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:2604.04736v1 Announce Type: cross
Abstract: Machine learning models, and deep neural networks in particular, are increasingly deployed in risk-sensitive domains such as healthcare, environmental forecasting, and finance, where reliable quantific…
arXiv:2604.04373v1 Announce Type: new
Abstract: There is growing interest in improving LLMs without updating model parameters. One well-established direction is test-time scaling, where increased inference-time computation (e.g., longer reasoning, sam…
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:2604.04475v1 Announce Type: cross
Abstract: Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to time series data whi…
arXiv:2604.04426v1 Announce Type: new
Abstract: Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP servers, a new class of sup…
arXiv:2302.00797v4 Announce Type: replace
Abstract: Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents’ strategies, and exploiting this opponent model by playing a best response. However, exist…
arXiv:2604.04604v1 Announce Type: cross
Abstract: AI agents – i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement – are being deployed at scale across enterprise functions …
arXiv:2502.13388v3 Announce Type: replace
Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Larg…
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…