SeaWolf-AI (VIDRAFT

reacted to their post with 👀❤️🤗👍🚀🔥 about 3 hours ago

Post

255

⚛️ The Open Quantum Challenge is live. Both seasons open today.

Quantum computers are expensive, queued, and reachable by only a few. So we removed the barrier. With a single GPU and a public harness, anyone can contribute to core quantum-computing problems with no quantum hardware at all. That is the point of this challenge: to move quantum research from a handful of labs to everyone.

• Season 1, Quantum simulation: reproduce a target quantum system on a GPU.
• Season 2, QEC decoder: correct errors on coded quantum states.

Answers are held privately and every submission is auto-scored against a frozen ground truth, so the ranking is reproducible and hardware-independent.

🏆 Prize: 2,000 USD total (1,000 USD per season, 🥇600 / 🥈300 / 🥉100)

Getting started takes one step: copy the participation guide from the Space and paste it into Codex or Claude Code, then submit against the harness.

📄 Read the article: https://huggingface.co/blog/FINAL-Bench/the-open-quantum-challenge-quantum-simulation-and
👉 Challenge: FINAL-Bench/open-quantum-challenge

posted an update about 3 hours ago

Post

255

⚛️ The Open Quantum Challenge is live. Both seasons open today.

Quantum computers are expensive, queued, and reachable by only a few. So we removed the barrier. With a single GPU and a public harness, anyone can contribute to core quantum-computing problems with no quantum hardware at all. That is the point of this challenge: to move quantum research from a handful of labs to everyone.

• Season 1, Quantum simulation: reproduce a target quantum system on a GPU.
• Season 2, QEC decoder: correct errors on coded quantum states.

Answers are held privately and every submission is auto-scored against a frozen ground truth, so the ranking is reproducible and hardware-independent.

🏆 Prize: 2,000 USD total (1,000 USD per season, 🥇600 / 🥈300 / 🥉100)

Getting started takes one step: copy the participation guide from the Space and paste it into Codex or Claude Code, then submit against the harness.

📄 Read the article: https://huggingface.co/blog/FINAL-Bench/the-open-quantum-challenge-quantum-simulation-and
👉 Challenge: FINAL-Bench/open-quantum-challenge

reacted to their post with 👀❤️ 2 days ago

Post

4026

🏆 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: FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: hotchpotch/S1MB-leaderboard

Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌

reacted to their post with 🚀👀🤗❤️🔥 2 days ago

Post

4028

🧠 We just released Darwin-27B-ZTC, a judgment engine that reaches a verdict without generating anything.

Most LLMs answer by generating, decoding one token at a time. Darwin-27B-ZTC takes a different route.

⚙️ How it works
🔹 It makes its call in a single forward pass.
🔹 Zero generated tokens, and no decoding loop.
🔹 That keeps latency and cost far below what a generative model needs.

🎯 What it judges
🔹 It handles several question types: free-form correctness (noul), multiple choice (choice), and scoring (score).
🔹 For each one it hands back a calibrated confidence, not just an answer.

📊 How well calibrated (measured)
🔹 KL 0.204, Brier 0.097, so the confidence it reports lines up with what actually happens.
🔹 0.743 accuracy (zero-shot, general split), across 2,000 judgments with zero errors.
🔹 By type: noul 0.847, choice 0.723, score 0.675.
🔹 None of the benchmark's train split went into it. It is pure zero-shot.

🚀 Where it fits
🔹 Grading at scale, model routing, safety gating, anywhere you want a fast decision without paying for generation.

🏆 It currently sits at #1 on the official typed-decisions leaderboard on Hugging Face (0.743 accuracy, zero-shot).

🔗 Links
Model: FINAL-Bench/Darwin-27B-ZTC
Leaderboard: LocalLLaMA/typed-decisions

Curious to hear what you make of the single-pass, no-generation approach. 🙌

2 replies

·

reacted to their post with 👍🤗🚀🔥 2 days ago

Post

4026

🏆 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: FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: hotchpotch/S1MB-leaderboard

Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌

posted an update 2 days ago

Post

4026

🏆 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: FINAL-Bench/Darwin-27B-ZTC-v2
🔗 Leaderboard: hotchpotch/S1MB-leaderboard

Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌

replied to their post 3 days ago

Thanks, your arithmetic checks out: the per-type split on the card does not add up to 0.743. We are recomputing the per-type numbers from the run with saved predictions and will post them here, then correct the card.

On "#1": fair point. It is #1 among the entries submitted to the Hub leaderboard, not above Decider 1's 0.768 in the maintainers' zero-shot table. We will reword it.

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