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NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry

Paper recorded by Signals 4 on 2026-08-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.AI · 人工智能 · first seen 2026-08-31

Abstract

Recent advances in large language models (LLMs) have demonstrated strong capabilities in natural language understanding and mathematical reasoning. However, their ability to translate informal mathematical problems into formal representations remains underexplored. This limitation is particularly important for neuro-symbolic geometry systems such as AlphaGeometry, whose theorem-proving engine requ

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#296 most recent of 300 cs.AI papers we have recorded · ↑ newer: How Proper Scoring Rules Shape LLM Forecasting · ↓ older: COVER: Identifiable Evaluation of Coalition Routing
Cite this page: NL2AGBench: Benchmarking LLM Auto-Formalization for AlphaGeometry: the #296 most recent of 300 cs.AI papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/nl2agbench-benchmarking-llm-auto-formalization-for-alphageometry.html
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