Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination

2026-04-27

AI Summary

This work addresses the challenges of uneven information coverage, context overload, and premature termination that commonly hinder high-quality decision-oriented enterprise research. To overcome these limitations, we propose a scalable research architecture that systematically enhances depth and consistency through outline-driven goal decomposition, dependency-guided context localization, explicit information sharing, and an evidence-sufficiency-based iterative termination strategy. Key innovations include reflective outline generation, dependency-aware context management, and evidence-aware termination criteria. Experimental results demonstrate that our approach significantly outperforms existing baselines on both an internal sales support task and the DeepResearch Bench benchmark, achieving state-of-the-art performance.

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.

Problem

• enterprise deep research
• premature stopping
• information coverage
• context explosion
• evidence sufficiency

Innovation

• Enterprise Deep Research
• Evidence-Aware Termination
• Dependency-Guided Execution
• Coverage-Driven Outline
• Controlled Information Flow