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20202026
most citedDiGress: Discrete Denoising diffusion for graph generation

73 citations · 142 across the 31 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

Shihao Zhang, Yunzhi Li, Yuguang Yan +4

Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with human aesthetics and improve r…

cs.CV2026

SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation

Xu Zhang, Yu Lu, Ruijie Quan +3

Flow Matching has enabled robust text-to-video generation via latent ODE sampling. However, velocity approximation and numerical discretization errors inevitably accumulate, causin…

cs.LG2026

Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training

Wenjie Zhou, Bohan Wang, Hongtao Zhang +3

Model merging has emerged as a lightweight paradigm for enhancing Large Language Models (LLMs), yet its underlying mechanisms remain poorly understood. In this work, we analyze lat…

cs.LG2026

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks

Wenquan Ma, Yang Sui, Jiaye Teng +3

Algorithmic stability is among the most potent techniques in generalization analysis. However, its derivation usually requires a stepsize under non-convex…

cs.CL2026

HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Weiqi Zhai, Zhihai Wang, Jinghang Wang +33

Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions. However, community-led analyses…

cs.LG2026

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks

Bohan Wang, Zewen Liu, Lu Lin +4

Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evidence of robustness. We show tha…