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20242026
most citedDFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellations

1 citations · 1 across the 13 of their papers we have counts for

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cs.AI2026

Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models

Zhi Song, Ximing Xing, Zhenchao Tang +9

Joint-embedding predictive architectures (JEPAs) learn dynamics by predicting future observations in representation space. Yet most JEPA world models return one latent successor, e…

cs.CV2026

SciFigPlag-Bench: A Benchmark for Provenance-Aware Scientific Figure Plagiarism Detection

Zhiying Cui, Minghao Yang, Linlin Gao +2

Scientific figures often encode the visual evidence behind scientific findings, yet figure plagiarism remains underexplored as a benchmarked multimodal evaluation problem. We prese…

cs.SD2026

Listen, Do Not Copy: Internalizing Audio-Grounded Scaffold Context for Robust Omni-Model Speech Understanding

Pengfei Zhang, Biao Tian, Tianxin Xie +3

Omni models transcribe clean, single-speaker speech well, but their accuracy drops sharply when speakers overlap and the scene is noisy, exactly where knowing who said what matters…

cs.LG2026

Quantile Geometry Regularization for Distributional Reinforcement Learning

Zhaofan Zhang, Minghao Yang, Rufeng Chen +2

Quantile-based distributional reinforcement learning methods learn return distributions through sampled quantile regression, but their bootstrapped target quantiles may induce dist…

cs.SD2026

AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching

Pengfei Zhang, Tianxin Xie, Minghao Yang +1

REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher features, but its effectiveness in to…

eess.AS2026

Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis

Pengfei Zhang, Tianxin Xie, Minghao Yang +1

Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards tr…

cs.LG2026

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

Minghao Yang, Ren Togo, Guang Li +2

Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central role in determining expert specializat…