Skip to content
NewA calmer, faster ZeroHour

EasyProver solves near-saturated Lean benchmarks and half of harder Aegis tasks

Original title: Scaling Lean Theorem Proving for Real-World Code Verification

Hugging Face daily papersby Debargha Ganguly, Divya Raghunathan, Dejan Jovanović ()1 min read
Part of a story covered by 2 sourcesEasyProver proves 1,054 of 2,000 Aegis Lean statementsMerged summary & timeline
AI summary · x-ai/grok-4.7Summaries by Vani AI

On a stratified Aegis sample of 2,000 statements, it proves 1,054 within a 30-minute budget.

EasyProver is a Lean proving system of recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. With an unnamed off-the-shelf frontier model and no extra training, it proves 669 of 672 PutnamBench problems, all six IMO 2026 formalizations, 597 of 600 NTP4VC conditions, and all 189 VERINA tasks. Aegis converts competitive-programming problems into harder checks over real Rust code translated to Lean with Aeneas.

Unconfirmed: The frontier model used by EasyProver is not named.

Key facts
  • Aegis produces 2,045 validated Rust-Lean pairs and 11,533 candidate specifications.
  • Obligations cover fixed-width arithmetic, explicit failure, generated loops, casts, and mutation.
  • EasyProver proves 1,054 of 2,000 sampled statements, or 52.7 percent.
  • The authors also propose the tasks as reinforcement-learning rewards.
Explain this AI news with Vani AIWhat was announced, what is new and why it matters, in a new chat

From the original article

1 min read at huggingface.co

We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service.

Hugging Face daily papers

By Debargha Ganguly, Divya Raghunathan, Dejan Jovanović · huggingface.co

Read the full article at Hugging Face daily papers

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.