Meituan LongCat Unveils Open-Source Theorem Prover Model LongCat-Flash-Prover
On March 24, 2026, the Meituan LongCat team officially open-sourced a specialized deep learning model for mathematical formalization and theorem proving: LongCat-Flash-Prover. This model overcomes the limitations of large language models in rigorous logical reasoning by breaking down formal reasoning into three core capabilities: auto-formalization, proof sketching, and final proving. It represents a paradigm shift from "probabilistic answer prediction" to "verifiable logical proof."

Using a combined Tool-Integrated Reasoning (TIR) strategy, the model achieved a 97.1% pass rate on the MiniF2F-Test benchmark with only 72 reasoning steps, setting a new state-of-the-art record for open-source theorem provers. Its performance also significantly exceeded existing open-source models on challenging competition-level benchmarks like MathOlympiad-Bench and PutnamBench.

Technically, LongCat-Flash-Prover employs a TIR-based "hybrid expert iteration" framework. By integrating Lean4Server verification, semantic and theorem consistency checks, and legality verification against nine types of cheating behaviors, the model effectively addresses logical loopholes and code deception. During training, the team introduced a hierarchical masking strategy and token-level staleness control, greatly improving reinforcement learning stability under the Mixture-of-Experts (MoE) architecture.
As AI reasoning evolves from handling natural language ambiguity to working with verifiable formal languages, such theorem provers are moving beyond simple algorithmic benchmarks. They are becoming foundational infrastructure for core scientific research. This breakthrough signals an accelerating era where AI deeply participates in frontier mathematical exploration and automated document verification.
GitHub:
https://github.com/meituan-longcat/LongCat-Flash-Prover
Hugging Face:https://huggingface.co/meituan-longcat/LongCat-Flash-Prover
Report:
https://github.com/meituan-longcat/LongCat-Flash-Prover/blob/main/LongCat_Flash_Prover_Technical_Report.pdf
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On March 24, 2026, the Meituan LongCat team officially open-sourced a specialized deep learning model for mathematical formalization and theorem proving: LongCat-Flash-Prover. This model overcomes the limitations of large language models in rigorous logical reasoning by breaking down formal reasoning into three core capabilities: auto-formalization, proof sketching, and final proving. It represents a paradigm shift from "probabilistic answer prediction" to "verifiable logical proof."

Using a combined Tool-Integrated Reasoning (TIR) strategy, the model achieved a 97.1% pass rate on the MiniF2F-Test benchmark with only 72 reasoning steps, setting a new state-of-the-art record for open-source theorem provers. Its performance also significantly exceeded existing open-source models on challenging competition-level benchmarks like MathOlympiad-Bench and PutnamBench.

Technically, LongCat-Flash-Prover employs a TIR-based "hybrid expert iteration" framework. By integrating Lean4Server verification, semantic and theorem consistency checks, and legality verification against nine types of cheating behaviors, the model effectively addresses logical loopholes and code deception. During training, the team introduced a hierarchical masking strategy and token-level staleness control, greatly improving reinforcement learning stability under the Mixture-of-Experts (MoE) architecture.
As AI reasoning evolves from handling natural language ambiguity to working with verifiable formal languages, such theorem provers are moving beyond simple algorithmic benchmarks. They are becoming foundational infrastructure for core scientific research. This breakthrough signals an accelerating era where AI deeply participates in frontier mathematical exploration and automated document verification.
GitHub:
https://github.com/meituan-longcat/LongCat-Flash-Prover
Hugging Face:https://huggingface.co/meituan-longcat/LongCat-Flash-Prover
Report:
https://github.com/meituan-longcat/LongCat-Flash-Prover/blob/main/LongCat_Flash_Prover_Technical_Report.pdf
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