Tag
This paper introduces a provenance-guided incremental learning framework to handle rule-induced concept shifts, where target definitions change, and evaluates it on a new benchmark with improved efficiency and accuracy.
SynGAP is a task-free continual learning framework that simulates biological metaplasticity via adaptive gradient preconditioning to mitigate catastrophic forgetting, demonstrating significant accuracy improvements over existing methods on benchmarks.
This paper introduces a quantum incremental learning framework using trainable mixed-state prototypes, enabling new classes to be added without increasing circuit width while mitigating catastrophic forgetting.
DeltaMem organizes LLM agent memory into residual trees to reduce redundancy and retrieval conflicts, storing incremental variations of experiences for continual learning.
This paper investigates adversarial robustness in Fuzzy ARTMAP, a streaming neural architecture, by introducing WB-Softmax as a mechanism-aligned white-box attack surrogate. It evaluates progressive training and selective updating strategies to improve robustness without data replay, while also offering interpretable diagnostics for structural failures.