@inproceedings{chakraborty-etal-2026-dealog,
title = "{D}e{ALOG}: Decentralized Multi-Agents Log-Mediated Reasoning Framework",
author = "Chakraborty, Abhijit and
Shekhar, Ashish Raj and
Agarwal, Shiven and
Gupta, Vivek",
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.923/",
doi = "10.18653/2026.findings-emnlp.923",
pages = "19470--19493",
ISBN = "979-8-89176-462-0",
abstract = "Complex multi-hop question answering over text, tables, and images requires integrating heterogeneous evidence with inspectable reasoning. Single-LLM chain-of-thought is opaque, while planner{--}executor pipelines concentrate faults in a central plan, propagating early errors. We introduce DeALOG, a decentralized, central-planner-free multi-agent framework whose specialized agents, i.e., Table, Context, Visual, Summarizing, and Verification, coordinate only by reading and appending to a shared, typed natural-language log as persistent memory, with a lightweight scheduler rather than a planner. Rather than competing on raw accuracy, DeALOG targets what a central planner forfeits: robustness, since no single plan concentrates errors and evidence stays globally visible, and transparent, since the append-only log yields a provenance-anchored, human-readable reasoning trace. We position decentralized log-mediated coordination as a robustness-and-transparency primitive for multimodal reasoning"
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<abstract>Complex multi-hop question answering over text, tables, and images requires integrating heterogeneous evidence with inspectable reasoning. Single-LLM chain-of-thought is opaque, while planner–executor pipelines concentrate faults in a central plan, propagating early errors. We introduce DeALOG, a decentralized, central-planner-free multi-agent framework whose specialized agents, i.e., Table, Context, Visual, Summarizing, and Verification, coordinate only by reading and appending to a shared, typed natural-language log as persistent memory, with a lightweight scheduler rather than a planner. Rather than competing on raw accuracy, DeALOG targets what a central planner forfeits: robustness, since no single plan concentrates errors and evidence stays globally visible, and transparent, since the append-only log yields a provenance-anchored, human-readable reasoning trace. We position decentralized log-mediated coordination as a robustness-and-transparency primitive for multimodal reasoning</abstract>
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%0 Conference Proceedings
%T DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework
%A Chakraborty, Abhijit
%A Shekhar, Ashish Raj
%A Agarwal, Shiven
%A Gupta, Vivek
%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 chakraborty-etal-2026-dealog
%X Complex multi-hop question answering over text, tables, and images requires integrating heterogeneous evidence with inspectable reasoning. Single-LLM chain-of-thought is opaque, while planner–executor pipelines concentrate faults in a central plan, propagating early errors. We introduce DeALOG, a decentralized, central-planner-free multi-agent framework whose specialized agents, i.e., Table, Context, Visual, Summarizing, and Verification, coordinate only by reading and appending to a shared, typed natural-language log as persistent memory, with a lightweight scheduler rather than a planner. Rather than competing on raw accuracy, DeALOG targets what a central planner forfeits: robustness, since no single plan concentrates errors and evidence stays globally visible, and transparent, since the append-only log yields a provenance-anchored, human-readable reasoning trace. We position decentralized log-mediated coordination as a robustness-and-transparency primitive for multimodal reasoning
%R 10.18653/2026.findings-emnlp.923
%U https://aclanthology.org/2026.findings-emnlp.923/
%U https://doi.org/10.18653/2026.findings-emnlp.923
%P 19470-19493
Markdown (Informal)
[DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework](https://aclanthology.org/2026.findings-emnlp.923/) (Chakraborty et al., Findings 2026)
ACL