arXiv AI
Jul 21
Self-Modifying Lean Proof Agents with Verifier-Grounded Benchmark Coevolution
arXiv:2607. 17352v1 Announce Type: new Abstract: Designing effective Lean proof agents is a central challenge in formal mathematical reasoning.
Do Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean
Related stories
arXiv AI
Jun 16
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv:2603. 02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub.
Hugging Face Trending Papers
Aug 13
Vero: Can AI Agents Build Formally Verified Software Repositories?
AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked proof of its specification, offers a stronger path toward trustworthy AI-generated software.
arXiv AI
Oct 2
FORALL-LEAN-AGENT for Auditable Reasoning in Formal Mathematics and Software Verification
FORALL-LEAN-AGENT is a frontend‑agnostic framework that enhances auditable reasoning in formal mathematics and software verification. It integrates isolated workspaces, Lean tools, and fresh review processes—including statement comparison, axiom audits, and independent proof checking—to bind verification evidence and reviewer decisions to the same candidate artifact, ensuring traceable acceptance. Evaluations on VeriSoftBench, PutnamBench, and the Lean Eval track show that the framework improves benchmark success rates and reduces costs while providing evidence beyond aggregate solve counts.
arXiv AI
Aug 14
Vero: Can AI Agents Build Formally Verified Software Repositories?
arXiv:2608. 13522v1 Announce Type: cross Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code.
arXiv AI
Jun 6
Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
arXiv:2606. 06468v1 Announce Type: new Abstract: We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement.
arXiv AI
Oct 2
RMA: Context-Orchestrated Research Math Agents
arXiv:2605.22875v2 Announce Type: replace Abstract: Long-horizon mathematical reasoning fails less often because a model cannot produce a valid next step than because an agent fails to maintain and e...
arXiv AI
Jun 9
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
arXiv:2606. 09450v1 Announce Type: new Abstract: LLMs have recently achieved strong results on formal proving benchmarks.
arXiv Machine Learning
Sep 23
VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean
arXiv:2602.18307v2 Announce Type: replace-cross Abstract: Large language models have achieved striking results in interactive theorem proving, particularly in Lean. However, most benchmarks for LLM-b...
Hugging Face Trending Papers
Jul 7
Harnessing Code Agents for Automatic Software Verification
Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large language models (LLMs) promise to generate these proofs automatically, yet existing approaches wire a fixed, human-designed proof strategy into the system and constrain the model to follow it (retrieving premises and predicting tactics one step at a time, or splitting goals by divide-and-conquer), and still prove only a fraction of their target theorems.
Hugging Face Trending Papers
Jun 8
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to capture how models behave on longer, more dependency-rich mathematical developments.
arXiv AI
Oct 12
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents
ProgramGym introduces a scalable framework for training long-horizon program reproduction agents, comprising Program-Env and Program-Agent components. Program-Env builds verifiable training environments from repositories via a feedback loop that repairs sandbox builds and improves black-box tests, yielding 1,648 environments across C, C++, Go, and Rust. Program-Agent employs a role-isolated workflow of analysis, implementation, and review with revision management, boosting performance on ProgramBench and raising Qwen3.8-27B’s accuracy from 34.87% to 42.84% and surpassing flagship models with fine-tuning.