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Imitation Learning for Connection-Tableau Construction

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

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 steps are sound: for clausal connection tableaux, leanCoP-style search and plCoP/rlCoP-style planning then become stateful policies over one interface, and policy-learning methods apply directly. We equip such policies with a graph neural network that scores proof edits from structure that transfers across problems, train it by imitation learning from found proofs, and

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