mlsanityData to 14 Oct 2026

ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning

arXiv 2604.06401 ↗First posted 7 Apr 2026cs.AI cs.CE cs.CVcited by 0

On the map: LLM reasoning → Mathematical reasoning

Abstract (arXiv)

The large language models (LLMs) might produce a persuasive argument within mathematical and logical fields, although such argument often includes some minor missteps, including the entire omission of side conditions, invalid inference patterns, or appeals to a lemma that cannot be derived logically out of the context being discussed. These omissions are infamously hard to notice solely out of the text, as even the misconstrued construction still may seem mostly accurate. Conversely, interactive theorem provers like Lean and Coq have rigorous reliability by ensuring that syntactic and semantic statements only accept statements that can pass all the syntactic and semantic steps in the program which is a small trusted kernel of the language type-checks with. Despite the fact that this technique provides strong guarantees, it comes at quite a heavy price: the evidence must be completely formalized, and the evidence user or a auxiliary search program must provide an avalanche of low-level information. This paper presents a hybrid pipeline where an LLM generates a typed proof sketch in a compact DSL and a lightweight trusted kernel expands the sketch into explicit proof obligations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 24 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

13 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition30 Apr
  2. 2020Generative Language Modeling for Automated Theorem Proving7 Sep

Other references · 11

  1. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
  2. 2021MiniF2F: a cross-system benchmark for formal Olympiad-level mathematicscited by 391
  3. 2024DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Datacited by 234
  4. 2023ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematicscited by 192
  5. 2022Formal Mathematics Statement Curriculum Learningcited by 142
  6. 2019Learning to Prove Theorems via Interacting with Proof Assistantscited by 109
  7. 2025Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2cited by 97
  8. 2018TacticToe: Learning to Prove with Tacticscited by 44
  9. 2019Generating Correctness Proofs with Neural Networkscited by 37
  10. 2021TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learningcited by 37
  11. 2017HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Provingcited by 26

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  1. 2025ProofCompass: Enhancing Specialized Provers with LLM Guidance
  2. 2024Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning
  3. 2025Hilbert: Recursively Building Formal Proofs with Informal Reasoning
  4. 2026MA-ProofBench: A Two-Tiered Evaluation of LLMs for Theorem Proving in Mathematical Analysis
  5. 2025Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving
  6. 2025MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification
  7. 2025Reliable Fine-Grained Evaluation of Natural Language Math Proofs
  8. 2026On Reasoning-Centric LLM-based Automated Theorem Proving