Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models. |
| Approach: | They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents. |
| Outcome: | The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training. |
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| Challenge: | Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks. |
| Approach: | They introduce a framework that enhances large language model reasoning by integrating external tool-using agents. |
| Outcome: | The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research. |
Mango: Multi-Agent Web Navigation via Global-View Optimization (2026.acl-long)
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| Challenge: | Existing web agents typically begin exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures. |
| Approach: | They propose a multi-agent web navigation method that leverages the website structure to dynamically determine optimal starting points. |
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VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks (2024.acl-long)
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Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Russ Salakhutdinov, Daniel Fried
| Challenge: | Existing benchmarks focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. |
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Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory (2026.acl-long)
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| Challenge: | Existing methods for embodied reasoning are coarse-grained and expensive . branch-and-browse framework enables fine-grounded, memory-guided, and efficient multi-branch reasoning. |
| Approach: | They propose a framework that unifies structured reasoning-acting, contextual memory, and efficient execution. |
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WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)
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| Challenge: | Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning. |
| Approach: | They propose a task-uncertainty-driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments. |
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AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks? (2024.emnlp-main)
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| Challenge: | Current language models and retrieval-augmented LMs are limited in their ability to perform tasks on the web. |
| Approach: | They propose a benchmark to evaluate language agents built on top of language models . they propose 'AssistantBench' which includes 214 tasks that can be automatically evaluated . |
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AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)
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Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks. |
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Beyond Browsing: API-Based Web Agents (2025.findings-acl)
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| Challenge: | Existing web agents use browsers to facilitate human activities such as online shopping, online planning, and other work-related tasks. |
| Approach: | They propose to use web browsers as an interface to interact with online content through application programming interfaces (APIs) they propose to call APIs and use Hybrid Agents to perform online tasks. |
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GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)
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Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey, Yilun Zhou, Muhao Chen, Jonathan May, Chien-Sheng Wu
| Challenge: | Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process . |
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RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems (2026.findings-acl)
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Jiacheng Liu, Zichen Tang, Zhongjun Yang, Xinyi Hu, Xueyuan Lin, Linwei Jia, Ruofei Bai, Rongjin Li, Shiyao Peng, Haocheng Gao, Haihong E
| Challenge: | Existing tools to generate structured content for research tasks are limited in their ability to generate high-quality roadmaps. |
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