Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs
- URL: http://arxiv.org/abs/2608.15432v1
- Date: Sat, 15 Aug 2026 22:20:08 GMT
- Title: Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs
- Abstract summary: We introduce Pistis, an agentic, oracle-guided proof search that produces formal Lean proofs that satisfy five conditions.<n>At its core is a novel faithfulness-preserving divide-and-conquer search, which we name OrderDecompose.<n>We apply Pistis on the first three books of Euclid's Elements, producing high-quality artifacts containing faithful formal proofs.
- Score: 16.584143060150165
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search can produce a formal proof that compiles, the generated proof does not necessarily reflect how the natural-language argument arrives at its conclusion--a property we refer to as faithfulness. With faithfully formalized proofs, one can check the reasoning behind a human- or AI-written argument, and assist mathematicians in formalizing their proof sketches. However, it is particularly challenging due to misalignment of formal proof tactics and natural language reasoning. In this work, we rigorously describe a set of five necessary conditions a faithful formal proof must satisfy, and introduce Pistis, an agentic, oracle-guided proof search that produces formal Lean proofs that satisfy them. At its core is a novel faithfulness-preserving divide-and-conquer search, which we name OrderDecompose, that tracks citation dependencies and blocks unfaithful shortcuts, paired with a refutation search, that surfaces gaps and errors in the natural language proof source. OrderDecompose completes proofs that baselines cannot close even within a 12-hour budget, and its artifacts compile over 33$\times$ as fast as prior work's. We apply Pistis on the first three books of Euclid's Elements, producing high-quality artifacts containing faithful formal proofs. Under a blinded human study and an LLM-as-a-judge protocol on rigorous rubrics, Pistis-generated proofs are favored over prior works--2.89$\times$ and 5.2$\times$ as often by human reviewers and the LLM judge, respectively. It further uncovers gaps in Euclid's proofs and their translation, and can accept or refute natural language proofs written by humans or AI, demonstrating that faithful formalization is useful as a proof-checking tool.
Related papers
- LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks [85.86474267842907]
Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate verifiable proofs in formal languages like Lean.<n>We present LEAP, an agentic framework that enables general-purpose foundation models to achieve state-of-the-art performance on automated formal theorem proving.
arXiv Detail & Related papers (2026-06-02T08:16:42Z) - Pseudo-Formalization for Automatic Proof Verification [17.612188352560494]
Reliable verification of proofs remains a bottleneck for training and evaluating AI systems on hard mathematical reasoning.<n>We propose Pseudo-Formalization (PF), a proof format that captures the modularity and precision of formal proofs.<n>To support future work, we release our research-level proof verification benchmark ArxivMathGradingBench.
arXiv Detail & Related papers (2026-05-19T22:08:51Z) - StepProof: Step-by-step verification of natural language mathematical proofs [16.150265021594088]
We propose StepProof, a novel autoformalization method for granular, step-by-step verification.<n>StepProof breaks down complete proofs into multiple verifiable subproofs, enabling sentence-level verification.<n>We show that StepProof significantly improves proof success rates and efficiency compared to traditional methods.
arXiv Detail & Related papers (2025-06-12T10:31:23Z) - Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification [56.218970738892764]
Chain-of-Thought prompting has become the de facto method to elicit reasoning capabilities from large language models (LLMs)<n>To mitigate hallucinations in CoT that are notoriously difficult to detect, current methods operate as opaque boxes and do not provide checkable evidence for their judgments, possibly limiting their effectiveness.<n>We propose a retrospective, step-aware formal verification framework $Safe$. Rather than assigning arbitrary scores, we strive to articulate mathematical claims in formal mathematical language Lean 4 at each reasoning step and provide formal proofs to identify hallucinations.
arXiv Detail & Related papers (2025-06-05T03:16:08Z) - LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction [74.79306773878955]
We introduce LeanProgress, a method that predicts the progress in the proof.<n>Our experiments show that LeanProgress achieves an overall prediction accuracy of 75.1%.
arXiv Detail & Related papers (2025-02-25T07:46:36Z) - Lean-STaR: Learning to Interleave Thinking and Proving [53.923617816215774]
We present Lean-STaR, a framework for training language models to produce informal thoughts prior to each step of a proof.<n>Lean-STaR achieves state-of-the-art results on the miniF2F-test benchmark within the Lean theorem proving environment.
arXiv Detail & Related papers (2024-07-14T01:43:07Z) - Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal
Proofs [30.57062828812679]
We introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches.
We show that large language models are able to produce well-structured formal sketches that follow the same reasoning steps as the informal proofs.
arXiv Detail & Related papers (2022-10-21T22:37:22Z) - NaturalProver: Grounded Mathematical Proof Generation with Language
Models [84.2064569475095]
Theorem proving in natural mathematical language plays a central role in mathematical advances and education.
We develop NaturalProver, a language model that generates proofs by conditioning on background references.
NaturalProver is capable of proving some theorems that require short (2-6 step) proofs, and providing next-step suggestions that are rated as correct and useful over 40% of the time.
arXiv Detail & Related papers (2022-05-25T17:01:18Z) - Generating Natural Language Proofs with Verifier-Guided Search [74.9614610172561]
We present a novel stepwise method NLProofS (Natural Language Proof Search)
NLProofS learns to generate relevant steps conditioning on the hypothesis.
It achieves state-of-the-art performance on EntailmentBank and RuleTaker.
arXiv Detail & Related papers (2022-05-25T02:22:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.