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arXiv:2505.17902v3 Announce Type: replace Abstract: In an era defined by rapid data evolution, traditional Machine Learning (ML) models often struggle to adapt to dynamic environments....
arXiv:2505.18289v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are key tools for graph representation learning, demonstrating strong results across diverse prediction tasks. In this...
arXiv:2505.18663v3 Announce Type: replace Abstract: Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical...
arXiv:2505.18832v3 Announce Type: replace Abstract: Understanding how knowledge is distributed across the layers of generative models is crucial for improving interpretability, controllability, and adaptation. While...
arXiv:2505.19328v4 Announce Type: replace Abstract: Ambivalence and hesitancy (A/H), a closely related construct, is the primary reasons why individuals delay, avoid, or abandon health behaviour...
arXiv:2505.19442v4 Announce Type: replace Abstract: Controllable code generation, the ability to synthesize code that follows a specified style while maintaining functionality, remains a challenging task....
arXiv:2505.19459v2 Announce Type: replace Abstract: Joint Energy-based Models (JEMs) are well known for their ability to unify classification and generation within a single framework. Despite...
arXiv:2505.19514v4 Announce Type: replace Abstract: Prompt quality plays a critical role in the performance of large language models (LLMs), motivating a growing body of work...
arXiv:2505.20298v3 Announce Type: replace Abstract: Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large...
arXiv:2505.20347v2 Announce Type: replace Abstract: Recent advances have demonstrated the effectiveness of Reinforcement Learning (RL) in improving the reasoning capabilities of Large Language Models (LLMs)....
arXiv:2505.20655v5 Announce Type: replace Abstract: Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged...
arXiv:2505.21184v4 Announce Type: replace Abstract: To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the...
arXiv:2505.21229v2 Announce Type: replace Abstract: In the {\sc Course Allocation} problem, there are a set of students and a set of courses at a given...
arXiv:2505.21388v4 Announce Type: replace Abstract: Social recommender systems facilitate social connections by identifying potential friends for users. Each user maintains a local social network centered...
arXiv:2505.21539v3 Announce Type: replace Abstract: The goal of point cloud assembly is to reconstruct a complete 3D shape by aligning multiple point cloud pieces. This...
arXiv:2505.22323v5 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input....
arXiv:2505.22692v4 Announce Type: replace Abstract: Accurate forecasting of Avian Influenza Virus (AIV) outbreaks within wild bird populations necessitates models that account for complex, multi-scale transmission...
arXiv:2505.22846v3 Announce Type: replace Abstract: Interactive Theorem Proving was repeatedly shown to be fruitful when combined with Generative Artificial Intelligence. This paper assesses multiple approaches...
arXiv:2505.23277v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Prior context compression methods rely on predefined importance metrics...
arXiv:2506.00555v3 Announce Type: replace Abstract: Medical Large Vision-Language Models (Med-LVLMs) have shown strong potential in multimodal diagnostic tasks. However, existing single-agent models struggle to generalize...
arXiv:2506.01327v3 Announce Type: replace Abstract: Federated Class-Incremental Learning (FCIL) enables Class-Incremental Learning (CIL) from distributed data. Existing FCIL methods typically integrate old knowledge preservation into...
arXiv:2506.02337v2 Announce Type: replace Abstract: Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at...
arXiv:2506.03059v2 Announce Type: replace Abstract: We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as...
arXiv:2506.04775v3 Announce Type: replace Abstract: We study stochastic linear bandits with heavy-tailed rewards, where the rewards have a finite $(1+\epsilon)$-absolute central moment bounded by $\upsilon$...