arXiv:2603.23967v1 Announce Type: new
Abstract: Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of t…
arXiv:2603.08566v2 Announce Type: replace-cross
Abstract: DARPA’s AI Cyber Challenge (AIxCC) showed that cyber reasoning systems (CRSs) can go beyond vulnerability discovery to autonomously confirm and patch bugs: seven teams built such systems and op…
arXiv:2510.15259v3 Announce Type: replace
Abstract: Most commodity software lacks accessible Application Programming Interfaces (APIs), requiring autonomous agents to interact solely through pixel-based Graphical User Interfaces (GUIs). In this API-fr…
arXiv:2603.24156v1 Announce Type: new
Abstract: In this paper, we present a novel variational plug-and-play algorithm for Poisson inverse problems. Our approach minimizes an explicit functional which is the sum of a Kullback-Leibler data fidelity term…
arXiv:2603.23977v1 Announce Type: new
Abstract: Deep learning architectures are fundamentally inspired by neuroscience, particularly the structure of the brain’s sensory pathways, and have achieved remarkable success in learning informative data repre…
arXiv:2602.24055v4 Announce Type: replace
Abstract: This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI’s materialized outcomes in deployment. Current approaches…
arXiv:2603.24157v1 Announce Type: new
Abstract: Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have focused on short-horizo…
arXiv:2601.10402v5 Announce Type: replace
Abstract: The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative c…
arXiv:2503.22172v2 Announce Type: replace
Abstract: This paper addresses the challenge of data scarcity in semantic segmentation by generating datasets through text-to-image (T2I) generation models, reducing image acquisition and labeling costs. Segme…
arXiv:2601.19178v2 Announce Type: replace
Abstract: Sequential recommendation models are widely used in applications, yet they face stringent latency requirements. Mainstream models leverage the Transformer attention mechanism to improve performance, …