Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal Problem-Solving
Qi Liu, Xinhao Zheng, Renqiu Xia, Xingzhi Qi, Qinxiang Cao, Junchi Yan

TL;DR
This paper introduces a formal framework for problem-solving using theorem proving environments, creating benchmarks and verification methods to improve process transparency and alignment with human reasoning.
Contribution
It proposes a new formal problem-solving framework, FPS, and its deductive variant D-FPS, along with benchmarks and a verification method, RPE, to advance AI problem-solving research.
Findings
Frameworks are proven to be sound and complete.
Constructed three new problem-solving benchmarks.
Baseline models solve up to 27.47% of benchmark problems.
Abstract
As a seemingly self-explanatory task, problem-solving has been a significant component of science and engineering. However, a general yet concrete formulation of problem-solving itself is missing. With the recent development of AI-based problem-solving agents, the demand for process-level verifiability is rapidly increasing yet underexplored. To fill these gaps, we present a principled formulation of problem-solving as a deterministic Markov decision process; a novel framework, FPS (Formal Problem-Solving), which utilizes existing FTP (formal theorem proving) environments to perform process-verified problem-solving; and D-FPS (Deductive FPS), decoupling solving and answer verification for better human-alignment. The expressiveness, soundness and completeness of the frameworks are proven. We construct three benchmarks on problem-solving: FormalMath500, a formalization of a subset of the…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Explainable Artificial Intelligence (XAI) · Machine Learning in Materials Science
