@inproceedings{zhang-etal-2026-dyco,
title = "{D}y{C}o: Dynamic Cognitive Role Coordination for Open-Ended Multi-Agent Research Synthesis and Verification",
author = "Zhang, Zhiyu and
Wu, Leheng and
Mo, Yijun and
Zhong, Mingchu and
Zhang, Yulin and
XI, Chen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1734/",
pages = "37087--37110",
ISBN = "979-8-89176-462-0",
abstract = "Most multi-agent systems rely on fairly fixed collaboration patterns: roles, interaction paths, and task decomposition are set in advance. This works poorly for open-ended, literature-grounded research synthesis, where uncertainty changes what needs to be explored, challenged, and verified. We introduce DyCo, a framework for dynamic cognitive role coordination that combines cognitive role partitioning, dynamic teaming, and Exploration{--}Verification Alternation (EVA) to balance idea generation with validation. We use the Myers{--}Briggs Type Indicator (MBTI) only as a structured way to specify role priors under controlled conditions, not as a claim about human personality. On DeepResearch Bench, a diverse DyCo configuration reaches a RACE score of 42.75, outperforming the strongest multi-agent baseline by 4.6{\%} ($p<0.01$, $d=0.42$), and a budget-matched non-MBTI control shows that this gain is not explained by additional computation. Extended matched-role controls on all 50 evaluation tasks indicate that most of the improvement comes from rich, structured role priors combined with verification-oriented coordination rather than the MBTI taxonomy itself, and the diverse configuration is not significantly better than the strongest homogeneous one. We further present DyCo-Efficient, which removes dynamic teaming and preserves most of the observed quality at roughly one thirteenth of the token cost, positioning Full DyCo as an optional high-budget configuration."
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<abstract>Most multi-agent systems rely on fairly fixed collaboration patterns: roles, interaction paths, and task decomposition are set in advance. This works poorly for open-ended, literature-grounded research synthesis, where uncertainty changes what needs to be explored, challenged, and verified. We introduce DyCo, a framework for dynamic cognitive role coordination that combines cognitive role partitioning, dynamic teaming, and Exploration–Verification Alternation (EVA) to balance idea generation with validation. We use the Myers–Briggs Type Indicator (MBTI) only as a structured way to specify role priors under controlled conditions, not as a claim about human personality. On DeepResearch Bench, a diverse DyCo configuration reaches a RACE score of 42.75, outperforming the strongest multi-agent baseline by 4.6% (p<0.01, d=0.42), and a budget-matched non-MBTI control shows that this gain is not explained by additional computation. Extended matched-role controls on all 50 evaluation tasks indicate that most of the improvement comes from rich, structured role priors combined with verification-oriented coordination rather than the MBTI taxonomy itself, and the diverse configuration is not significantly better than the strongest homogeneous one. We further present DyCo-Efficient, which removes dynamic teaming and preserves most of the observed quality at roughly one thirteenth of the token cost, positioning Full DyCo as an optional high-budget configuration.</abstract>
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%0 Conference Proceedings
%T DyCo: Dynamic Cognitive Role Coordination for Open-Ended Multi-Agent Research Synthesis and Verification
%A Zhang, Zhiyu
%A Wu, Leheng
%A Mo, Yijun
%A Zhong, Mingchu
%A Zhang, Yulin
%A XI, Chen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-dyco
%X Most multi-agent systems rely on fairly fixed collaboration patterns: roles, interaction paths, and task decomposition are set in advance. This works poorly for open-ended, literature-grounded research synthesis, where uncertainty changes what needs to be explored, challenged, and verified. We introduce DyCo, a framework for dynamic cognitive role coordination that combines cognitive role partitioning, dynamic teaming, and Exploration–Verification Alternation (EVA) to balance idea generation with validation. We use the Myers–Briggs Type Indicator (MBTI) only as a structured way to specify role priors under controlled conditions, not as a claim about human personality. On DeepResearch Bench, a diverse DyCo configuration reaches a RACE score of 42.75, outperforming the strongest multi-agent baseline by 4.6% (p<0.01, d=0.42), and a budget-matched non-MBTI control shows that this gain is not explained by additional computation. Extended matched-role controls on all 50 evaluation tasks indicate that most of the improvement comes from rich, structured role priors combined with verification-oriented coordination rather than the MBTI taxonomy itself, and the diverse configuration is not significantly better than the strongest homogeneous one. We further present DyCo-Efficient, which removes dynamic teaming and preserves most of the observed quality at roughly one thirteenth of the token cost, positioning Full DyCo as an optional high-budget configuration.
%U https://aclanthology.org/2026.findings-emnlp.1734/
%P 37087-37110
Markdown (Informal)
[DyCo: Dynamic Cognitive Role Coordination for Open-Ended Multi-Agent Research Synthesis and Verification](https://aclanthology.org/2026.findings-emnlp.1734/) (Zhang et al., Findings 2026)
ACL