@inproceedings{zhang-etal-2026-deepsynth,
title = "{D}eep{S}ynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing",
author = "Zhang, Hongzhi and
Hu, Yuanze and
Zhang, Tinghai and
Fu, Jia and
Wang, Tao and
Jing, Junwei and
Fan, Zhaoxin and
Bi, Wei and
Tang, Ruiming and
Li, Han and
Zhou, Guorui and
Gai, Kun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1688/",
pages = "33805--33822",
ISBN = "979-8-89176-395-1",
abstract = "The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retrieval synthesis stage{---}where agents must digest massive amounts of context and consolidate fragmented evidence into coherent, long-form reports{---}remains under-evaluated due to the subjectivity of open-ended writing.To bridge this gap, we introduce DeepSynth-Eval, a benchmark designed to objectively evaluate information consolidation capabilities. We leverage high-quality survey papers as gold standards, reverse-engineer research requests, and construct Oracle Contexts from their bibliographies to isolate synthesis from retrieval noise. We propose a fine-grained evaluation protocol using General Checklists (for factual coverage) and Constraint Checklists (for structural organization), transforming subjective judgment into verifiable metrics. Experiments across 96 tasks reveal that synthesizing information from hundreds of references remains a significant challenge. Our results demonstrate that agentic ``plan-then-write'' workflows significantly outperform single-turn generation, effectively reducing hallucinations and improving adherence to complex structural constraints."
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<abstract>The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retrieval synthesis stage—where agents must digest massive amounts of context and consolidate fragmented evidence into coherent, long-form reports—remains under-evaluated due to the subjectivity of open-ended writing.To bridge this gap, we introduce DeepSynth-Eval, a benchmark designed to objectively evaluate information consolidation capabilities. We leverage high-quality survey papers as gold standards, reverse-engineer research requests, and construct Oracle Contexts from their bibliographies to isolate synthesis from retrieval noise. We propose a fine-grained evaluation protocol using General Checklists (for factual coverage) and Constraint Checklists (for structural organization), transforming subjective judgment into verifiable metrics. Experiments across 96 tasks reveal that synthesizing information from hundreds of references remains a significant challenge. Our results demonstrate that agentic “plan-then-write” workflows significantly outperform single-turn generation, effectively reducing hallucinations and improving adherence to complex structural constraints.</abstract>
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%0 Conference Proceedings
%T DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing
%A Zhang, Hongzhi
%A Hu, Yuanze
%A Zhang, Tinghai
%A Fu, Jia
%A Wang, Tao
%A Jing, Junwei
%A Fan, Zhaoxin
%A Bi, Wei
%A Tang, Ruiming
%A Li, Han
%A Zhou, Guorui
%A Gai, Kun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-deepsynth
%X The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retrieval synthesis stage—where agents must digest massive amounts of context and consolidate fragmented evidence into coherent, long-form reports—remains under-evaluated due to the subjectivity of open-ended writing.To bridge this gap, we introduce DeepSynth-Eval, a benchmark designed to objectively evaluate information consolidation capabilities. We leverage high-quality survey papers as gold standards, reverse-engineer research requests, and construct Oracle Contexts from their bibliographies to isolate synthesis from retrieval noise. We propose a fine-grained evaluation protocol using General Checklists (for factual coverage) and Constraint Checklists (for structural organization), transforming subjective judgment into verifiable metrics. Experiments across 96 tasks reveal that synthesizing information from hundreds of references remains a significant challenge. Our results demonstrate that agentic “plan-then-write” workflows significantly outperform single-turn generation, effectively reducing hallucinations and improving adherence to complex structural constraints.
%U https://aclanthology.org/2026.findings-acl.1688/
%P 33805-33822
Markdown (Informal)
[DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing](https://aclanthology.org/2026.findings-acl.1688/) (Zhang et al., Findings 2026)
ACL
- Hongzhi Zhang, Yuanze Hu, Tinghai Zhang, Jia Fu, Tao Wang, Junwei Jing, Zhaoxin Fan, Wei Bi, Ruiming Tang, Han Li, Guorui Zhou, and Kun Gai. 2026. DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing. In Findings of the Association for Computational Linguistics: ACL 2026, pages 33805–33822, San Diego, California, United States. Association for Computational Linguistics.