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arxiv: 2605.08694 · v1 · submitted 2026-05-09 · 💻 cs.SE

Recognition: 2 theorem links

· Lean Theorem

A Learning Method for Symbolic Systems Using Large Language Models

Jian Fang, Yingfei Xiong, Yixun Yao

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:29 UTC · model grok-4.3

classification 💻 cs.SE
keywords automated theorem provinglarge language modelstactic miningCoqsymbolic proversformal verificationLLM2Ltacproof synthesis
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The pith

Large language models can mine reusable symbolic tactics from proofs to boost theorem provers like CoqHammer by nearly 24 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LLM2Ltac as a way to use large language models to examine collections of formal proofs and distill general, reusable tactics that symbolic provers can adopt. Rather than deploying LLMs as direct, resource-heavy provers, the method converts their reasoning into efficient symbolic code that runs without ongoing model costs. This combination matters for scaling formal verification of safety-critical systems, where traditional provers are fast but limited in scope while pure LLM approaches remain slow and opaque. Experiments mine tactics from over eleven thousand theorems and test them on nearly six thousand theorems across four real-world Coq projects.

Core claim

LLM2Ltac prompts an LLM to identify latent proof strategies within a corpus of 11,725 theorems drawn from the Coq standard library, then formalizes those strategies into verified, reusable tactics. When the resulting tactics are added to CoqHammer, the prover succeeds on 23.87 percent more theorems from the evaluation set of 6,199 theorems taken from CompCert, Coq-Art, Ext-Lib, and VFA. Integrating the enhanced CoqHammer with Claude Code further raises the total number of proved theorems by 9.90 percent.

What carries the argument

LLM2Ltac, which uses an LLM as a synthesizer to extract and formalize latent proof strategies from existing proofs into validated symbolic tactics for integration into provers.

Load-bearing premise

The tactics identified by the LLM are assumed to remain valid, generalizable across new theorems, and non-redundant when added to the symbolic prover, without any post-evaluation filtering that could inflate the measured gains.

What would settle it

Re-running the evaluation on the 6,199 theorems from the four projects after adding the mined tactics and finding that CoqHammer proves no additional theorems, or fewer, would falsify the central effectiveness claim.

Figures

Figures reproduced from arXiv: 2605.08694 by Jian Fang, Yingfei Xiong, Yixun Yao.

Figure 1
Figure 1. Figure 1: The workflow of LLM2Ltac (* Infinite trees with internal nodes labeled *) CoInductive LTree (A:Type) : Type := LLeaf : LTree A | LBin : A → LTree A → LTree A → LTree A. (* An extensional equality on (LTree A) *) CoInductive LTree_bisimilar : LTree A → LTree A → Prop := LTree_bisimilar_leaf : LTree_bisimilar LLeaf LLeaf | LTree_bisimilar_bin : forall (a:A) (t1 t'1 t2 t'2 : LTree A), LTree_bisimilar t1 t'1 →… view at source ↗
Figure 3
Figure 3. Figure 3: One mined tactic from lemma append_neutral_r by LLM2Ltac theorems that are not included in the mining dataset. We then apply the tactic into each position in the theorems’ proving processes. If the proof statement changes after the application, we consider the tactic to have generalization capability and retain it; otherwise, we discard it. For the retained tactics, LLM2Ltac records the source theorem from… view at source ↗
Figure 4
Figure 4. Figure 4: Workflow of generalization testing the current proof goal and then apply the symbolic prover again. We construct an And-Or tree [10] to try different combinations of the above process until the theorem is proved. For the theorem in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompts in proof analysis [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompts in Autoformalization via a single command, avoiding repeated script modifications and dependency conflicts across different tactics. For generalization testing, we insert each mined tactic after ev￾ery original tactic in the benchmark proof scripts (except for the last tactic that closes the goal), and check whether it successfully produces a new proof state. As shown in [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of Tactic Generalizability rates are comparable: 33.87% for OpenAI GPT-5.2 and 37.21% for Deepseek-V3.2. For generalization testing, we observe that LLMs still exhibit severe hallucinations in tactic mining: most generated tactics are incorrect and lack generalization capability, even though LLMs believe they are correct and generalizable. We further observe notable differences in the generali… view at source ↗
read the original abstract

Automated theorem proving is essential for the formal verification of safety-critical systems. As the corpus of formal proofs grows, a natural paradigm is to learn from existing proofs. However, current learning-based approaches predominantly train Large Language Models (LLMs) as end-to-end provers, which yields resource-intensive, opaque systems. Conversely, while traditional symbolic provers are computationally efficient, how to automatically improve these solvers from data remains an open challenge. This paper bridges this gap by proposing LLM2Ltac, the first approach that leverages the reasoning power of LLMs not as end-to-end provers, but as intelligent synthesizers to mine purely symbolic tactics from data. Given a corpus of formal proofs, LLM2Ltac asks an LLM to identify latent proof strategies and formalize them into reusable tactics. These tactics are verified for validity and generalizability, and finally integrated into symbolic provers to enhance their automated proving capabilities without the runtime cost of LLMs. We implement LLM2Ltac on Rocq 8.20.0 and mine tactics from 11,725 theorems in the standard library. We evaluate our approach on 6,199 theorems from four large real-world verification projects, namely, compcert, Coq-Art, Ext-Lib, and VFA. Results show that the mined tactics improve CoqHammer to prove 23.87% more theorems, and when integrating the improved CoqHammer with Claude Code, the overall proved theorems increases by 9.90%, indicating the effectiveness of LLM2Ltac.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes LLM2Ltac, a method that uses LLMs to identify and formalize reusable symbolic tactics from 11,725 theorems in the Coq standard library. These tactics are verified for validity and generalizability before being integrated into CoqHammer. Evaluation on 6,199 theorems from compcert, Coq-Art, Ext-Lib, and VFA reports that the enhanced CoqHammer proves 23.87% more theorems, with a further 9.90% gain when combined with Claude Code.

