Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting

RecTok has been introduced as a novel approach to enhance high-dimensional visual tokenizers in diffusion models, addressing the inherent trade-off between dimensionality and generation quality. By employing flow semantic distillation and reconstruction-alignment distillation, RecTok aims to improve the semantic richness of the forward flow used in training diffusion transformers.

A comprehensive survey has been conducted on event-based mobile sensing, highlighting its evolution from 2014 to 2025. The study emphasizes the challenges posed by high data volume, noise, and the need for low-latency processing in mobile applications, particularly in the context of event cameras that offer high temporal resolution.

A recent study published on arXiv explores how low-level bitwise perturbations, or fault injections, in large language models (LLMs) can affect the semantic meaning of generated image captions while maintaining grammatical integrity. This research highlights the vulnerability of transformers to subtle hardware bit flips, which can significantly alter the narratives produced by AI systems.

A new approach called Inference Time Feature Injection has been introduced to enhance real-time recommendation systems in long-form video streaming. This method allows for the selective injection of recent user watch history at inference time, overcoming the limitations of static user features that are updated only daily. The technique has shown a statistically significant increase in user engagement metrics by 0.47%.

A new method has been proposed for efficiently determining the regularization parameter for low-rank MMSE filters using a Kronecker-product representation. This approach highlights the importance of selecting the correct regularization parameter, which is closely tied to rank selection, and demonstrates significant improvements over traditional methods through simulations.

A new approach called Neural Modular Physics (NMP) has been introduced for elastic simulation, combining the strengths of neural networks with the reliability of traditional physics simulators. This method decomposes elastic dynamics into meaningful neural modules, allowing for direct supervision of intermediate quantities and physical constraints.

A new neural architecture named SigMA has been introduced, integrating path signatures with multi-head self-attention for parameter learning in stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm). This approach addresses the challenges posed by non-Markovian processes, which complicate traditional parameter estimation techniques.

A novel method for joint learning of unsupervised multi-view feature and instance co-selection with cross-view imputation has been proposed, addressing the challenges of missing data in multi-view datasets. This approach enhances the interaction between co-selection and imputation processes, aiming to improve the effectiveness of data analysis in scenarios where some samples are incomplete.