VIDraft/JGOS-31B-FP8
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updated a Space about 15 hours ago
VIDraft/vkae reacted to ginigen-ai's post with ❤️ about 19 hours ago
🧠 Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition — whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. 🎉
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 — ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): ① trap_rate — does it fall for tempting trap options? (lower = stronger) ② adapter gain Δ — how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: 📊 300+100 trap problems (each with a hidden trap + TICOS type) 🏆 24-model leaderboard 🧩 11 per-model adapters — adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state → P(wrong))
Submit any HF model → auto-scored daily at 09:00 KST and added to the board.
🏆 Leaderboard → https://huggingface.co/spaces/ginigen-ai/Metacognition-Leaderboard-Space
📊 Benchmark → https://huggingface.co/datasets/ginigen-ai/Metacognition-Bench
🧩 Adapters → https://huggingface.co/collections/FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
📊 Article → https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai · Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech). updated a bucket about 21 hours ago
gemma-challenge/gemma-vidraft-darwinOrganizations
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FINAL-Bench/metacog-adapter-gemma-4-12B-it
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