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daVinci-Prover enhances formal theorem proving with agent-native training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed daVinci-Prover, a new harness for formal theorem proving that interacts with a Lean verifier. This system utilizes a 33.7 billion token mid-training corpus focused on decomposition, search, self-verification, and revision, along with agentic trajectories. Applying this method to the Qwen3.5 series resulted in significant performance improvements across theorem-proving benchmarks, notably achieving near-saturating performance on MiniF2F. AI

IMPACT This research could accelerate the development of more capable AI agents for complex mathematical reasoning and formal verification tasks.

RANK_REASON The cluster describes a new research paper detailing a novel method and harness for formal theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

daVinci-Prover enhances formal theorem proving with agent-native training

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The cluster describes a new research paper detailing a novel method and harness for formal theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    daVinci-Prover: Agent-Native Mid-Training for Formal Theorem Proving

    Advances in large language models (LLMs) are rapidly transforming formal theorem proving. Yet further progress remains constrained by the lack of a transparent and effective data recipe for systematically improving theorem-proving models. In this work, we argue that effective age…

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