arXiv AI

From Inertia to Objectivity: Improving Deep Research Agents with Noise Isolation

arXiv AI

Oct 7

Stateless Language Agents: Scaling Long-Horizon Automated Research

arXiv:2610.07625v1 Announce Type: cross Abstract: Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay...

By Qizheng Zhang, Changxiu Ji, Isaac Sun, Yuetai Li, Shubhangi Upasani, Sherry Ruan, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Yoonho Lee, Yuzhen Mao, Genghan Zhang, Rulin Shao, Qiuyang Mang, Andy Dimnaku, Changran Hu, Radha Poovendran, Kunle Olukotun

llms agents benchmarks

arXiv AI

Jun 6

Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation

arXiv:2606. 05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile for deep research agents that actively search the web during inference.

By Yongjie Wang, Xinyue Zhang, Kunhong Yao, Zhiwei Zeng, Kaisong Song, Jun Lin, Zhiqi Shen

llms agents benchmarks

arXiv AI

Jul 10

WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search

arXiv:2607. 08662v1 Announce Type: cross Abstract: Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks.

By Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang, Guanting Dong, Jinghan Yang, Han Li, Kun Gai, Ji-Rong Wen, Zhicheng Dou

llms agents nlp benchmarks

arXiv AI

Jun 2

Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism

arXiv:2606. 00408v1 Announce Type: cross Abstract: Long-horizon search agents accumulate large amounts of retrieved content across many tool calls, making context-budget efficiency increasingly important.

By Haoxiang Zhang, Qixin Xu, Zhuofeng Li, Lei Zhang, Pengcheng Jiang, Yu Zhang, Julian McAuley

agents benchmarks

arXiv Machine Learning

Jun 9

SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History

arXiv:2606. 08671v1 Announce Type: new Abstract: Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change.

By Zhiwei Li, Yong Hu

agents benchmarks

arXiv AI

Jun 9

SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research

arXiv:2606. 09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite.

By Pu Ning, Quan Chen, Kun Tao, Xinyu Tang, Tianshu Wang, Qianggang Cao, Xinyu Kong, Zujie Wen, Zhiqiang Zhang, Jun Zhou

llms agents fine-tuning

arXiv Computation and Language

Sep 21

Boosting Deepresearch and LongContext Ability with Self-Generated Deepresearch Rollouts Traces

arXiv:2609.20844v1 Announce Type: new Abstract: Deepresearch (DR) agents interact with real-world web environments through multi-turn search and visit, causing their contexts to grow rapidly over tim...

By Zihan Wang, Hao Wang, Boyuan Jiang, Yiqun Zhang, Shi Feng, Xiaocui Yang, Yiwen Ye, Jianghang Lin, Xiaozhong Ji, Jinghao Lin, Kai Wu

agents reinforcement-learning benchmarks safety

arXiv AI

Sep 15

Question's Gambit: The First Move Matters in Agentic Deep Search

arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...

By Radin Hamidi Rad, Amin Bigdeli, Negar Arabzadeh, Sajad Ebrahimi, Charles L. A. Clarke, Benjamin C. M. Fung, Ebrahim Bagheri

llms rag agents 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

Oct 15

SearchMaster: Grounded and Regulated Self-Play for Search Agents

arXiv:2608.01822v2 Announce Type: replace Abstract: Training LLM-based search agents requires high-quality search data: tasks that demand genuine multi-hop retrieval and trajectories that use search...

By Wentao Tan, Qiong Cao, Jiaqi Wang, Nan Duan

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

arXiv:2606. 02060v1 Announce Type: new Abstract: Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis.

By Jiaming Wang, Ziteng Feng, Jiangtao Wu, Ruihao Li, Qianqian Xie, Yuxiang Ren, He Zhu, Xueming Han, Fanyu Meng, Junlan Feng, Jiaheng Liu

llms agents benchmarks

arXiv AI

Oct 15

Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents

arXiv:2610.16998v1 Announce Type: new Abstract: Deep search enables language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-sourc...

By Cong Pang, Jiaqi Su, Chengrui Wu, Kuihao Yan, Tianrui Zhang, Zixuan Qiu, Chao Lin, Zixiu Lin, Zixuan Chen, Zelin Xiao, Tiankuo Yao, Jiawei Hong, Zixuan Chen, Dan Xu, Lewei Lu, Wenjie Wang, Xin Lou

agents nlp benchmarks

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