Cell Death Discovery, Published online: 16 September 2026; doi:10.1038/s41420-026-03349-8

ESAM reduces sensitivity to anti-HER2 therapy in HER2-positive breast cancer by activating the mTOR pathway

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02602-9

Graphicalized vision-language modeling for comprehensive lung nodule analysis and risk stratification

arXiv:2601.04864v2 Announce Type: replace Abstract: Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations, we propose a novel approach employing task-specific Prompt-Prototype (ProP), thereby eliminating the need for key-value pairs. In our method, task-specific prompts facilitate more effective feature learning for the current task, while corresponding prototypes capture the representative features of the input. During inference, predictions are generated by binding each task-specific prompt with its associated prototype. Additionally, we introduce regularization constraints during prompt initialization to penalize excessively large values, thereby enhancing stability. Experiments on several widely used datasets demonstrate the effectiveness of the proposed method. In contrast to mainstream prompt-based approaches, our framework removes the dependency on key-value pairs, offering a fresh perspective for future continual learning research.