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Proving Theorems Recursively

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arxiv 2405.14414 v1 pith:5UGHMNM4 submitted 2024-05-23 cs.AI

classification cs.AI
keywords poetrytheoremlevelproofproofsprovingtheoremsapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in automated theorem proving leverages language models to explore expanded search spaces by step-by-step proof generation. However, such approaches are usually based on short-sighted heuristics (e.g., log probability or value function scores) that potentially lead to suboptimal or even distracting subgoals, preventing us from finding longer proofs. To address this challenge, we propose POETRY (PrOvE Theorems RecursivelY), which proves theorems in a recursive, level-by-level manner in the Isabelle theorem prover. Unlike previous step-by-step methods, POETRY searches for a verifiable sketch of the proof at each level and focuses on solving the current level's theorem or conjecture. Detailed proofs of intermediate conjectures within the sketch are temporarily replaced by a placeholder tactic called sorry, deferring their proofs to subsequent levels. This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels. Experiments are conducted on the miniF2F and PISA datasets and significant performance gains are observed in our POETRY approach over state-of-the-art methods. POETRY on miniF2F achieves an average proving success rate improvement of 5.1%. Moreover, we observe a substantial increase in the maximum proof length found by POETRY, from 10 to 26.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models

    cs.AI 2025-06 conditional novelty 6.0 of 10

    An inference-only neuro-symbolic pipeline, DSP+, solves 80.7% of miniF2F and the previously unsolved imo_2019_p1, matching heavily RL-trained theorem provers without fine-tuning.

  2. MATP-BENCH: Can MLLM Be a Good Automated Theorem Prover for Multimodal Problems?

    cs.CL 2025-06 conditional novelty 6.0 of 10

    MATP-BENCH pairs 1,056 multimodal math problems with formal theorem statements in Lean 4, Coq, and Isabelle; the strongest tested model solves only 5.68% of Lean 4 end-to-end proving tasks at pass@10.

  3. Solving Formal Math Problems by Decomposition and Iterative Reflection

    cs.AI 2025-07 conditional novelty 5.0 of 10

    An agent that decomposes Lean 4 goals into subproblems and iteratively repairs proofs achieves a 95.9% pass rate on miniF2F-test using a stock Gemini model.

  4. Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

    cs.CL 2025-05 reject novelty 5.0 of 10

    A grammar-based model of LLM-generated SMT-LIB code produces uncertainty signals that predict formalization errors on some reasoning tasks, with fused signals giving large error reductions only in an in-sample evaluation.

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