meituan-longcat

meituan-longcat / LongCat-Flash-Prover

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A flagship 560-billion-parameter open-source MoE model that advances Native Formal Reasoning in Lean4 through agentic tool-integrated reasoning.

19
2
100% credibility
Found Mar 21, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

LongCat-Flash-Prover is an open-source AI model expert at turning natural language math problems into verified formal proofs using Lean4.

How It Works

1
🔍 Discover LongCat

While hunting for help with tough math proofs, you find LongCat-Flash-Prover, an AI whiz from a big team.

2
📖 Check It Out

You read the friendly page with cool charts showing it crushes hard math challenges better than others.

3
🌟 Wow, Top Scores!

Get thrilled seeing it solve over 70% of pro-level proof problems that trip up everyone else.

4
💬 Start a Chat

Hop over to the LongCat chat site and type in your tricky math puzzle or theorem to prove.

5
🧠 AI Dives In

Watch the AI think step-by-step: it turns your words into exact math, sketches ideas, and builds the proof.

6
✅ Proof Delivered

You get a complete, double-checked proof that's ready to use and verify.

🎉 Math Mastered

Now you breeze through advanced theorems with your new trusty proof buddy by your side.

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Star Growth

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AI-Generated Review

What is LongCat-Flash-Prover?

LongCat-Flash-Prover is a flagship 560-billion-parameter open-source MoE model that advances native formal reasoning in Lean4 through agentic tool-integrated reasoning. It handles auto-formalization of informal math problems into verified Lean4 statements, lemma-style sketching, and full theorem proving, all via a chat template in Hugging Face Transformers. Developers get a ready-to-load model for tackling complex proofs with tool calls like syntax checks and consistency verification.

Why is it gaining traction?

It crushes benchmarks—97.1% pass rate on MiniF2F-Test with just 72 inferences per problem, 70.8% on ProverBench, and 41.5% on PutnamBench—outpacing other open-weights models in sample efficiency. The agentic workflow with interleaved thinking and tool integration makes formal proving feel natural, while retaining solid general reasoning scores. Early adopters hook on its Lean4-native capabilities without custom architectures.

Who should use this?

Lean4 theorem provers verifying math libraries, AI researchers training on formal tasks, or math competition solvers automating Putnam-style problems. Ideal for teams needing agentic proving pipelines with verifiable feedback loops, not casual chatbots.

Verdict

Promising for Lean4 formalists despite 19 stars and 1.0% credibility score—docs are benchmark-heavy but deployment guides exist for vLLM/SGLang. Test it on your proofs before committing; maturity lags behind claims.

(178 words)

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