Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large language models (LLMs) possess structured reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that leverages LLM-guided semantic manipulation under embedding-space similarity constraints. This alignment enforces visual-semantic consistency while selectively perturbing watermark-relevant semantics, ultimately inducing detector misclassification. Extensive empirical results show that CSI consistently outperforms prevailing attack baselines against content-aware semantic watermarking, revealing a fundamental security weakness of current semantic watermark designs when confronted with LLM-driven semantic perturbations.

This paper introduces Force Matching (ForM), a novel framework for generative modeling that represents an initial exploration into leveraging special relativistic mechanics to enhance the stability of the sampling process. By incorporating the Lorentz factor, ForM imposes a velocity constraint, ensuring that sample velocities remain bounded within a constant limit. This constraint serves as a fundamental mechanism for stabilizing the generative dynamics, leading to a more robust and controlled sampling process. We provide a rigorous theoretical analysis demonstrating that the velocity constraint is preserved throughout the sampling procedure within the ForM framework. To validate the effectiveness of our approach, we conduct extensive empirical evaluations. On the half-moons dataset, ForM significantly outperforms baseline methods, achieving the lowest Euclidean distance loss of 0.714, in contrast to vanilla first-order flow matching (5.853) and first- and second-order flow matching (5.793). Additionally, we perform an ablation study to further investigate the impact of our velocity constraint, reaffirming the superiority of ForM in stabilizing the generative process. The theoretical guarantees and empirical results underscore the potential of integrating special relativity principles into generative modeling. Our findings suggest that ForM provides a promising pathway toward achieving stable, efficient, and flexible generative processes. This work lays the foundation for future advancements in high-dimensional generative modeling, opening new avenues for the application of physical principles in machine learning.

The multiplicative weight update method is a well-known algorithm commonly used for decision making based on recommendations from n experts. Despite being widely used, the classical MWU algorithm suffers from a linear runtime complexity, which limits its efficiency on large problems. Additionally, since expert data may be sensitive, it is important to develop privacy-preserving methods. In this paper, we propose the first data structure that approximates the multiplicative weight update in sublinear time (o(n)) while providing differential privacy guarantees, significantly improving efficiency for large-scale problems. Our data structure both enhances computational efficiency and ensures privacy protection, with theoretical guarantees on both utility and privacy, enabling fast and private decision making.

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n3) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ2) respectively, where m = Ω(n4) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ2).

Sampling is a fundamental problem in optimization, machine learning and theoretical computer science. A common region of interest for sampling is the polytope, which is defined by a set of linear inequalities. The algorithm that is sampling from polytopes usually requires heavy matrix algebra, including matrix multiplication, matrix inversion and matrix determinant. In this work, we show how to implement the heavy matrix algebra in the area of sampling in nearly linear time for the polytope that has small treewidth. In particular, given a polytope defined by a matrix A ∈ ℝn×dwith treewidth τ, we improve the running time of each iteration for three typical sampling algorithms for polytopes such as Dikin Walk, Soft-Threshold Dikin Walk and Vaidya Walk from O(nd2) to O(nτ2) by exploiting the small treewidth structures of the matrices.

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named D ynam i c H ierarchical A ttention N etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.