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HeteGCD is proposed, a new statistics-aware dual-prototype learning perspective for generalized category discovery on heterophilous graphs that combines feature-based similarity with distribution-based consistency to support category discovery.
The challenges of reliable asynchronous task execution in LLM agents are highlighted and the need to evaluate how agents handle incoming results alongside end-to-end task success is highlighted.
A novel framework named Neural collapse Inspired Label-limited Evolving stream learning (NILE), which exploits Neural Collapse geometry to explicitly construct a Simplex Equiangular Tight Frame (ETF) as a fixed classifier, ensuring maximal inter-class separability to guide feature discriminability.
This paper proposes a novel lightweight plastic-memory framework specifically designed for few-shot incremental learning on graphs, featuring an innovative evolving micro-clustering structure that dynamically updates representations of class prototypes, sub-prototypes, and their interaction weights.
TopoNC, a topology-aware neural collapse framework for Graph GCD, is proposed and a Topology-Conditioned Pseudo-Labeling strategy that integrates Sinkhorn-based global balancing, Old-Class Top-K Admission Masking, and Neighborhood-Consensus Screening is designed, to reliably guide feature collapse.
Learning a stable yet highly discriminative representation space that can simultaneously recognize known categories and discover novel ones from limited labeled data is fundamental to Generalized Category Discovery (GCD) on graphs. Recently, Neural Collapse (NC) theory has emerged as a powerful geometric principle for...
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