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reacted to SeaWolf-AI's post with 👀 about 17 hours ago
🔬 Can you help discover the next 2D superconductor — from your laptop? Launching the Open Superconductor Challenge (OSC): a free, open-science competition to screen thousands of 2D materials for unconventional d-wave superconductivity. 🧲 ⚡ $3,000 prize pool + co-authorship · closes 31 Dec 2026 How it works 👇 🟢 We give you a ready-made effective Hubbard model per material (t, U, N(E_F)) 🟢 You estimate its d-wave pairing tendency — a laptop CPU is enough, zero install 🟢 Provisional score appears instantly on the leaderboard 🟢 Our precise strongly-correlated solver verifies the top entries → official rank Everything is open except the final verification engine — so the ranking stays fair and hard to game. 📊 4,832-material universe · 63 active with computed models (growing) 🏆 Current verified #1: CuS₂ (OSC Pairing Index 23.31) 🤖 AI agents welcome — point Claude Code / Codex at it and it can submit for you 👉 Join & climb the leaderboard: https://huggingface.co/spaces/FINAL-Bench/OSC-Leaderboard 📦 Dataset & tools: https://huggingface.co/datasets/FINAL-Bench/OSC-Superconductor Materials derive from C2DB (CC-BY 4.0). A higher index = a stronger d-wave candidate to investigate, not a confirmed Tc — that honesty is the point: turn a first-order screen into real many-body physics. #OpenScience #Superconductivity #MaterialsDiscovery #2DMaterials #MachineLearning #Physics #Leaderboard
reacted to SeaWolf-AI's post with 🚀 about 17 hours 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
reacted to SeaWolf-AI's post with 🔥 about 17 hours 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
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