mlsanityData to 7 Oct 2026

Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents

arXiv 2608.02097 ↗First posted 3 Aug 2026cs.AI cs.IRcited by 0

Not on the map. Why?

Abstract (arXiv)

Search agents now answer questions that take dozens of searches to settle, yet how such an agent reads a page has drawn far less attention than how it finds one. Nearly all of them use one of two document interfaces, and both tie a page to the moment it is opened. Visit-and-read injects a reading of the page into the message history at fetch time, fixing that reading before the agent knows which fact it will need. Stateful browsing instead extracts on demand from the page in hand, but holds one page at a time and releases it as soon as the agent opens another. Either way, a page that turns out to matter many turns later has to be fetched and rendered into context all over again. We propose Fetch-then-Explore, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later. Selection becomes almost free, extraction can wait until the agent knows what to look for and be repeated as its hypothesis sharpens, and pages are not released when the agent moves on, so evidence accumulates across the trajectory. In a unified ReAct harness with fixed search, we compare Fetch-then-Explore against snippet-only, visit-and-read, and browsing baselines on two open-web benchmarks, BrowseComp and WideSearch, across three agent backbones. It leads BrowseComp accuracy at every backbone and generally matches or exceeds the baselines on WideSearch, and a behavioral analysis traces the gains to the workspace's defining move: returning to a page after leaving it, which it does far more than any transient interface, so evidence missed on a first pass can still be recovered later.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 39 references that are in the map. First posted on arXiv 3 Aug 2026.

Builds on

31 of 39 references.

References · 31

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  6. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
  7. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  8. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  9. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  10. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
  11. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  12. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
19 more
  1. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
  2. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 268
  3. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 183
  4. 2025BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinesecited by 139
  5. 2025ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarizationcited by 132
  6. 2025AgentFold: Long-Horizon Web Agents with Proactive Context Managementcited by 95
  7. 2025MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scalingcited by 72
  8. 2025DeepAgent: A General Reasoning Agent with Scalable Toolsetscited by 69
  9. 2025WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agentscited by 66
  10. 2025A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challengescited by 58
  11. 2026Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interactioncited by 33
  12. 2026OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Datacited by 32
  13. 2026OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesiscited by 32
  14. 2025IterResearch: Rethinking Long-Horizon Agents with Interaction Scalingcited by 22
  15. 2026Is Grep All You Need? How Agent Harnesses Reshape Agentic Searchcited by 17
  16. 2026GrepSeek: Training Search Agents for Direct Corpus Interactioncited by 10
  17. 2026Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnessescited by 6
  18. 2026FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agentscited by 5
  19. 2026EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledgecited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Towards Retrieving Interaction Spaces for Agentic Search
  2. 2026A New Role for Relevance: Guiding Corpus Interaction in Agentic Search
  3. 2026Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation
  4. 2026HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents
  5. 2026Programmatic Search Agents: Extending Agentic Search Beyond Query Reformulation
  6. 2026Similar Accuracy, Unequal Evidence: Search APIs as Decision Surfaces for Tool-Using Agents
  7. 2026LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
  8. 2026LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents