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Zezhong Jin

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#machine learning Preprint Oct 2026

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgments. However, existing methods primarily focus on the final preference decision and provide little explicit supervision for localizing and correcting erroneous content....

Zi-Long Huang, K. Lee, Ze-Zhong Jin et al. · 0 citations

Preprint Aug 2026

Decoupled Latent Flow Matching for Few-Step Joint Vocal-Accompaniment Separation

Experiments show that latent adversarial refinement can improve perceptual and separation metrics under a reduced sampling budget and apply latent adversarial post-training inspired by Flow2GAN for few-step generation.

Li-Shi Zuo, You-Zhi Tu, Lu Yi et al. · 0 citations

Preprint Aug 2026

Beyond Residual Connections: Manifold-Constrained Hyper-Connections for Robust Speaker Representation Learning

Manifold-Constrained Hyper-Connections (mHC) is introduced, reformulat- ing residual paths as a multi-stream evolution where informa- tion is mixed through a doubly stochastic matrix, highlighting its effectiveness for robust speaker representation learning.

Zezhong Jin, Xiaoyu Wang, Zhe Li et al. · 0 citations