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arXiv:2602.13910v1 Announce Type: new Abstract: Algorithmic stability is a classical framework for analyzing the generalization error of learning algorithms. It predicts that an algorithm has...
arXiv:2602.13912v1 Announce Type: new Abstract: We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for...
arXiv:2602.13914v1 Announce Type: new Abstract: There has been an increasing interest in topological semantics for epistemic logic, which has been shown to be useful for,...
arXiv:2602.13915v1 Announce Type: new Abstract: Location information extracted from mobile devices has been largely exploited to reveal our routines, significant places, and interests just to...
arXiv:2602.13920v1 Announce Type: new Abstract: Recent advances in agent-mediated systems have enabled a new paradigm of social network simulation, where AI agents interact with human-like...
arXiv:2602.13921v1 Announce Type: new Abstract: Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge....
arXiv:2602.13926v1 Announce Type: new Abstract: Electric Vehicle (EV) charging infrastructure is critical for the widespread adoption of EVs, ensuring efficient and secure charging processes. Evaluating...
arXiv:2602.13928v1 Announce Type: new Abstract: We present voice2mode, a method for classification of four singing phonation modes (breathy, neutral (modal), flow, and pressed) using embeddings...
arXiv:2602.13930v1 Announce Type: new Abstract: Breast cancer screening programmes increasingly seek to move from one-size-fits-all interval to risk-adapted and personalized strategies. Deep learning (DL) has...
arXiv:2602.13932v1 Announce Type: new Abstract: We introduce and study the Joint Task Assistance Planning problem which generalizes prior work on optimizing assistance in robotic collaboration....
arXiv:2602.13933v1 Announce Type: new Abstract: Large language model (LLM) agents demonstrate strong performance in short-text contexts but often underperform in extended dialogues due to inefficient...
arXiv:2602.13934v1 Announce Type: new Abstract: Code generation has progressed more reliably than reinforcement learning, largely because code has an information structure that makes it learnable....
arXiv:2602.13935v1 Announce Type: new Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, particularly under uncertainty,...
arXiv:2602.13936v1 Announce Type: new Abstract: Trajectory prediction for traffic agents is critical for safe autonomous driving. However, achieving effective zero-shot generalization in previously unseen domains...
arXiv:2602.13937v1 Announce Type: new Abstract: Automated Machine Learning (AutoML) has revolutionized the development of data-driven solutions; however, traditional frameworks often function as "black boxes", lacking...
arXiv:2602.13939v1 Announce Type: new Abstract: Business environments characterized by structural demand intermittency, high variability, and multi-step planning horizons require robust and reproducible model selection mechanisms....
arXiv:2602.13940v1 Announce Type: new Abstract: Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general...
arXiv:2602.13941v1 Announce Type: new Abstract: Social media platforms, especially Facebook parenting groups, have long been used as informal support networks for mothers seeking advice and...
arXiv:2602.13943v1 Announce Type: new Abstract: Combinatorial optimization problems are central to science and engineering and specialized hardware from quantum annealers to classical Ising machines are...
arXiv:2602.13944v1 Announce Type: new Abstract: Recent years have witnessed remarkable progress in multimodal learning within computational pathology. Existing models primarily rely on vision and language...
arXiv:2602.13949v1 Announce Type: new Abstract: Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice,...
arXiv:2602.13953v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a trending paradigm for training reasoning large language models (LLMs). However, due...
arXiv:2602.13954v1 Announce Type: new Abstract: We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to...
arXiv:2602.13957v1 Announce Type: new Abstract: In this paper, we propose a data-enabled moving horizon estimation (MHE) approach for nonlinear systems. While the approach is formulated...