arXiv AI June 4, 2026 By Soyeong Jeong, Jinheon Baek, Minki Kang, Sung Ju Hwang

TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration

arXiv:2606. 04743v1 Announce Type: cross Abstract: Agents are widely deployed as assistants over documents, tools, and code.

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

agents

arXiv AI

1d ago

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.

By Ziyang Cai, Amir Saeidi, Harkirat Behl

agents fine-tuning benchmarks

arXiv AI

Jul 24

AREX: Towards a Recursively Self-Improving Agent for Deep Research

arXiv:2607. 21461v1 Announce Type: new Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints.

By Shuqi Lu, Chaofan Li, Kun Luo, Zhang Zhang, Hui Wang, Hongwang Xiao, Zheng Liu, Lei Xiong, Jiahao Wang, Sen Wang, Xiyan Jiang, Wanli Li, Yuyang Hu, Hongjin Qian, Bingyu Yan, Ziyi Xia, Yingxia Shao, Kang Liu, Zhicheng Dou, Di He, Chaozhuo Li, Qiwei Ye, Zhongyuan Wang, Zheng Liu

agents reinforcement-learning benchmarks

arXiv AI

Aug 6

Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports

arXiv:2608. 04682v1 Announce Type: cross Abstract: Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific bug in large-scale codebase.

By Haobin Li, Ping Deng, Weizhong Qian, Liang Jiang, Zhenyu Huang, Mouxing Yang, Xi Peng

llms agents benchmarks

arXiv Machine Learning

Jul 7

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI

arXiv:2605. 08678v3 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes.

By Bohan Lyu, Yucheng Yang, Siqiao Huang, Jiaru Zhang, Qixin Xu, Xinghan Li, Xinyang Han, Yicheng Zhang, Huaqing Zhang, Runhan Huang, Kaicheng Yang, Zitao Chen, Wentao Guo, Junlin Yang, Xinyue Ai, Wenhao Chai, Yadi Cao, Ziran Yang, Kun Wang, Dapeng Jiang, Huan-ang Gao, Shange Tang, Chengshuai Shi, Simon S. Du, Max Simchowitz, Jiantao Jiao, Dawn Song, Chi Jin

llms agents benchmarks

arXiv AI

Jul 1

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.

By Gan Luo, Zihan Qin, Bin Dong, Wotao Yin

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 16

ACC: Compiling Agent Trajectories for Long-Context Training

arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.

By Qisheng Su, Zhen Fang, Shiting Huang, Yu Zeng, Yiming Zhao, Kou Shi, Ziao Zhang, Lin Chen, Zehui Chen, Lijun Wu, Feng Zhao

llms agents fine-tuning benchmarks

Related stories

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

AREX: Towards a Recursively Self-Improving Agent for Deep Research

Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

ACC: Compiling Agent Trajectories for Long-Context Training