8 citations · 12 across the 8 of their papers we have counts for
6 papers · 1 filter
P: Joint Program-and-Proof Planning for Verified Code Generation
Zenan Li, Ziran Yang, Peiyang Song +2
Verified code generation asks a large language model (LLM) to generate both an executable program and a machine-checkable proof that the program meets a formal specification, promi…
Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry
Zhaoyu Li, Hangrui Bi, Youyuan Zhang +5
Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation. Yet,…
Learning to Disprove: Formal Counterexample Generation with Large Language Models
Zenan Li, Zhaoyu Li, Kaiyu Yang +2
Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. Howeve…
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
Zenan Li, Zhaoyu Li, Wen Tang +6
Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tact…
Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean
Peiyang Song, Kaiyu Yang, Anima Anandkumar
Neural theorem proving combines large language models (LLMs) with proof assistants such as Lean, where the correctness of formal proofs can be rigorously verified, leaving no room…
A Survey on Deep Learning for Theorem Proving
Zhaoyu Li, Jialiang Sun, Logan Murphy +5
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems. In recent years, the advance…