PhysicalEye-Decide-35B
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One-pass calibrated decisions on images
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🏆 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. 🙌
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. 🙌
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💻 Data-center AI, now on a laptop: POCKET-Darwin-180B
We're releasing a 4-bit GGUF build of Darwin-180B-RSI, #1 on seven official Hugging Face leaderboards (self-reported), that runs without a GPU.
📦 360 GB → 111 GB (4-bit GGUF, 4 files)
🖥️ No GPU: one server CPU (16 threads) at 18.4–21.0 tokens/s
💻 RTX 5060 laptop (8 GB VRAM) + 32 GB RAM: 4.17 tokens/s
🧊 128 GB mini PC: whole model in memory, no GPU needed
🎯 MMLU-Pro, 2,000 questions, paired: original 87.65% = 4-bit 87.65%
How?
· Only ~3B of 180B parameters are active per token (10 of 512 experts)
· llama.cpp streams just the needed experts from SSD, so 32 GB RAM is enough
· Graft quantization: we took the proven Unsloth UD-Q4_K_XL base build and swapped in only the 300 tensors our RSI training changed (300/300 verified)
Under the hood is Model-level Recursive Self-Improvement. The model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces.
Built for teams that can't send data to an external cloud (defense, finance, public sector) to run a top-tier model fully offline.
📝 Article: https://huggingface.co/blog/FINAL-Bench/data-center-ai-now-on-a-laptop-pocket-darwin-180b
🤗 Model: FINAL-Bench/POCKET-Darwin-180B-GGUF
🧬 Original: FINAL-Bench/Darwin-180B-RSI
#Darwin #RSI #GGUF #llamacpp #OnDevice #MoE
We're releasing a 4-bit GGUF build of Darwin-180B-RSI, #1 on seven official Hugging Face leaderboards (self-reported), that runs without a GPU.
📦 360 GB → 111 GB (4-bit GGUF, 4 files)
🖥️ No GPU: one server CPU (16 threads) at 18.4–21.0 tokens/s
💻 RTX 5060 laptop (8 GB VRAM) + 32 GB RAM: 4.17 tokens/s
🧊 128 GB mini PC: whole model in memory, no GPU needed
🎯 MMLU-Pro, 2,000 questions, paired: original 87.65% = 4-bit 87.65%
How?
· Only ~3B of 180B parameters are active per token (10 of 512 experts)
· llama.cpp streams just the needed experts from SSD, so 32 GB RAM is enough
· Graft quantization: we took the proven Unsloth UD-Q4_K_XL base build and swapped in only the 300 tensors our RSI training changed (300/300 verified)
Under the hood is Model-level Recursive Self-Improvement. The model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces.
Built for teams that can't send data to an external cloud (defense, finance, public sector) to run a top-tier model fully offline.
📝 Article: https://huggingface.co/blog/FINAL-Bench/data-center-ai-now-on-a-laptop-pocket-darwin-180b
🤗 Model: FINAL-Bench/POCKET-Darwin-180B-GGUF
🧬 Original: FINAL-Bench/Darwin-180B-RSI
#Darwin #RSI #GGUF #llamacpp #OnDevice #MoE
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🧬 Darwin-180B-RSI — an AI that learns from itself and knows when it's right
👉 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
🤗 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
👉 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
🤗 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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