Heterogeneous Multi-Agent Framework for Dynamic Generalized Category Discovery
Fatimah Alotaibi, Adithya Kulkarni, Dawei Zhou · Society for Industrial and Applied Mathematics eBooks · 2025
In the fast-paced realm of open-world machine learning, Generalized Category Discovery (GCD) has emerged as a crucial task for identifying new classes within ever-evolving datasets. With the rise of multimodal data that includes text, images, audio, and video, traditional GCD methods, which often rely on parametric classifiers and single-modality inputs, face significant limitations. These approaches can lead to overfitting and hinder the ability to generalize to new categories effectively. This paper highlights the pressing need for innovative strategies that harness the richness of multimodal data to enhance contextual understanding and facilitate real-time category identification. We aim to establish a foundational framework for future GCD research, promoting a more agile and resilient approach to data classification in today’s complex information landscape. To achieve this, we propose a dynamic framework that integrates heterogeneous multi-agent systems, combining Large Language Models (LLMs) with diverse non-LLM methodologies. This approach not only enhances the adaptability and robustness of GCD solutions but also opens up transformative possibilities across critical fields such as autonomous driving, medical diagnostics, and social media analysis.