@inproceedings{yang-etal-2026-scaffoldagent,
title = "{S}caffold{A}gent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research",
author = "Yang, Zhibang and
Jiang, Xinke and
Xiao, Yuzhen and
Zhang, Ruizhe and
Fang, Yue and
Wan, Xinfei and
Song, Zhengxing and
Liu, Yuxuan and
Huang, Yuheng and
Zhao, Junfeng and
Wang, Yasha and
Chu, Xu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.887/",
pages = "20454--20474",
ISBN = "979-8-89176-459-0",
abstract = "Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a structural scaffold that coordinates retrieval, evidence organization, and generation. However, existing methods either fix the outline before writing or refine it with local heuristics, leading to scaffold drift under continuous information accumulation and delayed feedback for evaluating outline modifications. We propose ScaffoldAgent, a utility-guided dynamic outline optimization framework for OEDR. ScaffoldAgent models outline evolution as a structured decision process with three operations: Expansion, Contraction, and Revision, enabling controlled updates to the report scaffold. It further introduces a utility-guided feedback mechanism that estimates the downstream value of each outline operation from retrieval gain, structural coherence, and trial-generation quality. The resulting utility signal guides node selection, operation scheduling, and termination during inference. Experiments on DeepResearch Bench and DeepResearch Gym show that ScaffoldAgent consistently improves long-form report generation and factual grounding over existing deep research agents."
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<abstract>Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a structural scaffold that coordinates retrieval, evidence organization, and generation. However, existing methods either fix the outline before writing or refine it with local heuristics, leading to scaffold drift under continuous information accumulation and delayed feedback for evaluating outline modifications. We propose ScaffoldAgent, a utility-guided dynamic outline optimization framework for OEDR. ScaffoldAgent models outline evolution as a structured decision process with three operations: Expansion, Contraction, and Revision, enabling controlled updates to the report scaffold. It further introduces a utility-guided feedback mechanism that estimates the downstream value of each outline operation from retrieval gain, structural coherence, and trial-generation quality. The resulting utility signal guides node selection, operation scheduling, and termination during inference. Experiments on DeepResearch Bench and DeepResearch Gym show that ScaffoldAgent consistently improves long-form report generation and factual grounding over existing deep research agents.</abstract>
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%0 Conference Proceedings
%T ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
%A Yang, Zhibang
%A Jiang, Xinke
%A Xiao, Yuzhen
%A Zhang, Ruizhe
%A Fang, Yue
%A Wan, Xinfei
%A Song, Zhengxing
%A Liu, Yuxuan
%A Huang, Yuheng
%A Zhao, Junfeng
%A Wang, Yasha
%A Chu, Xu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yang-etal-2026-scaffoldagent
%X Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a structural scaffold that coordinates retrieval, evidence organization, and generation. However, existing methods either fix the outline before writing or refine it with local heuristics, leading to scaffold drift under continuous information accumulation and delayed feedback for evaluating outline modifications. We propose ScaffoldAgent, a utility-guided dynamic outline optimization framework for OEDR. ScaffoldAgent models outline evolution as a structured decision process with three operations: Expansion, Contraction, and Revision, enabling controlled updates to the report scaffold. It further introduces a utility-guided feedback mechanism that estimates the downstream value of each outline operation from retrieval gain, structural coherence, and trial-generation quality. The resulting utility signal guides node selection, operation scheduling, and termination during inference. Experiments on DeepResearch Bench and DeepResearch Gym show that ScaffoldAgent consistently improves long-form report generation and factual grounding over existing deep research agents.
%U https://aclanthology.org/2026.emnlp-main.887/
%P 20454-20474
Markdown (Informal)
[ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research](https://aclanthology.org/2026.emnlp-main.887/) (Yang et al., EMNLP 2026)
ACL
- Zhibang Yang, Xinke Jiang, Yuzhen Xiao, Ruizhe Zhang, Yue Fang, Xinfei Wan, Zhengxing Song, Yuxuan Liu, Yuheng Huang, Junfeng Zhao, Yasha Wang, and Xu Chu. 2026. ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20454–20474, Budapest, Hungary. Association for Computational Linguistics.