Tag
This paper presents a framework that augments Large Language Models with geometric vision parsing and symbolic solving to match state-of-the-art multimodal models on complex geometry problems, using a new benchmark from 2025 Chinese Zhongkao exams for evaluation.
A neurosurgery resident at Peking College Hospital uses GPT 5.6 Sol to prove a two-decades-old conjecture in numerical linear algebra, supporting research on transcranial ultrasound.
Introducing Leanstral 1.5, a 119B parameter (6B active) open model for formal proof engineering in Lean 4, achieving 100% on miniF2F, state-of-the-art scores on PutnamBench and FATE benchmarks, and discovering previously unknown bugs in open-source repositories.
This paper introduces the MELD dataset for evaluating whether text embedding models capture mathematical equivalence across different terminologies, and finds that current models fail. It proposes a contrastive learning approach to align informal and formal mathematical statements, improving retrieval on both informal-formal and natural language tasks.
This paper applies graph neural networks to predict the solvability of finite groups, demonstrating an AI-driven approach to a classic problem in group theory.
OpenAI claims its general-purpose reasoning model discovered a counterexample to the conjectured upper bound in Erdős's planar unit-distance problem, producing a proof reviewed by mathematicians.
Lean Refactor presents a retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of Lean proofs, achieving significant compression and compilation-time reduction.