Scaling Lean Theorem Proving for Real-World Code Verification
Summary
The paper presents EasyProver, a Lean proving system using recursive agents and asynchronous search that near-saturates PutnamBench and IMO benchmarks with an off-the-shelf frontier model, plus Aegis, a pipeline converting competitive programming into harder real-world Rust-to-Lean verification tasks for benchmarking and RL.
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Paper page - Scaling Lean Theorem Proving for Real-World Code Verification
Source: https://huggingface.co/papers/2610.38174
Abstract
EasyProver is a Lean proving system using recursive agents and asynchronous search that solves nearly all Putnam and IMO benchmark problems, while Aegis generates harder verification tasks from competitive programming to benchmark provers and support reinforcement learning.
We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. Using an off-the-shelf frontier model with no additional training, it proves 669/672 PutnamBench problems and all six IMO 2026 formalizations and resolves 597/600 NTP4VC verification conditions and all 189 VERINA tasks by proof or refutation. Because these benchmarks are close to saturation, we also introduce Aegis, which converts competitive programming problems into much harder verification tasks over real implementations. The pipeline writes safe Rust solutions, translates them into Lean with Aeneas, generates specifications, and labels each frozen statement with a kernel-checked proof or refutation. It produces 2,045 differentially validated Rust-Lean program pairs and 11,533 candidate specifications. Unlike hand-written benchmarks, these tasks carry implementation-level obligations from fixed-width arithmetic, explicit failure, generated loops, casts, and functionalized mutation. On a stratified sample of 2,000 statements, EasyProver proves 1,054 (52.7%) within a 30-minute budget per statement. The same tasks can also be used to benchmark other provers and as rewards forreinforcement learning.
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