Collaborative Multimodal Agent Networks: Dynamic Specialization and Emergent Communication for Complex Scene Understanding

Prasanth Yadla; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 6527-6534

We present a framework for collaborative multimodal agent networks that dynamically specialize in complementary perception tasks while developing communication protocols. Our approach addresses the challenge of complex scene understanding by distributing multimodal processing across specialized agents that communicate through learned semantic representations. We introduce the Dynamic agent coordination algorithm that enables real-time role assignment and information fusion across vision, audio, and language modalities. Experiments on five multimodal benchmarks demonstrate that our collaborative approach achieves consistent improvements of 1.8-2.9% over single-agent baselines, including mixture-of-experts and ensemble methods, in multimodal scene understanding tasks. Our analysis reveals emergent specialization patterns and communication strategies that provide insights into distributed multimodal intelligence while maintaining computational efficiency compared to monolithic ap-proaches.