Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration

Challenge:	Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models.
Approach:	They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration.
Outcome:	The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark.

Similar Papers

Overview

Challenge

Approach

Outcome

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Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury

Responsible & Trustworthy NLP

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WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks (2026.findings-acl)

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Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang, Yujia Bao

Information Extraction & Text Mining

Challenge:	Large-language-model (LLM) agents are competent at straightforward web tasks, but struggle with complex tasks.
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R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory (2025.acl-long)

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Tenghao Huang, Kinjal Basu, Ibrahim Abdelaziz, Pavan Kapanipathi, Jonathan May, Muhao Chen

Challenge:	Existing methods for web agents struggle with efficient navigation and action execution due to limited visibility and understanding of web structures.
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WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)

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Lingfeng Zhang, Yongan Sun, Jinpeng Hu, Hui Ma, Ying Yang, Kuien Liu, Zenglin Shi, Meng Wang

Reasoning

Challenge:	Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning.
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On the Multi-turn Instruction Following for Conversational Web Agents (2024.acl-long)

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Yang Deng, Xuan Zhang, Wenxuan Zhang, Yifei Yuan, See-Kiong Ng, Tat-Seng Chua

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within web-based environments.
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WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback (2025.findings-emnlp)

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Minda Hu, Tianqing Fang, Jianshu Zhang, Jun-Yu Ma, Zhisong Zhang, Jingyan Zhou, Hongming Zhang, Haitao Mi, Dong Yu, Irwin King

Reasoning

Challenge:	Web agents powered by Large Language Models lack the ability to perform in uncertain web environments.
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MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning (2026.findings-acl)

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Juexiang Ye, Xue Li, Yang Xinyu, Chengkai Huang, Lanshun Nie, Lina Yao, Dechen Zhan

Information Retrieval

Challenge:	Existing methods rely on unstructured retrieval or coarse abstractions, which lead to temporal conflicts, brittle reasoning, and limited traceability.
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Outcome:	The proposed framework significantly improves multi-hop and temporal reasoning accuracy while reducing input context length by over 95% compared to long-context baselines.

Browsing Like Human: A Multimodal Web Agent with Experiential Fast-and-Slow Thinking (2025.acl-long)

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Haohao Luo, Jiayi Kuang, Wei Liu, Ying Shen, Jian Luan, Yang Deng

Multimodality

Challenge:	Existing web agents lack visual perception, planning, and memory abilities, but their reasoning process is deviate from human cognition.
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Mango: Multi-Agent Web Navigation via Global-View Optimization (2026.acl-long)

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Weixi Tong, Yifeng Di, Tianyi Zhang

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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An Efficient Context-Dependent Memory Framework for LLM-Centric Agents (2025.naacl-industry)

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Pengyu Gao, Jinming Zhao, Xinyue Chen, Long Yilin

Green & Sustainable NLP

Challenge:	a recent study has demonstrated that context-dependent memory encoding can help to retrieve key memory cues essential for problem-solving.
Approach:	They propose an efficient architecture miming human memory processes through multistage encoding, context-aware storage, and retrieval strategies for LLM-centric agents.
Outcome:	The proposed architecture surpasses state-of-the-art online LLM-centric approaches on two interactive decision-making benchmarks in the navigation and manipulation domain.