SourceStalecollected in 7h

MathAtlas: New Benchmark for Graduate-Level Autoformalization

Read original on ArXiv AI
#autoformalization#mathematics#benchmarking#llm-reasoning

First benchmark for graduate-level math autoformalization; exposes critical failures in current SOTA reasoning models.

30-Second TL;DR

What Changed

Contains 52k theorems, definitions, and proofs from 103 graduate textbooks.

Why It Matters

This benchmark exposes the limitations of current LLMs in handling deep, structured mathematical reasoning. It will likely drive research into more robust, dependency-aware architectures for formal mathematics.

What To Do Next

Download the MathAtlas dataset from the arXiv repository to stress-test your model's reasoning capabilities on complex, multi-step mathematical proofs.

Who should care:Researchers & Academics

Key Points

  • Contains 52k theorems, definitions, and proofs from 103 graduate textbooks.
  • Features a dependency graph with 178k relations for dependency-aware evaluation.
  • Reveals significant performance degradation in SOTA models as dependency depth increases.
  • Provides a challenging testbed for autoformalization with low baseline correctness scores.

Weekly AI Recap

Read this week's curated digest of top AI events →

AI-curated news aggregator. All content rights belong to original publishers.
Original source: ArXiv AI

This is a summary, not the original. Read the source, or get the weekly briefing.

The weekly digest

One email a week. Unsubscribe anytime.