@inproceedings{gupta-etal-2026-learning,
title = "Learning Optimal Message Representations for Agentic Communication",
author = "Gupta, Shashwat and
Bastos, Anson and
Das, Mayukh and
Ghosh, Supriyo and
Natarajan, Nagarajan and
Bansal, Chetan and
Rajmohan, Saravan",
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.1441/",
pages = "28849--28879",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities in agentic collaborative problem-solving, albeit a gap exists. Existing frameworks predominantly rely on natural language as a primary representation (format) for agentic communication. However natural language could be ambiguous and verbose. Furthermore, recent works have shown that alternative representations can enhance performance in LLMs on certain tasks. But current approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively. In this paper, we propose to dynamically learn the optimal message representations to enhance agentic performance. We model the optimization problem as an Expanding Markov Decision Process (EMDP) and propose our method named OPTiMACS. We evaluate our system across benchmark datasets of collaborative problem-solving. The results show significant performance improvements while maintaining efficiency. Our work bridges the gap between rigid communication protocols and open-ended natural language by providing an adaptive framework that learns task-aware structural representations."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable capabilities in agentic collaborative problem-solving, albeit a gap exists. Existing frameworks predominantly rely on natural language as a primary representation (format) for agentic communication. However natural language could be ambiguous and verbose. Furthermore, recent works have shown that alternative representations can enhance performance in LLMs on certain tasks. But current approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively. In this paper, we propose to dynamically learn the optimal message representations to enhance agentic performance. We model the optimization problem as an Expanding Markov Decision Process (EMDP) and propose our method named OPTiMACS. We evaluate our system across benchmark datasets of collaborative problem-solving. The results show significant performance improvements while maintaining efficiency. Our work bridges the gap between rigid communication protocols and open-ended natural language by providing an adaptive framework that learns task-aware structural representations.</abstract>
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%0 Conference Proceedings
%T Learning Optimal Message Representations for Agentic Communication
%A Gupta, Shashwat
%A Bastos, Anson
%A Das, Mayukh
%A Ghosh, Supriyo
%A Natarajan, Nagarajan
%A Bansal, Chetan
%A Rajmohan, Saravan
%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 gupta-etal-2026-learning
%X Large Language Models (LLMs) have demonstrated remarkable capabilities in agentic collaborative problem-solving, albeit a gap exists. Existing frameworks predominantly rely on natural language as a primary representation (format) for agentic communication. However natural language could be ambiguous and verbose. Furthermore, recent works have shown that alternative representations can enhance performance in LLMs on certain tasks. But current approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively. In this paper, we propose to dynamically learn the optimal message representations to enhance agentic performance. We model the optimization problem as an Expanding Markov Decision Process (EMDP) and propose our method named OPTiMACS. We evaluate our system across benchmark datasets of collaborative problem-solving. The results show significant performance improvements while maintaining efficiency. Our work bridges the gap between rigid communication protocols and open-ended natural language by providing an adaptive framework that learns task-aware structural representations.
%U https://aclanthology.org/2026.findings-acl.1441/
%P 28849-28879
Markdown (Informal)
[Learning Optimal Message Representations for Agentic Communication](https://aclanthology.org/2026.findings-acl.1441/) (Gupta et al., Findings 2026)
ACL
- Shashwat Gupta, Anson Bastos, Mayukh Das, Supriyo Ghosh, Nagarajan Natarajan, Chetan Bansal, and Saravan Rajmohan. 2026. Learning Optimal Message Representations for Agentic Communication. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28849–28879, San Diego, California, United States. Association for Computational Linguistics.