arXiv AI August 3, 2026 By Zhan'ao Yao, Liang Yin, Zhihao Gao, Boxuan Zhang, Xiaoyu Wu, Linjing Li, Rongyan Wang, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu

Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery

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arXiv:2607. 28684v1 Announce Type: new Abstract: Existing benchmarks for scientific equation discovery are largely composed of well-known equations available in the public domain, making it difficult to determine whether a model is discovering laws from data or merely recalling answers from its training corpus.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Evidence-Informed LLM Beliefs for Continual Scientific Discovery

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LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search

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