# A Minimal Agent for Automated Theorem Proving

arXiv: https://arxiv.org/abs/2602.24273 · HTML: https://mlsanity.com/paper/2602.24273/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 41 of its 60 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 30 of 41)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://mlsanity.com/paper/2306.15626/) (2023)
- [MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics](https://mlsanity.com/paper/2109.00110/) (2021)
- [Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs](https://mlsanity.com/paper/2210.12283/) (2022)
- [DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition](https://mlsanity.com/paper/2504.21801/) (2025)
- [Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies](https://mlsanity.com/paper/2308.03188/) (2023)
- [HyperTree Proof Search for Neural Theorem Proving](https://mlsanity.com/paper/2205.11491/) (2022)
- [DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search](https://mlsanity.com/paper/2408.08152/) (2024)
- [Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning](https://mlsanity.com/paper/2504.11354/) (2025)
- [PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition](https://mlsanity.com/paper/2407.11214/) (2024)
- [Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction](https://mlsanity.com/paper/2508.03613/) (2025)
- [The Lean mathematical library](https://mlsanity.com/paper/1910.09336/) (2019)
- [Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving](https://mlsanity.com/paper/2502.07640/) (2025)
- [Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving](https://mlsanity.com/paper/2507.23726/) (2025)
- [BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving](https://mlsanity.com/paper/2502.03438/) (2025)
- [An In-Context Learning Agent for Formal Theorem-Proving](https://mlsanity.com/paper/2310.04353/) (2023)
- [STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving](https://mlsanity.com/paper/2502.00212/) (2025)
- [Hilbert: Recursively Building Formal Proofs with Informal Reasoning](https://mlsanity.com/paper/2509.22819/) (2025)
- [Aristotle: IMO-level Automated Theorem Proving](https://mlsanity.com/paper/2510.01346/) (2025)
- [Process-Driven Autoformalization in Lean 4](https://mlsanity.com/paper/2406.01940/) (2024)
- [Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experience](https://mlsanity.com/paper/2512.17260/) (2025)
- [InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided Search](https://mlsanity.com/paper/2410.15700/) (2024)
- [HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving](https://mlsanity.com/paper/2412.20735/) (2024)
- [Reliable Evaluation and Benchmarks for Statement Autoformalization](https://mlsanity.com/paper/2406.07222/) (2024)
- [A Semantic Search Engine for Mathlib4](https://mlsanity.com/paper/2403.13310/) (2024)
- [Prover Agent: An Agent-Based Framework for Formal Mathematical Proofs](https://mlsanity.com/paper/2506.19923/) (2025)
- [Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics](https://mlsanity.com/paper/2510.12787/) (2025)

## Built on by (most cited first, 18 of 18)

- [Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement](https://mlsanity.com/paper/2606.06468/) (2026)
- [SorryDB: Can AI Provers Complete Real-World Lean Theorems?](https://mlsanity.com/paper/2603.02668/) (2026)
- [Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics](https://mlsanity.com/paper/2606.31134/) (2026)
- [Optimizing the Cost-Quality Tradeoff of Agentic Theorem Provers in Lean](https://mlsanity.com/paper/2606.04883/) (2026)
- [AxDafny: Agentic Verified Code Generation in Dafny](https://mlsanity.com/paper/2606.32007/) (2026)
- [AoA: Theorem Proving Agent over Abstract Syntax Tree of Redesigned Language](https://mlsanity.com/paper/2607.16372/) (2026)
- [FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving](https://mlsanity.com/paper/2608.25220/) (2026)
- [AxQM: A Textbook-Scale Benchmark for Formal Proof Synthesis in a Library of Finite-Dimensional Quantum Mechanics](https://mlsanity.com/paper/2609.05157/) (2026)
- [Compile to Compress: Boosting Formal Theorem Provers by Compiler Outputs](https://mlsanity.com/paper/2604.18587/) (2026)
- [CAPRI: Contract-Aware Proof Repair for Isabelle](https://mlsanity.com/paper/2608.13459/) (2026)
- [TCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Science](https://mlsanity.com/paper/2609.35606/) (2026)
- [FORALL-LEAN-AGENT for Auditable Reasoning in Formal Mathematics and Software Verification](https://mlsanity.com/paper/2610.00885/) (2026)
- [Let the Library Speak: Self-Advertised Method Selection for Formal Proving](https://mlsanity.com/paper/2610.09401/) (2026)
- [Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification](https://mlsanity.com/paper/2610.09681/) (2026)
- [Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify](https://mlsanity.com/paper/2610.13632/) (2026)
- [The Future of AI-assisted Physics: Discovering, Verifying and Organizing Results via Formalization](https://mlsanity.com/paper/2610.14059/) (2026)
- [Do Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean](https://mlsanity.com/paper/2610.17331/) (2026)
- [Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization](https://mlsanity.com/paper/2610.20573/) (2026)

## Abstract (arXiv)

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.