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.

Indian Sign Language, though used by a diverse community, still lacks well-annotated resources for developing systems that would enable sign language processing. In recent years researchers have actively worked for sign languages like American Sign Languages, however, Indian Sign language is still far from data-driven tasks like machine translation. To address this gap, in this paper, we introduce a new dataset CISLR (Corpus for Indian Sign Language Recognition) for word-level recognition in Indian Sign Language using videos. The corpus has a large vocabulary of around 4700 words covering different topics and domains. Further, we propose a baseline model for word recognition from sign language videos. To handle the low resource problem in the Indian Sign Language, the proposed model consists of a prototype-based one-shot learner that leverages resource rich American Sign Language to learn generalized features for improving predictions in Indian Sign Language. Our experiments show that gesture features learned in another sign language can help perform one-shot predictions in CISLR.