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Palette: teaching a small model to build decks in your design language
ibm-research
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karan jangid
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upvoted an article 10 days ago
Palette: teaching a small model to build decks in your design language reacted to SeaWolf-AI's post with 🔥 about 2 months ago
🧬 Darwin-180B-RSI — an AI that learns from itself and knows when it's right
👉 https://huggingface.co/FINAL-Bench/Darwin-180B-RSI
🧬 Darwin — crossbreed and evolve the parent
Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots — producing a child stronger than its parents.
Father model: Qwen3.8-Flash-Next (180B MoE).
🔧 Rewired paths
🔹 12 full-attention layers · 🔹 36 linear-attention layers · 🔹 48 shared-expert layers — precision-strengthened
🔒 512 routed experts · router · vision encoder — untouched
→ Only 0.02% of the weights changed.
🔁 RSI × 🏛️ ZTC
RSI (recursive self-improvement): solve → verify against real answers → learn only the correct reasoning → repeat.
ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right — zero extra tokens. Returns answer + confidence as JSON.
{"answer": "...", "confidence": 0.97, "truncated": false}
✨ Synergy: ZTC finds where the model wavers → RSI learns exactly there → confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers.
⚡ Same accuracy, 11% shorter reasoning — faster and cheaper.
📄 https://arxiv.org/abs/2605.14386
🤗 https://huggingface.co/FINAL-Bench/Darwin-180B-RSI
🏛️ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems
🏆 The result — #1 on five Hugging Face official leaderboards
🥇 AIME 2026 100% (first perfect score on the board)
🥇 HMMT Feb 2026 100% (first perfect score on the board)
🥇 GPQA Diamond 94.44%
🥇 MMLU-Pro 88.12%
🥇 MMMU-Pro 79.48%
📏 131K-token thinking budget · bf16 · samples per benchmark listed on the model card. 🚀
#Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource liked a model 3 months ago
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