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

新的EDR架构可防止企业深度研究过早停止

作者 PulseAugur 编辑部 · [2 个来源] · 2026-04-27 20:31

研究人员推出了一种新的企业深度研究（EDR）架构，旨在提高研究报告的质量和一致性。该系统通过将请求分解为可管理的目标来解决信息覆盖不完整和过早停止等常见问题。它通过大纲生成与反思、本地化上下文管理以及基于证据的完成标准来实现这一点，确保代理在得出结论前收集到足够的信息。 AI

影响提高了企业决策中人工智能驱动的研究输出的可靠性和深度。

排序理由这是一篇详细介绍企业深度研究新架构的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit, Jiaxin Zhang, Vaibhav Vats, Yu Li, Xiangyu Peng, Chien-Sheng Wu · 2026-04-29 04:00

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

arXiv:2604.24978v1 Announce Type: new 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…
arXiv cs.CL TIER_1 English(EN) · Chien-Sheng Wu · 2026-04-27 20:31

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

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 in…