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arXiv:2603.00808v1 Announce Type: new Abstract: A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and...
arXiv:2603.00811v1 Announce Type: new Abstract: In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency....
arXiv:2603.00812v1 Announce Type: new Abstract: Work introduces a hierarchical binary tree-based reduction that replaces standard self-attention. The core idea is to use a recursive Gated...
arXiv:2603.00819v1 Announce Type: new Abstract: This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First, it reviews...
arXiv:2603.00821v1 Announce Type: new Abstract: Context: Static code analysis (SCA) tools play a vital role in software development, reducing the cost and time required for...
arXiv:2603.00822v1 Announce Type: new Abstract: As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language Agent Instructions...
arXiv:2603.00823v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is...
arXiv:2603.00824v1 Announce Type: new Abstract: We develop a discrete gauge-theoretic framework for superposition in large language models (LLMs) that replaces the single-global-dictionary premise with a...
arXiv:2603.00825v1 Announce Type: new Abstract: Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with...
arXiv:2603.00828v1 Announce Type: new Abstract: In recent years, various methods have been proposed for mesh analysis, each offering distinct advantages and often excelling on different...
arXiv:2603.00829v1 Announce Type: new Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms. A central challenge is detecting...
arXiv:2603.00839v1 Announce Type: new Abstract: We develop a randomized extension of tensor Krylov subspace methods based on the Einstein product for solving large-scale multilinear systems...
arXiv:2603.00840v1 Announce Type: new Abstract: Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist...
arXiv:2603.00841v1 Announce Type: new Abstract: This article argues that security is not enough to fully capture what is at stake in government exceptional access to...
arXiv:2603.00842v1 Announce Type: new Abstract: Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing...
arXiv:2603.00843v1 Announce Type: new Abstract: Quasi-Monte Carlo (QMC) integration over unbounded domains $\mathbb{R}^s$ remains challenging due to the high dimensionality of sampling space and the...
arXiv:2603.00846v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) to mitigate factual hallucinations. Recent paradigms shift from static pipelines to Modular...
arXiv:2603.00847v1 Announce Type: new Abstract: While Personal Informatics (PI) systems support behavior change, everyday well-being involves more than achieving individual target behaviors. It is shaped...
arXiv:2603.00853v1 Announce Type: new Abstract: We propose a simple yet effective UHDPromer, a neural discrimination-prompted Transformer, for Ultra-High-Definition (UHD) image restoration and enhancement. Our UHDPromer...
arXiv:2603.00854v1 Announce Type: new Abstract: Recommendation Systems are effective in managing the ever-increasing amount of multimodal data available today and help users discover interesting new...
arXiv:2603.00855v1 Announce Type: new Abstract: Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel...
arXiv:2603.00856v1 Announce Type: new Abstract: Systems based on Large Language Models (LLMs) have become formidable tools for automating research and software production. However, their governance...
arXiv:2603.00857v1 Announce Type: new Abstract: Predicting physicochemical properties across chemical space is vital for chemical engineering, drug discovery, and materials science. Current molecular foundation models...
arXiv:2603.00859v1 Announce Type: new Abstract: Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and...