@inproceedings{sun-etal-2026-agendaflow,
title = "{A}genda{F}low: Task-Adaptive Meeting Protocols for Multi-Agent {LLM} Reasoning",
author = "Sun, Ming and
Wang, Yin and
Zhang, Yanlin",
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.322/",
pages = "6809--6835",
ISBN = "979-8-89176-462-0",
abstract = "Multi-agent large language model (LLM) systems often rely on independent solvers, critique, debate, or answer aggregation. While these designs increase diversity and feedback, they do not necessarily create effective group reasoning: information may be poorly shared, expertise underused, discussions may drift, and consensus may emerge before unresolved issues are examined. We introduce AgendaFlow, a meeting-science-inspired framework that treats multi-agent reasoning as a structured meeting rather than parallel answering or generic debate. AgendaFlow routes each task to a suitable agenda, instantiates complementary functional roles, and uses a moderator to manage phases, speaking order, shared artifacts, unresolved issues, and completion checks. This enables agents to pool information, surface constraints, compare alternatives, repair errors, and converge through a managed collective decision process. Across five reasoning, planning, and rule- or evidence-based benchmarks, AgendaFlow consistently outperforms single-agent, sampling-based, debate-style, and multi-agent orchestration baselines. Ablations, token-matched comparisons, and process-level analyses suggest that the gains come from meeting-level organization rather than agent diversity or additional computation alone, highlighting meeting design as a missing dimension in multi-agent LLM systems. Code is available at: https://github.com/sunming-code/agendaflow."
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<abstract>Multi-agent large language model (LLM) systems often rely on independent solvers, critique, debate, or answer aggregation. While these designs increase diversity and feedback, they do not necessarily create effective group reasoning: information may be poorly shared, expertise underused, discussions may drift, and consensus may emerge before unresolved issues are examined. We introduce AgendaFlow, a meeting-science-inspired framework that treats multi-agent reasoning as a structured meeting rather than parallel answering or generic debate. AgendaFlow routes each task to a suitable agenda, instantiates complementary functional roles, and uses a moderator to manage phases, speaking order, shared artifacts, unresolved issues, and completion checks. This enables agents to pool information, surface constraints, compare alternatives, repair errors, and converge through a managed collective decision process. Across five reasoning, planning, and rule- or evidence-based benchmarks, AgendaFlow consistently outperforms single-agent, sampling-based, debate-style, and multi-agent orchestration baselines. Ablations, token-matched comparisons, and process-level analyses suggest that the gains come from meeting-level organization rather than agent diversity or additional computation alone, highlighting meeting design as a missing dimension in multi-agent LLM systems. Code is available at: https://github.com/sunming-code/agendaflow.</abstract>
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%0 Conference Proceedings
%T AgendaFlow: Task-Adaptive Meeting Protocols for Multi-Agent LLM Reasoning
%A Sun, Ming
%A Wang, Yin
%A Zhang, Yanlin
%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 sun-etal-2026-agendaflow
%X Multi-agent large language model (LLM) systems often rely on independent solvers, critique, debate, or answer aggregation. While these designs increase diversity and feedback, they do not necessarily create effective group reasoning: information may be poorly shared, expertise underused, discussions may drift, and consensus may emerge before unresolved issues are examined. We introduce AgendaFlow, a meeting-science-inspired framework that treats multi-agent reasoning as a structured meeting rather than parallel answering or generic debate. AgendaFlow routes each task to a suitable agenda, instantiates complementary functional roles, and uses a moderator to manage phases, speaking order, shared artifacts, unresolved issues, and completion checks. This enables agents to pool information, surface constraints, compare alternatives, repair errors, and converge through a managed collective decision process. Across five reasoning, planning, and rule- or evidence-based benchmarks, AgendaFlow consistently outperforms single-agent, sampling-based, debate-style, and multi-agent orchestration baselines. Ablations, token-matched comparisons, and process-level analyses suggest that the gains come from meeting-level organization rather than agent diversity or additional computation alone, highlighting meeting design as a missing dimension in multi-agent LLM systems. Code is available at: https://github.com/sunming-code/agendaflow.
%U https://aclanthology.org/2026.findings-emnlp.322/
%P 6809-6835
Markdown (Informal)
[AgendaFlow: Task-Adaptive Meeting Protocols for Multi-Agent LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.322/) (Sun et al., Findings 2026)
ACL