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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.24273 |
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| _version_ | 1866913128281800704 |
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| author | Requena, Borja Letson, Austin Nowakowski, Krystian Beltran-Ferreiro, Izan Sarra, Leopoldo |
| author_facet | Requena, Borja Letson, Austin Nowakowski, Krystian Beltran-Ferreiro, Izan Sarra, Leopoldo |
| contents | We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core features shared among state-of-the-art systems: iterative proof refinement, library search and context management. We evaluate this agentic approach using qualitatively different benchmarks and compare various frontier language models and design choices. Our results show competitive performance compared to state-of-the-art approaches, while using a significantly simpler architecture and a fraction of their cost. Additionally, we demonstrate consistent advantages of an iterative approach over multiple single-shot generations, especially in terms of sample efficiency and cost effectiveness. The implementation is released open-source as a candidate reference for future research and as an accessible prover for the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_24273 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Minimal Agent for Automated Theorem Proving Requena, Borja Letson, Austin Nowakowski, Krystian Beltran-Ferreiro, Izan Sarra, Leopoldo Artificial Intelligence We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core features shared among state-of-the-art systems: iterative proof refinement, library search and context management. We evaluate this agentic approach using qualitatively different benchmarks and compare various frontier language models and design choices. Our results show competitive performance compared to state-of-the-art approaches, while using a significantly simpler architecture and a fraction of their cost. Additionally, we demonstrate consistent advantages of an iterative approach over multiple single-shot generations, especially in terms of sample efficiency and cost effectiveness. The implementation is released open-source as a candidate reference for future research and as an accessible prover for the community. |
| title | A Minimal Agent for Automated Theorem Proving |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.24273 |