arXiv:2604.04231v1 Announce Type: new
Abstract: Foundation models, such as large language models (LLMs), are powerful but often require customization before deployment to satisfy practical constraints such as safety, privacy, and task-specific require…
arXiv:2603.23472v2 Announce Type: replace
Abstract: Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy. However, gradients and model …
arXiv:2603.13343v2 Announce Type: replace-cross
Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnost…
arXiv:2603.23064v3 Announce Type: replace-cross
Abstract: We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory…
arXiv:2604.04226v1 Announce Type: cross
Abstract: Agentic Web, as a new paradigm that redefines the internet through autonomous, goal-driven interactions, plays an important role in group intelligence. As the foundational semantic primitives of the Ag…
arXiv:2604.04380v1 Announce Type: new
Abstract: Designing visually diverse and high-quality designs remains a manual, time-consuming process, limiting scalability and personalization in creative workflows. We present a system for generating editable d…
arXiv:2604.04262v1 Announce Type: cross
Abstract: Autonomous underwater vehicles (AUVs) and sensor nodes increasingly support decentralized sensing and coordination in the Internet of Underwater Things (IoUT), yet most deployments rely on static trust…
arXiv:2604.02369v2 Announce Type: replace-cross
Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous env…
arXiv:2603.28733v2 Announce Type: replace
Abstract: We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine learning methods. …
arXiv:2509.20349v2 Announce Type: replace
Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consistency required in reg…