AgentFold: Long-Horizon Web Agents with Proactive Context Management
On the map: LLM agents and tool use → Agent memory systems
Abstract (arXiv)
LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, a novel agent paradigm centered on proactive context management, inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as a dynamic cognitive workspace to be actively sculpted, rather than a passive log to be filled. At each step, it learns to execute a `folding' operation, which manages its historical trajectory at multiple scales: it can perform granular condensations to preserve vital, fine-grained details, or deep consolidations to abstract away entire multi-step sub-tasks. The results on prominent benchmarks are striking: with simple supervised fine-tuning (without continual pre-training or RL), our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or matches open-source models of a dramatically larger scale, such as the DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like OpenAI's o4-mini.
Key facts. Cited by 103 papers in the map (data through 14 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 30 of its 39 references that are in the map. First posted on arXiv 28 Oct 2025.
Builds on
30 of 39 references.
Closest ancestors · 3
Same direction, newest first
- 2025MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents18 Jun
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
- 2025A-MEM: Agentic Memory for LLM Agents17 Feb
Other references · 27
- 2025Qwen3 Technical Reportcited by 8,820
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- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
- 2023GAIA: a benchmark for General AI Assistantscited by 1,128
- 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
- 2025GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Modelscited by 467
- 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
- 2025MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agentcited by 246
- 2025WebSailor: Navigating Super-human Reasoning for Web Agentcited by 229
- 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
- 2025WebDancer: Towards Autonomous Information Seeking Agencycited by 188
15 more
- 2025Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RLcited by 150
- 2025BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinesecited by 145
- 2025A Survey of Context Engineering for Large Language Modelscited by 126
- 2025WebShaper: Agentically Data Synthesizing via Information-Seeking Formalizationcited by 121
- 2025WebExplorer: Explore and Evolve for Training Long-Horizon Web Agentscited by 92
- 2025MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Modelscited by 73
- 2025WideSearch: Benchmarking Agentic Broad Info-Seekingcited by 69
- 2025WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agentscited by 68
- 2025WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learningcited by 61
- 2025DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RLcited by 55
- 2025MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systemscited by 50
- 2025SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?cited by 47
- 2024Memory3: Language Modeling with Explicit Memorycited by 40
- 2025DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learningcited by 23
- 2025BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Paircited by 16
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Citing papers per year: 2025: 7; 2026: 96.
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