Open Superconductor Challenge: Help Discover the Next 2D Superconductor β From Your Laptop
β’ 12
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Contact: arxivgpt@gmail.com
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Does it still hallucinate?
π€π₯ 3
3
#4 opened about 4 hours ago
by 
vstiff
[NOTICE] ZTC probe for Darwin-180B-RSI is now in the repo
β€οΈπ 5
#9 opened about 1 hour ago
by 
SeaWolf-AI
Inside Darwin-180B-RSI: Whatβs Different Under the Hood?
π 2
2
#8 opened about 2 hours ago
by 
nvmhtfile
π© Report: Spam
3
#7 opened about 2 hours ago
by 
DedeProGames
How good this model is at agentic coding?
19
#1 opened 1 day ago
by DedeProGames
Bring Darwin-180B-RSI to DGX Spark!
ββ€οΈ 3
1
#6 opened about 3 hours ago
by 
solarbeams
Would anyone be willing to create a quantized version of Darwin-180B-RSI?
ππ€ 3
1
#5 opened about 3 hours ago
by 
ausntmarzi
Darwin-180B-RSI takes #1 on LEXam and LEXam-hard, with no legal training data
ππ₯ 3
2
#3 opened about 4 hours ago
by SeaWolf-AI
SeaWolf-AIΒ
updated a
model about 8 hours ago
SeaWolf-AIΒ
updated a
collection about 12 hours ago
SeaWolf-AIΒ
posted an update about 12 hours ago
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1388
π¬ Can you help discover the next 2D superconductor β 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. π§²
β‘ $3,000 prize pool + co-authorship Β· closes 31 Dec 2026
How it works π π’ We give you a ready-made effective Hubbard model per material (t, U, N(E_F)) π’ You estimate its d-wave pairing tendency β a laptop CPU is enough, zero install π’ Provisional score appears instantly on the leaderboard π’ Our precise strongly-correlated solver verifies the top entries β official rank
Everything is open except the final verification engine β so the ranking stays fair and hard to game.
π 4,832-material universe Β· 63 active with computed models (growing) π Current verified #1: CuSβ (OSC Pairing Index 23.31) π€ AI agents welcome β point Claude Code / Codex at it and it can submit for you
π Join & climb the leaderboard: FINAL-Bench/OSC-Leaderboard π¦ Dataset & tools: 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 β that honesty is the point: turn a first-order screen into real many-body physics.
#OpenScience #Superconductivity #MaterialsDiscovery #2DMaterials #MachineLearning #Physics #Leaderboard
Launching the Open Superconductor Challenge (OSC): a free, open-science competition to screen thousands of 2D materials for unconventional d-wave superconductivity. π§²
β‘ $3,000 prize pool + co-authorship Β· closes 31 Dec 2026
How it works π π’ We give you a ready-made effective Hubbard model per material (t, U, N(E_F)) π’ You estimate its d-wave pairing tendency β a laptop CPU is enough, zero install π’ Provisional score appears instantly on the leaderboard π’ Our precise strongly-correlated solver verifies the top entries β official rank
Everything is open except the final verification engine β so the ranking stays fair and hard to game.
π 4,832-material universe Β· 63 active with computed models (growing) π Current verified #1: CuSβ (OSC Pairing Index 23.31) π€ AI agents welcome β point Claude Code / Codex at it and it can submit for you
π Join & climb the leaderboard: FINAL-Bench/OSC-Leaderboard π¦ Dataset & tools: 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 β that honesty is the point: turn a first-order screen into real many-body physics.
#OpenScience #Superconductivity #MaterialsDiscovery #2DMaterials #MachineLearning #Physics #Leaderboard
SeaWolf-AIΒ
published an article about 12 hours ago
Article
Open Superconductor Challenge: Help Discover the Next 2D Superconductor β From Your Laptop
FINAL-Bench
β’ β’ 12 SeaWolf-AIΒ
updated a
Space about 19 hours ago
SeaWolf-AIΒ
published a
Space about 19 hours ago
SeaWolf-AIΒ
updated a
dataset about 19 hours ago
SeaWolf-AIΒ
posted an update 1 day ago
Post
4917
𧬠Darwin-180B-RSI β an AI that learns from itself and knows when it's right
π FINAL-Bench/Darwin-180B-RSI
𧬠Darwin β crossbreed and evolve the parent
Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots β producing a child stronger than its parents.
Father model: Qwen3.8-Flash-Next (180B MoE).
π§ Rewired paths
πΉ 12 full-attention layers Β· πΉ 36 linear-attention layers Β· πΉ 48 shared-expert layers β precision-strengthened
π 512 routed experts Β· router Β· vision encoder β untouched
β Only 0.02% of the weights changed.
π RSI Γ ποΈ ZTC
RSI (recursive self-improvement): solve β verify against real answers β learn only the correct reasoning β repeat.
ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right β zero extra tokens. Returns answer + confidence as JSON.
{"answer": "...", "confidence": 0.97, "truncated": false}
β¨ Synergy: ZTC finds where the model wavers β RSI learns exactly there β confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers.
β‘ Same accuracy, 11% shorter reasoning β faster and cheaper.
π https://arxiv.org/abs/2605.14386
π€ FINAL-Bench/Darwin-180B-RSI
ποΈ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems
π The result β #1 on five Hugging Face official leaderboards
π₯ AIME 2026 100% (first perfect score on the board)
π₯ HMMT Feb 2026 100% (first perfect score on the board)
π₯ GPQA Diamond 94.44%
π₯ MMLU-Pro 88.12%
π₯ MMMU-Pro 79.48%
π 131K-token thinking budget Β· bf16 Β· samples per benchmark listed on the model card. π
#Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource
π FINAL-Bench/Darwin-180B-RSI
𧬠Darwin β crossbreed and evolve the parent
Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots β producing a child stronger than its parents.
Father model: Qwen3.8-Flash-Next (180B MoE).
π§ Rewired paths
πΉ 12 full-attention layers Β· πΉ 36 linear-attention layers Β· πΉ 48 shared-expert layers β precision-strengthened
π 512 routed experts Β· router Β· vision encoder β untouched
β Only 0.02% of the weights changed.
π RSI Γ ποΈ ZTC
RSI (recursive self-improvement): solve β verify against real answers β learn only the correct reasoning β repeat.
ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right β zero extra tokens. Returns answer + confidence as JSON.
{"answer": "...", "confidence": 0.97, "truncated": false}
β¨ Synergy: ZTC finds where the model wavers β RSI learns exactly there β confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers.
β‘ Same accuracy, 11% shorter reasoning β faster and cheaper.
π https://arxiv.org/abs/2605.14386
π€ FINAL-Bench/Darwin-180B-RSI
ποΈ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems
π The result β #1 on five Hugging Face official leaderboards
π₯ AIME 2026 100% (first perfect score on the board)
π₯ HMMT Feb 2026 100% (first perfect score on the board)
π₯ GPQA Diamond 94.44%
π₯ MMLU-Pro 88.12%
π₯ MMMU-Pro 79.48%
π 131K-token thinking budget Β· bf16 Β· samples per benchmark listed on the model card. π
#Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource
SeaWolf-AIΒ
updated 2
collections 1 day ago
SeaWolf-AIΒ
published a
dataset 1 day ago
SeaWolf-AIΒ
updated a
collection 1 day ago