@inproceedings{na-etal-2026-dynamic,
title = "Dynamic Context Allocation for Concurrent Multi-Agent {LLM}s",
author = "Na, Junho and
Matsunaga, Daiki E. and
Koh, Nam Ho and
Lee, Jongmin and
Poupart, Pascal and
Kim, Kee-Eung",
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.1785/",
pages = "40651--40683",
ISBN = "979-8-89176-459-0",
abstract = "Concurrent multi-agent LLM systems increasingly rely on role-specialized Workers that act in parallel, but effective coordination depends not only on what Workers generate, but also on what task state each Worker observes before generating. Existing systems usually fix this observation interface through workflow rules, retrieval heuristics, or full-history sharing, so information flow cannot adapt as task state and feedback evolve. We introduce $\textbf{Dynamic Context Allocation (DCA)}$, which treats per-Worker observation control as a trainable coordination problem for a fixed-role team. An RL-trained $\textit{Context Allocator}$ maintains a bounded $\textit{Context Board}$ and decides before each round which evolving task-state entries should be retained, revised, removed, or exposed to each Worker, rather than learning agent selection, execution order, or workflow topology. We evaluate DCA on cooperative code generation, Customer Relationship Management (CRM) lead qualification, and $\textbf{MultiWebGen}$, a new benchmark for multi-agent full-stack web development. Across these settings, DCA improves over fixed-protocol coordination and fixed-context multi-agent RL baselines, and it outperforms all evaluated frontier single-agent LLMs on CRM lead qualification despite using smaller role-specialized Workers."
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<abstract>Concurrent multi-agent LLM systems increasingly rely on role-specialized Workers that act in parallel, but effective coordination depends not only on what Workers generate, but also on what task state each Worker observes before generating. Existing systems usually fix this observation interface through workflow rules, retrieval heuristics, or full-history sharing, so information flow cannot adapt as task state and feedback evolve. We introduce Dynamic Context Allocation (DCA), which treats per-Worker observation control as a trainable coordination problem for a fixed-role team. An RL-trained Context Allocator maintains a bounded Context Board and decides before each round which evolving task-state entries should be retained, revised, removed, or exposed to each Worker, rather than learning agent selection, execution order, or workflow topology. We evaluate DCA on cooperative code generation, Customer Relationship Management (CRM) lead qualification, and MultiWebGen, a new benchmark for multi-agent full-stack web development. Across these settings, DCA improves over fixed-protocol coordination and fixed-context multi-agent RL baselines, and it outperforms all evaluated frontier single-agent LLMs on CRM lead qualification despite using smaller role-specialized Workers.</abstract>
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%0 Conference Proceedings
%T Dynamic Context Allocation for Concurrent Multi-Agent LLMs
%A Na, Junho
%A Matsunaga, Daiki E.
%A Koh, Nam Ho
%A Lee, Jongmin
%A Poupart, Pascal
%A Kim, Kee-Eung
%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 na-etal-2026-dynamic
%X Concurrent multi-agent LLM systems increasingly rely on role-specialized Workers that act in parallel, but effective coordination depends not only on what Workers generate, but also on what task state each Worker observes before generating. Existing systems usually fix this observation interface through workflow rules, retrieval heuristics, or full-history sharing, so information flow cannot adapt as task state and feedback evolve. We introduce Dynamic Context Allocation (DCA), which treats per-Worker observation control as a trainable coordination problem for a fixed-role team. An RL-trained Context Allocator maintains a bounded Context Board and decides before each round which evolving task-state entries should be retained, revised, removed, or exposed to each Worker, rather than learning agent selection, execution order, or workflow topology. We evaluate DCA on cooperative code generation, Customer Relationship Management (CRM) lead qualification, and MultiWebGen, a new benchmark for multi-agent full-stack web development. Across these settings, DCA improves over fixed-protocol coordination and fixed-context multi-agent RL baselines, and it outperforms all evaluated frontier single-agent LLMs on CRM lead qualification despite using smaller role-specialized Workers.
%U https://aclanthology.org/2026.emnlp-main.1785/
%P 40651-40683
Markdown (Informal)
[Dynamic Context Allocation for Concurrent Multi-Agent LLMs](https://aclanthology.org/2026.emnlp-main.1785/) (Na et al., EMNLP 2026)
ACL
- Junho Na, Daiki E. Matsunaga, Nam Ho Koh, Jongmin Lee, Pascal Poupart, and Kee-Eung Kim. 2026. Dynamic Context Allocation for Concurrent Multi-Agent LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 40651–40683, Budapest, Hungary. Association for Computational Linguistics.