mlsanityData to 7 Oct 2026

Translating Informal Proofs into Formal Proofs Using a Chain of States

arXiv 2512.10317 ↗First posted 11 Dec 2025cs.LO cs.AIcited by 3

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Abstract (arXiv)

We address the problem of translating informal mathematical proofs expressed in natural language into formal proofs in Lean4 under a constrained computational budget. Our approach is grounded in two key insights. First, informal proofs tend to proceed via a sequence of logical transitions - often implications or equivalences - without explicitly specifying intermediate results or auxiliary lemmas. In contrast, formal systems like Lean require an explicit representation of each proof state and the tactics that connect them. Second, each informal reasoning step can be viewed as an abstract transformation between proof states, but identifying the corresponding formal tactics often requires nontrivial domain knowledge and precise control over proof context. To bridge this gap, we propose a two stage framework. Rather than generating formal tactics directly, we first extract a Chain of States (CoS), a sequence of intermediate formal proof states aligned with the logical structure of the informal argument. We then generate tactics to transition between adjacent states in the CoS, thereby constructing the full formal proof. This intermediate representation significantly reduces the complexity of tactic generation and improves alignment with informal reasoning patterns. We build dedicated datasets and benchmarks for training and evaluation, and introduce an interactive framework to support tactic generation from formal states. Empirical results show that our method substantially outperforms existing baselines, achieving higher proof success rates.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 11 Dec 2025.

Builds on

25 of 33 references.

References · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  4. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
  5. 2023Llemma: An Open Language Model For Mathematicscited by 385
  6. 2022Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofscited by 278
  7. 2022Autoformalization with Large Language Modelscited by 252
  8. 2024DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Datacited by 225
  9. 2022HyperTree Proof Search for Neural Theorem Provingcited by 192
  10. 2024DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Searchcited by 189
  11. 2023ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematicscited by 180
  12. 2022Formal Mathematics Statement Curriculum Learningcited by 141
13 more
  1. 2025Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Provingcited by 126
  2. 2024Lean Workbook: A large-scale Lean problem set formalized from natural language math problemscited by 115
  3. 2023LEGO-Prover: Neural Theorem Proving with Growing Librariescited by 108
  4. 2025BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Provingcited by 82
  5. 2024Lean-STaR: Learning to Interleave Thinking and Provingcited by 64
  6. 2024TheoremLlama: Transforming General-Purpose LLMs into Lean4 Expertscited by 58
  7. 2024A Survey on Deep Learning for Theorem Provingcited by 55
  8. 2024Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalizationcited by 53
  9. 2024HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Provingcited by 32
  10. 2024LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN provercited by 25
  11. 2025ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of Datacited by 23
  12. 2022Towards a Mathematics Formalisation Assistant using Large Language Modelscited by 22
  13. 2024FormalAlign: Automated Alignment Evaluation for Autoformalizationcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

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  1. 2025Hilbert: Recursively Building Formal Proofs with Informal Reasoning
  2. 2025ProofBridge: Auto-Formalization of Natural Language Proofs in Lean via Joint Embeddings
  3. 2025Steering LLMs for Formal Theorem Proving
  4. 2025DRIFT: Decompose, Retrieve, Illustrate, then Formalize Theorems
  5. 2026Magenta: Closing the Loop Between Mathematical Reasoning and Lean Verification
  6. 2026Pythagoras-Prover: Advancing Efficient Formal Proving via Augmented Lean Formalisation
  7. 2026LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
  8. 2025Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs