OSC Open Superconductor Challenge
π§²
32
Estimate superconducting pairing and submit your score
raxisvictory
raxisvictory
AI & ML interests
None yet
Recent Activity
liked a Space about 11 hours ago
FINAL-Bench/OSC-Leaderboard reacted to SeaWolf-AI's post with π₯ about 11 hours ago
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: https://huggingface.co/spaces/FINAL-Bench/OSC-Leaderboard π¦ 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 β that honesty is the point: turn a first-order screen into real many-body physics.
#OpenScience #Superconductivity #MaterialsDiscovery #2DMaterials #MachineLearning #Physics #Leaderboard liked a model 1 day ago
FINAL-Bench/Darwin-180B-RSIOrganizations
None yet
liked a
Space about 11 hours ago
reacted to SeaWolf-AI's post with π₯ about 11 hours ago
Post
1287
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
liked a
model 1 day ago
liked 2
models 4 days ago
liked a
Space 7 days ago
reacted to SeaWolf-AI's post with π 7 days ago
Post
3398
The cost of a judging gate is usually quoted as a number. This puts it on a Tetris board.
Three boards get the same piece order, and on every move the same proposal and the same noise β a paired comparison. The gate decides one thing: keep this move, or draw again. Each board gets the same 60 seconds of gate time.
The text-writing gates get through 15β22 moves. The generation-free gate gets through 40β50. The boards that stop simply run out of clock.
It does not win on accuracy: on the same 2,018-question LODO set, JEV scores AUC 0.7350 against ZTC-Judge-27B's 0.7289. The separation is elsewhere. Clock β 2.1 s vs 0.0615 s per call, and on a 200-candidate agent screen one judging call measured 3.206 s generative vs 0.033 s readout, same server. Calibration β a gate is a threshold, and at ECE 0.4985 (vs ZTC 0.0245) a threshold stops carrying information. Mechanism β a text judge can name option 42 when there is no option 42; a scoring readout cannot. Not a lower error rate. No path.
The curve in the ZTC panel is real online fitting, scored prequentially β predict first, learn after β with base weights untouched. Not recursive self-improvement.
Limits, also stated on the page: Laya's AUC and latency are not our measurements and are set equal to JEV's, so calibration is the only measured axis it differs on. The page is a simulation driven by measured constants.
KO / EN / ZH.
FINAL-Bench/Tetris-JEV-LAYA-ZTC
FINAL-Bench/ZTC-Judge-27B
Three boards get the same piece order, and on every move the same proposal and the same noise β a paired comparison. The gate decides one thing: keep this move, or draw again. Each board gets the same 60 seconds of gate time.
The text-writing gates get through 15β22 moves. The generation-free gate gets through 40β50. The boards that stop simply run out of clock.
It does not win on accuracy: on the same 2,018-question LODO set, JEV scores AUC 0.7350 against ZTC-Judge-27B's 0.7289. The separation is elsewhere. Clock β 2.1 s vs 0.0615 s per call, and on a 200-candidate agent screen one judging call measured 3.206 s generative vs 0.033 s readout, same server. Calibration β a gate is a threshold, and at ECE 0.4985 (vs ZTC 0.0245) a threshold stops carrying information. Mechanism β a text judge can name option 42 when there is no option 42; a scoring readout cannot. Not a lower error rate. No path.
The curve in the ZTC panel is real online fitting, scored prequentially β predict first, learn after β with base weights untouched. Not recursive self-improvement.
Limits, also stated on the page: Laya's AUC and latency are not our measurements and are set equal to JEV's, so calibration is the only measured axis it differs on. The page is a simulation driven by measured constants.
KO / EN / ZH.
FINAL-Bench/Tetris-JEV-LAYA-ZTC
FINAL-Bench/ZTC-Judge-27B
- 5 replies
liked a
Space 9 days ago
liked 4
models 9 days ago
FINAL-Bench/ZTC-Judge-4B
Text Classification β’ 5B β’ Updated β’ 151 β’ 39
FINAL-Bench/ZTC-Judge-9B
Text Classification β’ 10B β’ Updated β’ 2.42k β’ 38
FINAL-Bench/ZTC-Judge-27B
Text Classification β’ 28B β’ Updated β’ 425 β’ 48
FINAL-Bench/Darwin-397B-ZTC
Text Generation β’ 404B β’ Updated β’ 1.31k β’ 39
upvoted 5
collections 9 days ago
liked 2
Spaces 9 days ago
liked a
model 10 days ago
