SPARK: Structural Priors Activated from closed-set Recognition Knowledge for On-the-Fly Category Discovery

On-the-Fly Category Discovery aims to identify known classes and continuously distinguish new classes from a streaming test sequence. Existing methods often entangle category discovery with backbone optimization by introducing discovery-specific modules or objectives that reshape the closed-set feature space. In this paper, we ask a simpler question: can a standard closed-set classifier already provide useful structural priors for online discovery? We answer this question affirmatively and propose SPARK, a plug-and-play framework that activates the geometric knowledge encoded by closed-set supervision. Instead of treating known classes as isolated prototypes, SPARK learns Local Manifold Anchors around each known class. These anchors preserve inter-class discrimination while providing non-redundant local coverage of intra-class semantic variations. The union of all anchors forms a discrete approximation to a shared superclass manifold. At test time, when a streaming sample is rejected as unknown, SPARK analytically initializes its corresponding prototype under the constraint of this manifold and then updates the prototype online as more samples arrive. Experiments on seven public benchmarks show that SPARK achieves strong overall performance. These results suggest that on-the-fly category discovery does not necessarily require heavy specialized modules and the structural priors already present in closed-set classifiers can be directly activated for open-world recognition.