Explore Before Committing: Hypothesis-Guided Search for Deep Research Agents
cs.CL
Submitted: 2026-09-01
Updated: 2026-09-01
License: http://creativecommons.org/licenses/by/4.0/
The gist: Deep-research agents answer complex questions by interacting with search and browsing tools, yet they often search along a single evolving trajectory.
Terminology
Abstract
Deep-research agents answer complex questions by interacting with search and browsing tools, yet they often search along a single evolving trajectory. Our trajectory-level analysis reveals a common failure mode in which the agent may encounter an early search state with several plausible directions, but follow one direction before collecting enough comparative evidence. Once this happens, subsequent tool calls tend to reinforce the same path, increasing the chance of failure when the initial direction is misleading. We further find that successful trajectories reduce this risk through two behaviors: grounding vague exploration in concrete candidates and shifting directions when the current path is weak or incomplete. Based on these findings, we propose HypoSearch, which generates lightweight hypotheses as soft search hints, explores them through bounded independent branches, and compares branch-level evidence before commitment. Across four deep-research benchmarks and three backbone models, HypoSearch consistently outperforms single-trajectory search and standard parallel baselines, improving Qwen3.5-122B from 46.7 to 60.0 on BC-small while using fewer tool calls than five independent trajectories. A pilot supervised fine-tuning study further shows that these behavioral signals can curate compact training trajectories and reduce degradation from unfiltered data.
Sources
- Deep Research Agents: A Systematic Examination And Roadmap
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
- GPT-4 Technical Report
- Improving and Evaluating Open Deep Research Agents
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
- WebGPT: Browser-assisted question-answering with human feedback
- ReAct: Synergizing Reasoning and Acting in Language Models
- FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
- ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents
- Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Kimi K2.5: Visual Agentic Intelligence
- BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
- LongCat-Flash-Thinking-2601 Technical Report
- Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
- Scaling Test-time Compute for LLM Agents
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Qwen3 Technical Report
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering