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Learning new tasks in multimodal continual instruction tuning can cause catastrophic forgetting of previously acquired knowledge. Although multi-expert LoRA distributes learning across experts, updates to a dense low-rank expert can still affect many output features. We introduce Monarch-MoE, which replaces the dense f...
This work proposes QUALS, a large-scale time series corpus equilibrium framework that significantly enhances data efficiency, enabling existing models to achieve superior performance using only a small fraction of the original training data.
P predictive Prompting (PrePrompt) is proposed, a novel CIL framework that circumvents correlation-based limitations by leveraging the inherent classification ability of pre-trained models to predict task-specific prompts and decomposes CIL into a two-stage prediction process: task-specific prompt prediction followed b...
RSIAT significantly outperforms state-of-the-art methods in both performance and parameter efficiency, achieving superior stability–plasticity trade-offs with minimal trainable parameters.
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