EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools

TL;DR

EigentSearch-Q+ introduces structured reasoning tools to improve deep research agents by guiding web search, enhancing evidence aggregation, and increasing accuracy across multiple benchmarks.

Contribution

The paper presents Q+, a novel set of query and evidence processing tools that make web search more deliberate and integrated into deep research agents.

Findings

Q+ improves Eigent's accuracy by up to 3.8 percentage points.

Q+ produces more coherent tool-calling trajectories.

Case studies show better evidence handling and search progress monitoring.

Abstract

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.