physical ai
masterset-ai
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https://www.masterset.ai
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liked a Space 27 minutes ago
FINAL-Bench/ONGRID reacted to SeaWolf-AI's post with ❤️ about 4 hours ago
🏆 Darwin-27B-ZTC-v2 just took #1 on the System One Mosaic Benchmark (S1MB).
S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score.
📊 Top of the board
🥇 Darwin ZTC v2 (FINAL-Bench) 89.58
🥈 OpenJev-27B 87.50
🥉 AutoJev-27B 87.07
4️⃣ Eikos 27B 85.43
5️⃣ Jev 1.13 85.05
🔎 Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline.
⚙️ Why a zero-token judge wins here
🔹 It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass.
🔹 Zero generated tokens, no decoding loop, so latency and cost stay low.
🔹 Holds up out of distribution too: General Noul 96.00, General Choice 99.34.
It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call.
🔗 Model: https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: https://huggingface.co/spaces/hotchpotch/S1MB-leaderboard
Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌
reacted to SeaWolf-AI's post with 🤗 about 4 hours ago
🏆 Darwin-27B-ZTC-v2 just took #1 on the System One Mosaic Benchmark (S1MB).
S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score.
📊 Top of the board
🥇 Darwin ZTC v2 (FINAL-Bench) 89.58
🥈 OpenJev-27B 87.50
🥉 AutoJev-27B 87.07
4️⃣ Eikos 27B 85.43
5️⃣ Jev 1.13 85.05
🔎 Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline.
⚙️ Why a zero-token judge wins here
🔹 It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass.
🔹 Zero generated tokens, no decoding loop, so latency and cost stay low.
🔹 Holds up out of distribution too: General Noul 96.00, General Choice 99.34.
It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call.
🔗 Model: https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: https://huggingface.co/spaces/hotchpotch/S1MB-leaderboard
Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌
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