Web$hell is a unified interface that consolidates various web access methods—search, browser actions, and filesystem interactions—into a single model for large language model agents. It uses a WebContract schema to compile live webpages into addressable information and action objects, which are stored in a WebCache and manipulated through shared operations. Experiments across search, navigation, and research tasks show that Web$hell delivers strong performance at lower cost, highlighting the role of interface design in enhancing agentic web access.
By Sunghwan Kim, Deogyong Kim, Sangam Lee, Donguk Kwon, Dongha Lee
arXiv:2602. 17245v2 Announce Type: replace Abstract: This position paper argues that building a reliable agentic Web requires shifting from low-level interaction primitives to typed actions supported by a semantic layer.
By Linxi Jiang, Rui Xi, Zhijie Liu, Shuo Chen, Zhiqiang Lin, Suman Nath
arXiv:2604. 06367v2 Announce Type: replace-cross Abstract: Web agents automate browser tasks, ranging from simple form completion to complex workflows like ordering groceries.
By Guruprasad Viswanathan Ramesh, Asmit Nayak, Basieem Siddique, Kassem Fawaz
arXiv:2606. 13385v1 Announce Type: cross Abstract: Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences.
By Zihao Wang, Yiming Li, Yutong Wu, Zheyu Liu, Kangjie Chen, Fok Kar Wai, Pin-Yu Chen, Vrizlynn L. L. Thing, Bo Li, Dacheng Tao, Tianwei Zhang
arXiv:2609.13548v1 Announce Type: new
Abstract: Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections ca...
By Xinyun Cao, Adriana Szekeres, Fazle Elahi Faisal
arXiv:2506. 01952v2 Announce Type: replace-cross Abstract: Powered by large language models (LLMs), web browsing agents operate graphical user interfaces in a human-like manner, offering a transparent and general framework for automating web-based tasks.
By Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira, Atsuki Sato, Tatsumi Sunada, Shota Onohara, Hiromasa Yamanishi, Mashiro Toyooka, Kunato Nishina, Ryoma Maeda, Kiyoharu Aizawa, Toshihiko Yamasaki
arXiv:2608.30530v1 Announce Type: new
Abstract: VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that judges it, and visual plausibility...
By Jiajun Wu, Jian Yang, Yaxin Du, Wei Zhang, Haowen Wang, Junhang Cheng, Yuxuan Zhang, Tuney Zheng, Xianglong Liu, Ming Zhou
arXiv:2606. 15673v1 Announce Type: new Abstract: Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improvement.
By Jiwan Chung, JiHyuk Byun, Vibhav Vineet, Seon Joo Kim
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
arXiv:2606. 17645v1 Announce Type: new Abstract: Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action.
By Shiqi He, Yue Cui, Feijie Wu, Xinyu Ma, Jiaheng Lu, Yaliang Li, Bolin Ding, Mosharaf Chowdhury
arXiv:2606. 17929v1 Announce Type: new Abstract: Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full cost again.
By Bojie Li
arXiv:2608. 06704v1 Announce Type: new Abstract: Delegating a web task involves more than asking a question; it requires transferring a policy: what to verify, how to handle uncertainty, which preferences matter, and when to stop.
By Zhi Li, Tao Zhou, Yeqing Li, Eugene Ie, Demetri Terzopoulos