@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://preview.aclanthology.org/ingest-acl/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."
}