Category
arXiv:2602.15064v1 Announce Type: cross Abstract: Large populations of AI agents are increasingly embedded in online environments, yet little is known about how their collective interaction …
arXiv:2602.15070v1 Announce Type: cross Abstract: This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, …
arXiv:2602.15072v1 Announce Type: cross Abstract: Accurate polyp segmentation in colonoscopy is essential for cancer prevention but remains challenging due to: (1) high morphological variability (from …
arXiv:2602.15074v1 Announce Type: cross Abstract: We introduce a structure-aware approach for symbolic piano accompaniment that decouples high-level planning from note-level realization. A lightweight transformer predicts …
arXiv:2602.15082v1 Announce Type: cross Abstract: Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have …
arXiv:2602.15087v1 Announce Type: cross Abstract: We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with …
arXiv:2602.15128v1 Announce Type: cross Abstract: Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields …
arXiv:2602.15138v1 Announce Type: cross Abstract: The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic …
arXiv:2602.15161v1 Announce Type: cross Abstract: Federated learning (FL) enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as …
arXiv:2602.15190v1 Announce Type: new Abstract: In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented …
arXiv:2602.15197v1 Announce Type: new Abstract: Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks. While most existing benchmarks assume simple, perfectly …
arXiv:2602.15312v1 Announce Type: new Abstract: Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study …
