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arXiv:2602.11805v1 Announce Type: new Abstract: Path signatures embed trajectories into tensor algebra and constitute a universal, non-parametric representation of paths; however, in the standard form,...
arXiv:2602.11804v1 Announce Type: new Abstract: Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on...
arXiv:2602.11802v1 Announce Type: new Abstract: Graph link prediction (LP) plays a critical role in socially impactful applications, such as job recommendation and friendship formation. Ensuring...
arXiv:2602.11801v1 Announce Type: new Abstract: Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged...
arXiv:2602.11800v1 Announce Type: new Abstract: Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with...
arXiv:2602.11799v1 Announce Type: new Abstract: Multi-modal recommendation has gained traction as items possess rich attributes like text and images. Semantic ID-based approaches effectively discretize this...
arXiv:2602.11798v1 Announce Type: new Abstract: Next-generation communication networks are characterized by integrated ultra-high reliability, ultra-low latency, massive connectivity, and ubiquitous coverage. However, this paradigm faces...
arXiv:2602.11795v1 Announce Type: new Abstract: This paper presents an embedding-based approach to detecting variation without relying on prior normalisation or predefined variant lists. The method...
arXiv:2602.11794v1 Announce Type: new Abstract: We study the problem of learning the law of linear stochastic partial differential equations (SPDEs) with additive Gaussian forcing from...
arXiv:2602.11793v1 Announce Type: new Abstract: Watermarking has emerged as a crucial technique for detecting and attributing content generated by large language models. While recent advancements...
arXiv:2602.11792v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is central to training modern reasoning models, but the undisclosed training data raises concerns...
arXiv:2602.11791v1 Announce Type: new Abstract: We give the first two algorithms to enumerate all binary words of $\{0,1\}^\ell$ (like Gray codes) while ensuring that the...
arXiv:2602.11790v1 Announce Type: new Abstract: Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that...
arXiv:2602.11788v1 Announce Type: new Abstract: This paper establishes a novel upper bound-termed the arithmetic Singleton bound-on the Hamming distance of any simple-root constacyclic code over...
arXiv:2602.11786v1 Announce Type: new Abstract: Traditional benchmarks for large language models (LLMs) primarily assess safety risk through breadth-oriented evaluation across diverse tasks. However, real-world deployment...
arXiv:2602.11785v1 Announce Type: new Abstract: As automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases....
arXiv:2602.11782v1 Announce Type: new Abstract: LLMs can solve complex tasks through reasoning and tool use, but accurately translating these solutions into structured workflows remains challenging....
arXiv:2602.11780v1 Announce Type: new Abstract: In online advertising, advertising text plays a critical role in attracting user engagement and driving advertiser value. Existing industrial systems...
arXiv:2602.11779v1 Announce Type: new Abstract: Temperature is a crucial hyperparameter in large language models (LLMs), controlling the trade-off between exploration and exploitation during text generation....
arXiv:2602.11776v1 Announce Type: new Abstract: In binary classification systems, decision thresholds translate model scores into actions. Choosing suitable thresholds relies on the specific distribution of...
arXiv:2602.11775v1 Announce Type: new Abstract: Explanations are essential for helping users interpret and trust autonomous smart-home decisions, yet evaluating their quality and impact remains methodologically...
arXiv:2602.11773v1 Announce Type: new Abstract: For a directed graph $G$, and a linear order $\ll$ on the vertices of $G$, we define backedge graph $G^\ll$...
arXiv:2602.11772v1 Announce Type: new Abstract: In this paper we study the inverse eigenvector centrality problem on directed graphs: given a prescribed node centrality profile, we...
arXiv:2602.11771v1 Announce Type: new Abstract: Species distribution models (SDMs) commonly produce probabilistic occurrence predictions that must be converted into binary presence-absence maps for ecological inference...