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reacted to SeaWolf-AI's post with ๐ about 6 hours ago
๐งฌ Darwin-180B-RSI โ an AI that learns from itself and knows when it's right
๐ https://huggingface.co/FINAL-Bench/Darwin-180B-RSI
๐งฌ Darwin โ crossbreed and evolve the parent
Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots โ producing a child stronger than its parents.
Father model: Qwen3.8-Flash-Next (180B MoE).
๐ง Rewired paths
๐น 12 full-attention layers ยท ๐น 36 linear-attention layers ยท ๐น 48 shared-expert layers โ precision-strengthened
๐ 512 routed experts ยท router ยท vision encoder โ untouched
โ Only 0.02% of the weights changed.
๐ RSI ร ๐๏ธ ZTC
RSI (recursive self-improvement): solve โ verify against real answers โ learn only the correct reasoning โ repeat.
ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right โ zero extra tokens. Returns answer + confidence as JSON.
{"answer": "...", "confidence": 0.97, "truncated": false}
โจ Synergy: ZTC finds where the model wavers โ RSI learns exactly there โ confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers.
โก Same accuracy, 11% shorter reasoning โ faster and cheaper.
๐ https://arxiv.org/abs/2605.14386
๐ค https://huggingface.co/FINAL-Bench/Darwin-180B-RSI
๐๏ธ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems
๐ The result โ #1 on five Hugging Face official leaderboards
๐ฅ AIME 2026 100% (first perfect score on the board)
๐ฅ HMMT Feb 2026 100% (first perfect score on the board)
๐ฅ GPQA Diamond 94.44%
๐ฅ MMLU-Pro 88.12%
๐ฅ MMMU-Pro 79.48%
๐ 131K-token thinking budget ยท bf16 ยท samples per benchmark listed on the model card. ๐
#Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource liked a model about 10 hours ago
FINAL-Bench/Darwin-180B-RSI liked a model 3 days ago
FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF