Prompt-SSLC: A Unified Framework for Dual Prompt augmented Semi-Supervised Sequential Leader Clustering in On-the-Fly Category Discovery

On-the-fly Category Discovery (OCD) enables intelligent systems to perform real-time predictions while adapting to emerging classes without full offline retraining. However, deploying OCD in dynamic environments faces three critical bottlenecks: prototype drift under continuous streams, static feature bias toward base classes, and severe latency caused by sample-by-sample clustering. To address those challenges, we propose **Prompt-SSLC**, a unified framework that tightly couples robust streaming clustering, adaptive prompting, and routing. First, **Semi-Supervised Sequential Leader Clustering (SSLC)** anchors labeled class priors and utilizes a Distance-Aware update rule to maintain explicit decision margins and prevent prototype overlap. Second, a **Dual Prompt** architecture decouples global task context from local feature alignment; a **Task Prompt** guides category discovery, while an **Instance Prompt** dynamically recalibrates representations using neighbor prototypes to prevent base-class collapse without parameter tuning. Third, an **Open-Set Aware (OSA) Router** leverages uncertainty estimation to instantly classify high-confidence known samples, drastically cutting latency by only routing novel or ambiguous samples downstream. This cohesive integration of streaming clustering, adaptive prompting, and routing establishes a robust framework that balances stability and adaptability. Extensive experiments across generic and fine-grained benchmarks show that Prompt-SSLC achieves state-of-the-art accuracy while maintaining the low computational overhead required for real-time OCD deployment.