Qi Jin
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STaR-KV introduces a novel, training-free KV cache compression framework that adaptively re-weights token importance across spatial, temporal, and distributional axes, significantly reducing GPU memory usage for GUI vision-language models while maintaining high accuracy.
The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coherent latent interests.
Proposed DDMSR framework for multi-modal sequential recommendation using graph-based feature denoising and frequency-domain sequence denoising, and multi-modal contrastive alignment objective.
Papers
Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation
Proposed DDMSR framework for multi-modal sequential recommendation using graph-based feature denoising and frequency-domain sequence denoising, and multi-modal contrastive alignment objective.