FINAL-Bench / Darwin-180B-RSI

Darwin-180B-RSI

180B Mixture-of-Experts · vision-language · #1 on five Hugging Face official leaderboards — AIME 2026 100 · HMMT Feb 2026 100 · GPQA Diamond 94.44 · MMLU-Pro 88.12 · MMMU-Pro 79.48 · self-improving

reasoning · MoE 512 experts · 262K long context · image + text · Korean + English · self-improvement · ZTC

The newest flagship of the Darwin family — #1 on AIME 2026, HMMT Feb 2026, GPQA Diamond, MMLU-Pro and MMMU-Pro, and a model that gets better by learning from its own verified work.

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🏆 Five #1s — head-to-head with Chinese frontier models

Scores as listed on the Hugging Face official benchmark leaderboards (self-reported by each model's publisher). 🥇 = #1 on that leaderboard. "—" = not reported.

_{Leaderboard values are each publisher's own reported numbers; settings (samples, voting, thinking budget) differ across models. Darwin-180B-RSI settings are listed in the evaluation protocol below.}

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🧬 The Darwin Family

Darwin is VIDRAFT 's measurement-driven reasoning model family — 50+ official models , 400+ community derivatives , and now two places in the GPQA Diamond top 3 (Darwin-180B-RSI #1 · Darwin-397B-ZTC #3).

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🧬 Darwin — evolve the parent, keep what works

Darwin treats a strong open model as a parent . It measures where the parent is weak, and strengthens exactly those parts — instead of re-training everything and risking what already works.

• Diagnose before you change. Every Darwin generation starts from a measured weakness map of the parent.
• Change little, precisely. Darwin modifies a small, targeted fraction of the network. Knowledge stored in the experts is preserved.
• Proven capability over new guesses. Earlier Darwin generations grafted the best-performing expert/FFN blocks from other strong models onto a base backbone; Darwin-180B-RSI adds a new ingredient — the model's own verified work .
• Measured, not claimed. Every change must beat the parent on held-out tests before it ships.

Lineage

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📄 Darwin Platform & Research

• Darwin Family — MRI trust-weighted evolutionary merging for training-free scaling of language-model reasoning ( arXiv:2605.14386 )
• Placement Is Free, Composition Is Not — the Latin square as a provably-balanced construction for heterogeneous sequence-mixer stacks ( 2609.20269 ) — the AETHER architecture line
• FINAL Bench — VIDRAFT's measurement-driven evaluation framework (SSRN)
• Four-layer Pre-AGI roadmap — Darwin → AETHER → PROMETHEUS → HEPHAESTUS
• Collections: Darwin Family · ZTC Models — JEV ecosystems

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🔁 RSI — a model that improves from its own work

Recursive self-improvement (RSI) is the core of this generation. Instead of distilling a bigger teacher, the model improves by learning from itself:

1. Solve — the model works through practice problems it has never seen in evaluation.
2. Verify — its answers are checked against verifiable references (answer keys, executable checks). Nothing unverified is learned.
3. Learn — it is re-trained on the reasoning that turned out to be correct.
4. Repeat — the improved model becomes the next solver.

What it bought in this release:

Same or better accuracy with shorter reasoning — cheaper and faster to serve. Practice sets are deduplicated against every evaluation set we report (8-gram overlap filter).

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🏛️ ZTC — it knows before it answers

Zero-Token Confidence (ZTC) reads the model's own internal state once, before generation , and returns the probability that the answer it is about to give is correct — no extra tokens, no second model.

{"answer": "...", "confidence": 0.93, "ztc_score": 1.84, "truncated": false}

Use it to gate actions: when confidence is low, do not call the tool, escalate, or answer "I don't know".

