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Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4
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Machine-assisted theorem proving refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models in conjunction with proof assistants to perform this task. In this paper, we introduce Pantograph, a tool that provides a versatile interface to the Lean 4 proof assistant and enables efficient proof search via powerful search algorithms such as Monte Carlo Tree Search. In addition, Pantograph enables high-level reasoning by enabling a more robust handling of Lean 4's inference steps. We provide an overview of Pantograph's architecture and features. We also report on an illustrative use case: using machine learning models and proof sketches to prove Lean 4 theorems. Pantograph's innovative features pave the way for more advanced machine learning models to perform complex proof searches and high-level reasoning, equipping future researchers to design more versatile and powerful theorem provers.
Forward citations
Cited by 3 Pith papers
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Nazrin: An Atomic Neural Proof Automation Tactic in Lean 4
A finite set of atomic Lean tactics plus a transposing atomization algorithm lets a small graph neural network, Nazrin, be trained on converted proofs and prove held-out formal theorems.
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White-box proof search with factorized Lean 4 goals reaches 18.4% on MiniF2F with Llemma-7B, outperforming black-box generation at 9.6%.
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Reviving DSP for Advanced Theorem Proving in the Era of Reasoning Models
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.
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