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

arXiv cs.CL / 4/29/2026

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Key Points

The paper proposes a scalable Enterprise Deep Research (EDR) architecture aimed at making deep-research outputs more decision-ready for enterprises.
It improves coverage by decomposing requests into outline-driven, reflection-based objectives that focus on what information is missing.
It reduces context overload by localizing information flows using dependency-guided execution and explicit information sharing among agents.
It prevents premature termination by using evidence-based completion criteria, iteratively collecting information until predefined sufficiency conditions are satisfied.
In evaluations on an internal sales enablement task and the DeepResearch Bench, the proposed design delivers the strongest overall performance and better consistency and depth than baseline approaches.

Abstract

Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a 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 compared with 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.