Understanding Tool-Augmented Agents for Lean Formalization: A Factorial Analysis
Automatic translation of natural language mathematics into faithful Lean 4 code is hindered by the fundamental dissonance between informal set-theoretic intuition and strict formal type theory. This gap often causes LLMs to hallucinate non-existent library definitions, resulting in code that fails to compile or lacks semantic fidelity. In this work, we investigate the effectiveness of tool-augmented agents for this task through a systematic factorial analysis of three distinct tool categories: Fine-tuned Model Querying (accessing expert drafts), Knowledge Search (retrieving symbol definitions), and Compiler Feedback (verifying code via a Lean REPL). We first benchmark the agent against on...
| Authors | Ke Zhang, Patricio Gallardo, Maziar Raissi et al. |
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| Primary category | cs.SE |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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