What ships with this model

from handler import EndpointHandler
h = EndpointHandler("FINAL-Bench/Darwin-180B-RSI")   # local snapshot path
print(h({"inputs": "What is 17 * 23?"}))
# [{"answer": "...391...", "confidence": 0.97, "ztc_score": 2.1, "truncated": false}]

Same format as Darwin-397B-ZTC . The readout is fitted only on practice data that is disjoint from every benchmark reported here.

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🏆 Results

Evaluation protocol

Common to every benchmark

Per benchmark

All numbers are self-measured and reproducible with the settings above. Majority-vote scores are system scores (several samples per question) and are labeled as such.

MMLU-Pro by category (single sample) — strongest in math 95.0 · biology 94.6 · physics 92.5; room to grow in law and history.

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⚙️ Specifications

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🚀 Quickstart

Serving with vLLM (8 × B200 or equivalent)

vllm serve FINAL-Bench/Darwin-180B-RSI \
  --tensor-parallel-size 8 --enable-expert-parallel \
  --max-model-len 135168 --trust-remote-code

Chat Completions (OpenAI-compatible)

from openai import OpenAI
c = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
r = c.chat.completions.create(model="FINAL-Bench/Darwin-180B-RSI",
    messages=[{"role": "user", "content": "Explain why the sky is blue in two sentences."}],
    temperature=1.0, top_p=0.95, extra_body={"top_k": 20})
print(r.choices[0].message.content)

Transformers

from transformers import AutoProcessor, AutoModelForImageTextToText
model_id = "FINAL-Bench/Darwin-180B-RSI"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

Tip: this is a thinking model. Give it room — a thinking budget of 32K–131K tokens is recommended for hard reasoning. Short budgets truncate the reasoning and cost accuracy.

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⚠️ Limitations and disclosure

• Scores are self-measured with the settings stated in the Results table; majority-vote numbers use several samples per question.
• Very long reasoning is normal for hard problems; a short thinking budget will truncate answers and lower accuracy.
• Like every LLM, the model can be confidently wrong — use the ZTC confidence readout to gate high-stakes actions.

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🔗 Related Darwin Models

• Darwin-397B-ZTC — 397B MoE (FP8), GPQA Diamond 93.43 %, ZTC on board
• Darwin-28B-REASON — 28B, GPQA Diamond 89.39 %
• Darwin-36B-Opus — 36B MoE, GPQA Diamond 88.4 %
• Darwin-27B-RSI — 27B, the first Darwin RSI model
• Darwin-9B-NEG — 9B with Negentropy distillation, GPQA Diamond 84.3 %
• ZTC-Judge-27B — standalone ZTC judge

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📚 Citation

@misc{darwin180b_rsi_2026,
  title  = {Darwin-180B-RSI: Recursive Self-Improvement on Verified Answers for a 180B Mixture-of-Experts Reasoning Model},
  author = {FINAL-Bench / Darwin Research Team},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-180B-RSI}},
  note   = {GPQA Diamond 94.44 \% · MMLU-Pro 88.12 \%}
}

@misc{darwin_family_2026,
  title  = {Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning},
  author = {Kim, Taebong and Hong, Youngsik and Kim, Minsik and Choi, Sunyoung and Jang, Jaewon and Shin, Junghoon},
  year   = {2026},
  eprint = {2605.14386},
  archivePrefix = {arXiv}
}

@misc{latin_square_2026,
  title  = {Placement Is Free, Composition Is Not: The Latin Square as a Provably-Balanced Construction for Heterogeneous Sequence-Mixer Stacks},
  author = {Kim, Taebong and Hong, Youngsik and Kim, Minsik and Choi, Sunyoung and Jang, Jaewon and Kim, Minseo},
  year   = {2026},
  eprint = {2609.20269},
  archivePrefix = {arXiv}
}

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📜 License

Darwin-180B-RSI is a derivative of Qwen3.8-Flash-Next and is distributed under the Qwen Community License 1.0 (see LICENSE ).

🏢 About

Built by VIDRAFT · evaluated with FINAL-Bench .

This model is part of the Darwin Family .