arXiv AI June 15, 2026 By Li Xia, Baoxun Wang

Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks

arXiv:2606. 14386v1 Announce Type: cross Abstract: Scientific discovery saturates when new hypotheses cease to provide independent information, even if the nominal hypothesis space remains large.

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

arXiv AI

Jul 21

Automated Discovery Has No Universally Superior Harness

arXiv:2607. 18235v1 Announce Type: cross Abstract: Autonomous discovery systems such as OpenEvolve and TTT-Discover are often used as general-purpose harnesses.

By Akshat Gupta, Jermaine Lei, Alexander Lu, Gopala Anumanchipalli, Leshem Choshen

llms agents

arXiv Machine Learning

Jul 7

Dictionaries, Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery

arXiv:2607. 04108v1 Announce Type: new Abstract: Large language models are increasingly used as evolutionary engines for scientific discovery: generate candidates, select winners, feed them back as parents, and repeat.

By Pan Li

llms

arXiv AI

Jun 30

Evidence-Informed LLM Beliefs for Continual Scientific Discovery

arXiv:2606. 29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides which hypotheses to test next.

By Dhruv Agarwal, Reece Adamson, Andrew McCallum, Peter Clark, Ashish Sabharwal, Bodhisattwa Prasad Majumder

llms rag

arXiv AI

Jul 29

Structured Scaling of AI Discovery Across Diverse Scientific Domains

arXiv:2604. 19341v2 Announce Type: replace-cross Abstract: Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions.

By Haotian Ye, Haowei Lin, Jingyi Tang, Yizhen Luo, Rahul Thapa, Caiyin Yang, Chang Su, Rui Yang, Ruihua Liu, Rundao Li, Zeyu Li, Pengwei Sun, Chong Gao, Dachao Ding, Guangrong He, Miaolei Zhang, Lina Sun, Wenyang Wang, Yuchen Zhong, Zhuohao Shen, Puheng Li, Pan Lu, Bianxiao Cui, Di He, Jianzhu Ma, Junfeng Li, Hexi Baoyin, Yejin Choi, Stefano Ermon, Xiaowen Chu, Tongyang Li, Yuzhi Xu, James Zou

llms benchmarks

arXiv Machine Learning

Jul 30

Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility

arXiv:2607. 26828v1 Announce Type: new Abstract: Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates.

By Yansen Zhang, Yilu Liu, Tianyu Liu, Jiamin Chen, Xiaokun Zhang, Kai Xie, Xue Liu, Chen Ma, Yiyan Qi

llms benchmarks

arXiv Machine Learning

Jun 25

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

arXiv:2606. 25039v1 Announce Type: new Abstract: Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains.

By Nikhil Abhyankar, Sha Li, Sanchit Kabra, Naren Ramakrishnan, Yulia Gel, Chandan K. Reddy

llms benchmarks

Related stories

Automated Discovery Has No Universally Superior Harness

Dictionaries, Not Darwin: Set-Level Selection Beats LLM Evolution in Scientific Equation Discovery

Evidence-Informed LLM Beliefs for Continual Scientific Discovery

Structured Scaling of AI Discovery Across Diverse Scientific Domains

Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility

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