arXiv:2511.21970v1 Announce Type: new Abstract: This paper presents a systematic study on developing multi-template machine learning (ML) surrogate models and applying them to the inverse design of transformers (XFMRs) in radio-frequency integrated circuits (RFICs). Our study starts with benchmarking four…
arXiv:2511.22599v1 Announce Type: cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user context (e.g., sessions, preferences) across geo-distributed edge nodes challenging. Existing solutions, such…
arXiv:2511.21990v1 Announce Type: new Abstract: This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising…
arXiv:2511.22819v1 Announce Type: cross Abstract: Recent work introduced a robust computational framework combining embedded mathematical structures, advanced optimization, and neural network architecture, leading to the discovery of multiple unstable self-similar solutions for key fluid dynamics equations, including the Incompressible Porous…
arXiv:2511.23311v1 Announce Type: cross Abstract: Large-scale Vision Language Models (LVLMs) exhibit advanced capabilities in tasks that require visual information, including object detection. These capabilities have promising applications in various industrial domains, such as autonomous driving. For example, LVLMs can generate…
arXiv:2511.23224v1 Announce Type: cross Abstract: This article proposes a Graph Neural Network (GNN) approach to estimate nonstabilizerness in quantum circuits, measured by the stabilizer R’enyi entropy (SRE). Nonstabilizerness is a fundamental resource for quantum advantage, and efficient SRE estimations are…
arXiv:2501.01457v3 Announce Type: replace Abstract: While inference-time thinking allows Large Language Models (LLMs) to address complex problems, the extended thinking process can be unreliable or inconsistent because of the model’s probabilistic nature, especially near its knowledge boundaries. Existing approaches attempt…
arXiv:2511.20558v2 Announce Type: replace-cross Abstract: The abundance of fine-grained spatio-temporal data, such as traffic sensor networks, offers vast opportunities for scientific discovery. However, inferring causal relationships from such observational data remains challenging, particularly due to unobserved confounders that are specific…
arXiv:2511.22904v1 Announce Type: cross Abstract: To interact effectively with humans in the real world, it is important for agents to understand language that describes the dynamics of the environment–that is, how the environment behaves–rather than just task instructions specifying “what…
arXiv:2511.20798v2 Announce Type: replace Abstract: Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be…
