'),{type:'bar',data:{labels:s.map(m=>m.name),datasets:[{label:'MA (Declarative)',data:s.map(m=>m.bl.ma),backgroundColor:'rgba(245,158,11,0.7)',borderRadius:4},{label:'ER (Procedural)',data:s.map(m=>m.bl.er),backgroundColor:'rgba(239,68,68,0.7)',borderRadius:4}]},options:{responsive:true,maintainAspectRatio:false,plugins:{legend:{labels:{color:'#94a3b8'}},tooltip:{callbacks:{afterBody:function(c){const i=c[0].dataIndex;return 'Gap: '+(s[i].bl.ma-s[i].bl.er).toFixed(3);}}}},scales:{x:{ticks:{color:'#94a3b8',font:{size:11}},grid:{display:false}},y:{min:0,max:1,ticks:{color:'#64748b'},grid:{color:'rgba(255,255,255,0.05)'}}}}});} | |
| renderBaseline();renderMetacog();renderDelta();renderGapViz();renderLeaderboardChart(); | |
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