Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.07927 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910171850080256 |
|---|---|
| author | Zhang, Boer Wu, Mingyan Zhou, Dongzhuoran Zhu, Yuqicheng Fan, Wendong Zhang, Puzhen Ding, Zifeng Li, Guohao He, Yuan |
| author_facet | Zhang, Boer Wu, Mingyan Zhou, Dongzhuoran Zhu, Yuqicheng Fan, Wendong Zhang, Puzhen Ding, Zifeng Li, Guohao He, Yuan |
| contents | Deep research requires reasoning over web evidence to answer open-ended questions, and it is a core capability for AI agents. Yet many deep research agents still rely on implicit, unstructured search behavior that causes redundant exploration and brittle evidence aggregation. Motivated by Anthropic's "think" tool paradigm and insights from the information-retrieval literature, we introduce Q+, a set of query and evidence processing tools that make web search more deliberate by guiding query planning, monitoring search progress, and extracting evidence from long web snapshots. We integrate Q+ into the browser sub-agent of Eigent, an open-source, production-ready multi-agent workforce for computer use, yielding EigentSearch-Q+. Across four benchmarks (SimpleQA-Verified, FRAMES, WebWalkerQA, and XBench DeepSearch), Q+ improves Eigent's browser agent benchmark-size-weighted average accuracy by 3.0, 3.8, and 0.6 percentage points (pp) for GPT-4.1, GPT-5.1, and Minimax M2.5 model backends, respectively. Case studies further suggest that EigentSearch-Q+ produces more coherent tool-calling trajectories by making search progress and evidence handling explicit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07927 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools Zhang, Boer Wu, Mingyan Zhou, Dongzhuoran Zhu, Yuqicheng Fan, Wendong Zhang, Puzhen Ding, Zifeng Li, Guohao He, Yuan Artificial Intelligence Deep research requires reasoning over web evidence to answer open-ended questions, and it is a core capability for AI agents. Yet many deep research agents still rely on implicit, unstructured search behavior that causes redundant exploration and brittle evidence aggregation. Motivated by Anthropic's "think" tool paradigm and insights from the information-retrieval literature, we introduce Q+, a set of query and evidence processing tools that make web search more deliberate by guiding query planning, monitoring search progress, and extracting evidence from long web snapshots. We integrate Q+ into the browser sub-agent of Eigent, an open-source, production-ready multi-agent workforce for computer use, yielding EigentSearch-Q+. Across four benchmarks (SimpleQA-Verified, FRAMES, WebWalkerQA, and XBench DeepSearch), Q+ improves Eigent's browser agent benchmark-size-weighted average accuracy by 3.0, 3.8, and 0.6 percentage points (pp) for GPT-4.1, GPT-5.1, and Minimax M2.5 model backends, respectively. Case studies further suggest that EigentSearch-Q+ produces more coherent tool-calling trajectories by making search progress and evidence handling explicit. |
| title | EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.07927 |