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arXiv:2602.09295v1 Announce Type: new Abstract: This work presents the largest curation of Southern Resident Killer Whale (SRKW) acoustic data to date, also containing other marine...
arXiv:2602.09296v1 Announce Type: new Abstract: Think-Aloud Computing, a method for capturing users' verbalized thoughts during software tasks, allows eliciting rich contextual insights into evolving intentions,...
arXiv:2602.09297v1 Announce Type: new Abstract: Transformers leverage attention, the residual connection, and layer normalization to control the variance of token representations. We propose to modify...
arXiv:2602.09299v1 Announce Type: new Abstract: This paper describes how AI models can be augmented and adapted to produce interpretation of landscapes. We describe the technical...
arXiv:2602.09300v1 Announce Type: new Abstract: We propose risk-sensitive reinforcement learning algorithms catering to three families of risk measures, namely expectiles, utility-based shortfall risk and optimized...
arXiv:2602.09302v1 Announce Type: new Abstract: In this work, we propose a new bounded arithmetic theory, denoted $APX_1$, designed to formalize a broad class of probabilistic...
arXiv:2602.09303v1 Announce Type: new Abstract: We propose a physics-informed consistency modeling framework for solving partial differential equations (PDEs) via fast, few-step generative inference. We identify...
arXiv:2602.09304v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of a designated forget set from a trained model while preserving utility on...
arXiv:2602.09305v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning (RL)-based fine-tuning is a...
arXiv:2602.09306v1 Announce Type: new Abstract: Federated sequential recommendation (FedSeqRec) aims to perform next-item prediction while keeping user data decentralised, yet model quality is frequently constrained...
arXiv:2602.09307v1 Announce Type: new Abstract: Applying dynamic logics to program verifications is a challenge, because their axiomatic rules for regular expressions can be difficult to...
arXiv:2602.09310v1 Announce Type: new Abstract: Computer Use Agents (CUAs) operate interfaces by pointing, clicking, and typing -- mirroring interactions of sighted users (SUs) who can...
arXiv:2602.09311v1 Announce Type: new Abstract: Automated regression testing is a cornerstone of modern software development, often contributing directly to code review and Continuous Integration (CI)....
arXiv:2602.09312v1 Announce Type: new Abstract: Utilizing Large Language Models (LLM) as chatbots in diverse business scenarios often presents the challenge of maintaining topic continuity. Abrupt...
arXiv:2602.09314v1 Announce Type: new Abstract: Optimizers leveraging the matrix structure in neural networks, such as Shampoo and Muon, are more data-efficient than element-wise algorithms like...
arXiv:2602.09315v1 Announce Type: new Abstract: Hospitalization of patients is one of the major factors for high wound care costs. Most patients do not acquire a...
arXiv:2602.09316v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) based Large Language Models (LLMs) have achieved superior performance, yet the massive memory overhead caused by storing multiple...
arXiv:2602.09317v1 Announce Type: new Abstract: Neural networks are increasingly used as surrogate solvers and control policies, but unconstrained predictions can violate physical, operational, or safety...
arXiv:2602.09318v1 Announce Type: new Abstract: Accurate classification of breast cancer histopathology images is pivotal for early oncological diagnosis and therapeutic intervention.However, conventional deep learning architectures...
arXiv:2602.09319v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory management. However,...
arXiv:2602.09323v1 Announce Type: new Abstract: Major challenges in LLMs inference remain frequent memory bandwidth bottlenecks, computational redundancy, and inefficiencies in long-sequence processing. To address these...
arXiv:2602.09324v1 Announce Type: new Abstract: Deep models based on vision transformer (ViT) and convolutional neural network (CNN) have demonstrated remarkable performance on natural datasets. However,...
arXiv:2602.09326v1 Announce Type: new Abstract: Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This...
arXiv:2602.09328v1 Announce Type: new Abstract: The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typically...