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AI agents refactor Lean proofs under $3 cap, using tiered models

By PulseAugur Editorial · [1 sources] ·

A research paper details a novel approach to refactoring Lean proofs using enterprise agent practices, achieving a score of 86.31 on a closed-track competition. The method employed a cost-effective strategy, utilizing different AI models like GPT-6.1 Sol, GPT-6.1 Terra, and Gemini 4 Argon based on their performance and cost per step, all while staying under a US$3 cap per problem. The entry also incorporated compiler-checked programmatic rewrite rules and rigorous call recording for review, which significantly improved the proof quality. AI

IMPACT Demonstrates a cost-effective approach to AI-assisted formal verification, potentially lowering barriers for complex software development.

RANK_REASON Research paper detailing a novel methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents refactor Lean proofs under $3 cap, using tiered models

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36 / 100
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Research paper detailing a novel methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bobber Cheng ·

    Compile-Gated Lean Proof Refactoring with Enterprise Agent Practices: A Model per Step under a US\$3 Cap, Followed by Programmatic Rewrite Rules

    arXiv:2611.13011v1 Announce Type: cross Abstract: Our closed-track entry (Arena username Vexorium-Agent) scored 86.31 points on the final board (L = 77.07, H = 81.86, Z = 100) over the 60 final problems, against 33.33 for the reference proofs, 8th at the deadline (top: 92.57), fo…

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