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README.md

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   <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🧬_FINAL_Bench-Leaderboard-teal?style=flat-square" alt="FINAL Leaderboard"></a>
 </p>
 
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 ## Dataset Summary
 
 ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for **91 AI models** across 6 modalities. Every numerical score is tagged with a confidence level (`cross-verified`, `single-source`, or `self-reported`) and its original source. The dataset is designed for researchers, developers, and decision-makers who need a trustworthy, unified view of the AI model landscape.
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 | **Video Gen** | 10 | 7 fields | Quality, motion, consistency, text rendering, duration, resolution |
 | **Music Gen** | 8 | 6 fields | Quality, vocals, instrumental, lyrics, duration |
 
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 ## Live Leaderboard
 
 👉 **[https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard](https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard)**
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 | `elo` | int \| null | Arena Elo rating |
 | `license` | string | `Prop`, `Apache2`, `MIT`, `Open`, etc. |
 
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 ## Composite Score
 
 ```
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 # → {"level": "single-source", "source": "Google DeepMind model card"}
 ```
 
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 ## FINAL Bench — Metacognitive Benchmark
 
 FINAL Bench measures AI self-correction ability. Error Recovery (ER) explains 94.8% of metacognitive performance variance. 9 frontier models evaluated.