NAME-INVARIANT GRAPH NEURAL REPRESENTATIONS FOR AUTOMATED THEOREM PROVING
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United States of America
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Jan 16, 2025
app pub date -
Jul 14, 2023
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Jul 14, 2023
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Abstract
Systems and techniques that facilitate name-invariant graph neural representations for automated theorem proving are provided. In various embodiments, a system can access a set of first directed acyclic graphs respectively representing a conjecture and a set of axioms. In various aspects, the system can generate, via execution of at least one neural-guided automated theorem prover that independently processes the set of first directed acyclic graphs, a proof for the conjecture. In various instances, the at least one neural-guided automated theorem prover can leverage, for a node representing a non-logical symbol name present in more than one of the set of first directed acyclic graphs, a name-invariant learned embedding based on a second directed acyclic graph that is an aggregation of the set of first directed acyclic graphs.
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