arXiv subjects

Shixue Zhang

Publications and source records attributed to Shixue Zhang.

3 recordsLinked to original sources

arXiv2026

OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning

While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, open-source alternatives significantly lag behind. Most academic models remain heavily fragmented, and the few existing efforts toward unified video generation still struggle to seamlessly integrate diverse tasks within a single framework. To bridge this gap, we propose OmniWeaving, an omni-level video generation model featuring powerful multimodal composition and reasoning-informed capabilities. By leveraging a massive-scale pretraining dataset that encompasses diverse compositional and reasoning-augmented scenarios, OmniWeaving learns to temporally bind interleaved text, multi-image, and video inputs while acting as an intelligent agent to infer complex user intentions for sophisticated video creation. Furthermore, we introduce IntelligentVBench, the first comprehensive benchmark designed to rigorously assess next-level intelligent unified video generation. Extensive experiments demonstrate that OmniWeaving achieves SoTA performance among open-source unified models. The codes and model have already been publicly available. Project Page: https://omniweaving.github.io.

cs.CV↗

arXiv2026

Convergence of high-index saddle dynamics for degenerate saddle points on critical manifolds

The high-index saddle dynamics (HiSD) method provides a powerful framework for finding saddle points and constructing solution landscapes. While originally derived for nondegenerate critical points, HiSD has demonstrated empirical success in degenerate cases, where the Hessian matrix exhibits zero eigenvalues. However, the mathematical and numerical analysis of HiSD for degenerate saddle points remains unexplored. In this paper, utilizing Morse-Bott functions, we present a rigorous analysis of HiSD for computing degenerate saddle points on a critical manifold. We prove the local convergence of the continuous HiSD and establish the linear convergence rate of the discrete HiSD algorithm. Furthermore, we provide a theoretical explanation for the gradient alignment tendency, revealing that the gradient direction asymptotically aligns with a specific Hessian eigenvector. Our analysis also elucidates the flexibility in selecting the index for HiSD in the context of degenerate saddle points. We validate our analytical results through numerical experiments on neural-network loss landscapes and demonstrate that momentum-accelerated variants of HiSD achieve rapid convergence to degenerate saddle points.

math.NA↗

arXiv2023

Tensor Completion for Causal Inference with Multivariate Longitudinal Data: A Reevaluation of COVID-19 Mandates

We propose a new method that uses tensor completion to estimate causal effects with multivariate longitudinal data, data in which multiple outcomes are observed for each unit and time period. Our motivation is to estimate the number of COVID-19 fatalities prevented by government mandates such as travel restrictions, mask-wearing directives, and vaccination requirements. In addition to COVID-19 fatalities, we observe related outcomes such as the number of fatalities from other diseases and injuries. The proposed method arranges the data as a tensor with three dimensions (unit, time, and outcome) and uses tensor completion to impute the missing counterfactual outcomes. We first prove that under general conditions, combining multiple outcomes using the proposed method improves the accuracy of counterfactual imputations. We then compare the proposed method to other approaches commonly used to evaluate COVID-19 mandates. Our main finding is that other approaches overestimate the effect of masking-wearing directives and that mask-wearing directives were not an effective alternative to travel restrictions. We conclude that while the proposed method can be applied whenever multivariate longitudinal data are available, we believe it is particularly timely as governments increasingly rely on longitudinal data to choose among policies such as mandates during public health emergencies.

stat.ME↗