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Imitation learning boosts automated theorem proving performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to automated theorem proving by framing the construction of connection-tableaux as a policy within a transition system. This method utilizes a graph neural network trained via imitation learning from existing proofs to score proof edits. When tested on datasets like M2k, MPTP2078-bushy, and TPTP v9.2.1, these learned policies demonstrated a significant improvement, solving up to 46% more problems and achieving proofs an order of magnitude faster than the leanCoP system within a fixed step budget. AI

IMPACT This research could lead to more efficient and capable automated reasoning systems, potentially impacting fields that rely on formal verification and theorem proving.

RANK_REASON The cluster contains an academic paper detailing a new method for automated theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Imitation learning boosts automated theorem proving performance

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The cluster contains an academic paper detailing a new method for automated theorem proving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Fredrik R{\o}mming, Mantas Bak\v{s}ys, Martin S. Fixman, Sean B. Holden ·

    Imitation Learning for Connection-Tableau Construction

    arXiv:2608.26009v1 Announce Type: cross Abstract: An automated theorem prover builds a proof step by step, choosing at each point what to add and what to remove. We cast this construction as a policy acting in a transition system induced by a formal calculus, which fixes which st…

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