arXiv AI

Learning Simple Test-Time Environments for LLM Web Agents

The paper introduces Test-Time Environment Decomposition (TTED), a label‑free learning method that allows large language model agents to break down complex web environment observations into simpler sub‑modules during inference. By learning from experience within these sub‑environments, agents can compose the gained knowledge to improve performance in the full environment. Experiments on synthetic and realistic benchmarks show that this approach enhances compositional generalization and boosts real‑world web automation tasks.

arXiv AI

Jun 6

AdaMEM: Test-Time Adaptive Memory for Language Agents

arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.

By Yunxiang Zhang, Yiheng Li, Ali Payani, Lu Wang

agents fine-tuning

arXiv AI

Jul 1

InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training

arXiv:2601. 04126v3 Announce Type: replace-cross Abstract: GUI agents that interact with graphical interfaces on behalf of users represent a promising direction for practical AI assistants.

By Ziyun Zhang, Zezhou Wang, Xiaoyi Zhang, Zongyu Guo, Jiahao Li, Bin Li, Yan Lu

llms agents reinforcement-learning

arXiv AI

Aug 18

Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents

arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.

By Tianxin Wei, Zhan Shi, Minhua Lin, Bing He, Zewen Liu, Yisi Sang, Yuanchen Bei, Xuying Ning, Jiaru Zou, Ting-Wei Li, Xiao Lin, Yanjun Zhao, Chi Wang, Benoit Dumoulin, Dakuo Wang, Jingrui He, Hanqing Lu

llms agents benchmarks

arXiv AI

Jul 16

Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies

arXiv:2604. 00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time.

By Zhanzhi Lou, Hui Chen, Yibo Li, Qian Wang, Bryan Hooi

agents

arXiv AI

Jun 3

The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios

arXiv:2601. 08173v2 Announce Type: replace Abstract: The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static environments, overlooking robustness for stochastic real-world deployment.

By Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, Botian Shi

llms agents multimodal benchmarks safety

arXiv AI

Jul 2

Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use

arXiv:2607. 01084v1 Announce Type: new Abstract: While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics.

By Song-Lin Lv, Weiming Wu, Rui Zhu, Zi-Jian Cheng, Lan-Zhe Guo

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 4

Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents

arXiv:2606. 04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments.

By Bo Mao, Jie Zhou, Yutao Yang, Xin Li, Xian Wei, Qin Chen, Xingjiao Wu, Liang He

llms agents reinforcement-learning

arXiv AI

Sep 1

WebXSkill: Skill Learning for Autonomous Web Agents

arXiv:2604.13318v2 Announce Type: replace Abstract: Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap...

By Zhaoyang Wang, Qianhui Wu, Xuchao Zhang, Chaoyun Zhang, Wenlin Yao, Fazle Elahi Faisal, Baolin Peng, Si Qin, Suman Nath, Qingwei Lin, Chetan Bansal, Dongmei Zhang, Saravan Rajmohan, Jianfeng Gao, Huaxiu Yao

llms agents

arXiv AI

Jun 10

Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution

arXiv:2606. 10917v1 Announce Type: new Abstract: Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization.

By Xucong Wang, Ziyu Ma, Shidong Yang, Tongwen Huang, Pengkun Wang, Yong Wang, Xiangxiang Chu

llms agents benchmarks safety

arXiv AI

Jun 9

Exploring Autonomous Agentic Data Engineering for Model Specialization

arXiv:2605. 30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data.

By Yujie Luo, Xiangyuan Ru, Jingsheng Zheng, Jingjing Wang, Yuqi Zhu, Jintian Zhang, Runnan Fang, Kewei Xu, Ye Liu, Zheng Wei, Jiang Bian, Zang Li, Shumin Deng

llms agents data-engineering

arXiv AI

Aug 25

Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning

arXiv:2608.21898v1 Announce Type: new Abstract: Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding...

By Chenghao Zhang, Canran Xiao, SaiSai Hu, Dan Roth

llms agents reinforcement-learning

arXiv AI

Jul 2

AGI Maze as a Benchmark Framework for World-Modeling Agents

arXiv:2607. 00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an external world.

By Alexey Potapov

llms agents benchmarks

Related stories