Don’t Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination

Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit, Jiaxin Zhang, Vaibhav Vats, Yu Li, XIANGYU PENG, Chien-Sheng Wu

Keywords: Enterprise Deep Research, Planning, Agents

TL;DR: We propose a scalable Enterprise Deep Research architecture that improves decision-ready reports through coverage-driven task decomposition, dependency-guided context control, and evidence-based completion to prevent premature stopping.

Abstract: Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a \textit{scalable} Enterprise Deep Research (EDR) architecture to address these failures. Our system (i) decomposes requests into coverage-driven objectives via outline generation with reflection, (ii) localizes context with dependency-guided execution and explicit information sharing, and (iii) enforces evidence-based completion criteria so agents iteratively collect information until sufficiency conditions are met. We evaluate on an internal sales-enablement task and the public DeepResearch Bench benchmark, where our proposed system design achieves the strongest overall performance against competitive deep-research baselines. The results show that dependency-controlled context and explicit evidence sufficiency criteria reduce premature stopping and improve the consistency and depth of enterprise research outputs. We will make our code and permitted data publicly available.

Submission Type: Emerging

Submission Number: 412