Significance. If the validation protocol is sound and independent of the evaluation projects, the work is significant for demonstrating how LLMs can synthesize transferable symbolic strategies to improve efficient provers without incurring LLM inference costs at runtime. The use of large real-world verification projects for evaluation adds practical relevance to automated theorem proving in safety-critical systems.

major comments (3)
  1. [§5] §5 (Evaluation): The headline claim of a 23.87% increase in theorems proved by CoqHammer after adding mined tactics requires a detailed protocol for tactic validation, including how validity and generalizability are checked, whether a held-out verification set separate from the 6,199 evaluation theorems is used, and any statistical significance tests or error bars. The current description leaves open the possibility that filtering or formalization steps incorporate performance signals from the evaluation projects.
  2. [§4] §4 (LLM2Ltac method): The description of the LLM prompt, tactic extraction, and verification steps does not specify independence guarantees between the mining corpus (standard library) and the evaluation projects. Without explicit controls against data leakage or post-hoc selection of tactics that succeed on compcert/Coq-Art/etc., the measured improvements cannot be interpreted as evidence of generalizable symbolic strategies.
  3. [§5.2] §5.2 (Integration results): The 9.90% overall gain when combining the improved CoqHammer with Claude Code is presented without an ablation isolating the contribution of the LLM-mined tactics versus other factors, and without reporting how many of the additional proofs are directly attributable to the new tactics versus changes in search strategy.
minor comments (2)
  1. [§4] The manuscript would benefit from a table listing the mined tactics with their formal definitions and the number of theorems they apply to in the evaluation set.
  2. [Throughout] Notation for tactic success rates and 'proved theorems' should be defined consistently in the text and figures to avoid ambiguity between unique theorems and total proof attempts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have revised the manuscript to provide the requested clarifications, expanded protocols, and additional analyses.

read point-by-point responses

  1. Referee: §5 (Evaluation): The headline claim of a 23.87% increase in theorems proved by CoqHammer after adding mined tactics requires a detailed protocol for tactic validation, including how validity and generalizability are checked, whether a held-out verification set separate from the 6,199 evaluation theorems is used, and any statistical significance tests or error bars. The current description leaves open the possibility that filtering or formalization steps incorporate performance signals from the evaluation projects.

    Authors: We agree that additional detail on the validation protocol strengthens the paper. Tactic validation occurs exclusively on the standard library corpus: each candidate tactic is tested on its source theorem and on a set of other standard-library theorems to confirm generalizability. The 6,199 theorems from compcert, Coq-Art, Ext-Lib, and VFA are never accessed during mining, prompt construction, extraction, or validation; they constitute a completely held-out evaluation set. We will add a new subsection in §5 that documents the exact validation criteria, the separation of corpora, and the deterministic nature of the process. We will also report binomial proportion confidence intervals around the 23.87% figure to address the request for error bars. revision: yes

  2. Referee: §4 (LLM2Ltac method): The description of the LLM prompt, tactic extraction, and verification steps does not specify independence guarantees between the mining corpus (standard library) and the evaluation projects. Without explicit controls against data leakage or post-hoc selection of tactics that succeed on compcert/Coq-Art/etc., the measured improvements cannot be interpreted as evidence of generalizable symbolic strategies.

    Authors: The mining corpus consists solely of the 11,725 theorems in the Coq standard library; the four evaluation projects are external developments with no theorem or dependency overlap. LLM prompts are generated exclusively from standard-library proofs, and tactic retention decisions are made only on the basis of success within the standard library. We will revise §4 to state these independence guarantees explicitly, including a note on corpus disjointness and the absence of any evaluation-project-based filtering or post-hoc selection. revision: yes

  3. Referee: §5.2 (Integration results): The 9.90% overall gain when combining the improved CoqHammer with Claude Code is presented without an ablation isolating the contribution of the LLM-mined tactics versus other factors, and without reporting how many of the additional proofs are directly attributable to the new tactics versus changes in search strategy.

    Authors: We will add an ablation table in §5.2 that reports four configurations: (1) baseline CoqHammer, (2) enhanced CoqHammer with mined tactics, (3) baseline CoqHammer + Claude Code, and (4) enhanced CoqHammer + Claude Code. We will also quantify the number of proofs that become solvable only after the new tactics are added (by comparing proof-search traces with and without the mined tactics). This will isolate the contribution of the LLM-mined tactics from other factors. revision: yes

Circularity Check

0 steps flagged

No circularity; data separation and external evaluation keep claims independent

full rationale

The paper mines tactics from 11,725 standard-library theorems and evaluates improvements on a disjoint set of 6,199 theorems from compcert, Coq-Art, Ext-Lib and VFA. No equations, fitted parameters, or self-citations appear in the derivation; the reported gains (23.87% for CoqHammer, 9.90% overall) are measured after tactic extraction and verification on held-out projects. The method therefore does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the approach relies on standard assumptions of tactic validity checking and generalizability of mined strategies, with no explicit free parameters, axioms, or invented entities described.

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