ProofSketch: Efficient Verified Reasoning for Large Language Models

Disha Sheshanarayana, Tanishka Magar

Keywords: LLMs, Efficient Reasoning, Token Optimization, Verification

TL;DR: ProofSketch: short verified sketches vs long CoT. 37-71% token savings, 42-84% formal verification.

Abstract: Reasoning methods such as chain-of-thought prompting and self-consistency have shown immense potential to improve the accuracy of large language models across various reasoning tasks. However such methods involve generation of lengthy reasoning chains, which substantially increases token consumption, computational cost, and latency. To address this inefficiency, we propose ProofSketch, a verification-guided reasoning framework that integrates symbolic closure computation, lexicographic verification and adaptive sketch generation. Our experiments show that ProofSketch consistently reduces token usage while improving accuracy, demonstrating that this approach offers a promising path for efficient and trustworthy reasoning.

Submission Number: 295