arXiv AI

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.

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

5d ago

PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research

arXiv:2512. 19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain expertise, long-horizon reasoning, and reliable numerical computation.

By Tingjia Miao, Wenkai Jin, Jinxin Tan, Muhua Zhang, Xianghe Pang, Zexi Liu, Yuwen Du, Tian Jin, Tu Guo, Zhengliang Zhang, Jingkun Liu, Yuelin Hu, Jiejun Zhang, Yunjie Huang, Yuhan Wang, Wenbo Li, Yinuo Gao, Shuo Chen, Rui Ye, Yuzhi Zhang, Linfeng Zhang, Kun Chen, Wei Wang, Weinan E, Siheng Chen

llms agents benchmarks

arXiv AI

Jun 2

Principle-Evolvable Scientific Discovery via Uncertainty Minimization

arXiv:2602. 06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial priors.

By Yingming Pu, Tao Lin, Hongyu Chen

llms agents benchmarks

arXiv AI

Jul 14

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

arXiv:2607. 10127v1 Announce Type: cross Abstract: Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery.

By Xuanzhou Chen, Taoli Cheng

llms rag reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

1d ago

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

arXiv:2608. 15669v1 Announce Type: new Abstract: Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs.

By Zhongwei Yu, Yan Song, Xue Yan, Anjie Liu, Xingyu Lu, Yihang Chen, Huichi Zhou, Siyuan Guo, Luoyang Sun, Sihan Chen, Xiangning Yu, Jun Wang

llms

arXiv AI

Jun 10

Towards Diverse Scientific Hypothesis Search with Large Language Models

arXiv:2606. 10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses.

By Haorui Wang, Parshin Shojaee, Kazem Meidani, Kunyang Sun, Jos\'e Miguel Hern\'andez-Lobato, Teresa Head-Gordon, Jiajun He, Chandan K. Reddy, Chao Zhang, Yuanqi Du

llms

arXiv AI

22h ago

The Problem Is the Problem: Towards Scalable Mathematical Discovery

arXiv:2608. 16977v1 Announce Type: new Abstract: AI systems are increasingly capable of contributing to mathematical research.

By Zeyu Zheng, Shengtong Zhang, Jeremy Avigad, Prasad Tetali, Sean Welleck

arXiv AI

Aug 3

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.

By Chaoqun Yang, Xinyu Lin, Shulin Li, Wenjie Wang, Ruihan Guo, Fuli Feng, Tat-Seng Chua

llms agents benchmarks

arXiv AI

Jul 3

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

arXiv:2603. 13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher.

By Haonan Huang

llms agents benchmarks

arXiv AI

Jun 4

AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?

arXiv:2606. 05080v1 Announce Type: new Abstract: Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts.

By Zhangchen Xu, Junda Chen, Yue Huang, Dongfu Jiang, Jiefeng Chen, Hang Hua, Zijian Wu, Zheyuan Liu, Zexue He, Lichi Li, Shizhe Diao, Jiaxin Pei, Jinsung Yoon, Hao Zhang, Mengdi Wang, Radha Poovendran, Misha Sra, Alex Pentland, Zichen Chen

llms agents benchmarks

arXiv AI

Jul 28

LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

arXiv:2602. 10576v2 Announce Type: replace-cross Abstract: Symbolic regression aims to distill mathematical equations from observational data.

By Boxiao Wang, Kai Li, Tianyi Liu, Chen Li, Junzhe Wang, Yifan Zhang, Jian Cheng

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 8

Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus

arXiv:2606. 07426v1 Announce Type: new Abstract: A fundamental problem in science is identifying underlying patterns of complex systems in the form of concise mathematical formulas.

By Hanqiao Yu, Shusen Yang, Xuebin Ren, Cong Zhao

benchmarks

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