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
TRIAGE introduces a three-level routing framework for Large Language Model agents that reduces token consumption by reusing historical execution trajectories. The system classifies queries into direct reuse, skill substitution, and full ReAct levels, achieving significant token savings in large-scale security monitoring and cross-domain benchmarks. An automatic skill extraction mechanism further refines reusable patterns, creating a positive feedback loop that improves efficiency over time.
By Ruocan Wei
arXiv:2608. 16502v1 Announce Type: new Abstract: Large-scale agents increasingly rely on retrieval to access external capabilities.
By Yiqi Liu, Joseph James, Yang Wang, Chenghao Xiao, Chenghua Lin
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:2610.16319v1 Announce Type: new
Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluat...
By Zijian Cai, Haowei Liu, Kailai Chen, Lin Peng, Cong Wan, Ziqi Zhang, Chunfeng Yuan, Bing Li, Weiming Hu, Haiyang Xu
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:2609.36488v1 Announce Type: new
Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
By Dongchan Shin, Xing Han L\`u, Jiaqi Deng, Jay Gala, Tom\'as Vergara Browne, Jaewon Moon, Fengyuan Liu, Alexandre Drouin, Siva Reddy, Alexandre Lacoste
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
Toollery is a training‑free framework that compresses candidate lists for large language model agents, enabling efficient selection from thousands of skills and tools. It generates user‑intent queries from each skill or tool specification, builds a retrieval index, and limits online selection to a compact top‑k set before the LLM makes its final decision. Evaluations on the SkillRouter benchmark, BFCL‑V4, and a proprietary smart‑cockpit dataset show that Toollery improves recall and end‑to‑end selection while keeping selection costs bounded.
By Xiangxi Tian, Ran Guan
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
By Pavan C Shekar, Aswanth Krishnan
arXiv:2610.16402v1 Announce Type: new
Abstract: Web application development often proceeds through successive requests that extend an existing implementation. Yet web-generation benchmarks primarily...
By David Nukrai, Bar Alon, Nativ Levy, Ido Andrew Atad, Ido Cohen, Itamar Zimerman, Lior Wolf, Eli David, Ilan Naiman
arXiv:2610.23707v1 Announce Type: new
Abstract: A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on...
By Sam Partee, River Stevens, Thierry Damiba, Valerie Fanelle