arXiv:2511.02997v1 Announce Type: new Abstract: As AI systems become more capable and widely deployed as agents, ensuring their safe operation becomes critical. AI control offers one approach to mitigating the risk from untrusted AI agents by monitoring their actions and intervening or auditing when necessary. Evaluating the safety of these protocols requires understanding both their […]
ROSBag MCP Server: Analyzing Robot Data with LLMs for Agentic Embodied AI Applications
arXiv:2511.03497v1 Announce Type: cross Abstract: Agentic AI systems and Physical or Embodied AI systems have been two key research verticals at the forefront of Artificial Intelligence and Robotics, with Model Context Protocol (MCP) increasingly becoming a key component and enabler of agentic applications. However, the literature at the intersection of these verticals, i.e., Agentic Embodied […]
Phospho-Proteomics Method Optimization and Application to Stimulated Jurkat Cells
arXiv:2511.02932v1 Announce Type: new Abstract: In clinical proteomics, available input is often limited. In addition, phospho-proteomics is of particular interest since the dysregulation of these post-translational modifications (PTMs) has been implicated in various diseases such as cancer. We therefore assessed the feasibility of low input phospho-proteomics via phospho-bulk titration and low-input starting material. We found […]
Multi-User Personalisation in Human-Robot Interaction: Using Quantitative Bipolar Argumentation Frameworks for Preferences Conflict Resolution
arXiv:2511.03576v1 Announce Type: cross Abstract: While personalisation in Human-Robot Interaction (HRI) has advanced significantly, most existing approaches focus on single-user adaptation, overlooking scenarios involving multiple stakeholders with potentially conflicting preferences. To address this, we propose the Multi-User Preferences Quantitative Bipolar Argumentation Framework (MUP-QBAF), a novel multi-user personalisation framework based on Quantitative Bipolar Argumentation Frameworks (QBAFs) […]
Novel reaction-diffusion PDE model for fingerprint-like pattern emergence via the Schnakenberg mechanism
arXiv:2511.03096v1 Announce Type: new Abstract: Fingerprint analysis and fingerprint identification have been the most widely used tools for human identification. To this day, various models have been proposed to explain how fingerprints are formed, ranging from the fibroblast model, which focuses on cell-collagen interactions, to the buckling of thin layers model, both yielding significant results. […]
DQN Performance with Epsilon Greedy Policies and Prioritized Experience Replay
arXiv:2511.03670v1 Announce Type: cross Abstract: We present a detailed study of Deep Q-Networks in finite environments, emphasizing the impact of epsilon-greedy exploration schedules and prioritized experience replay. Through systematic experimentation, we evaluate how variations in epsilon decay schedules affect learning efficiency, convergence behavior, and reward optimization. We investigate how prioritized experience replay leads to faster […]
Meta-Semantics Augmented Few-Shot Relational Learning
arXiv:2505.05684v4 Announce Type: replace Abstract: Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on leveraging specific relational information, rich semantics inherent in KGs have been largely overlooked. To bridge this gap, we propose PromptMeta, a novel prompted meta-learning […]
Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
arXiv:2511.03095v1 Announce Type: cross Abstract: Modern artificial intelligence has revolutionized our ability to extract rich and versatile data representations across scientific disciplines. Yet, the statistical properties of these representations remain poorly controlled, causing misspecified anomaly detection (AD) methods to falter. Weak or rare signals can remain hidden within the apparent regularity of normal data, creating […]
Large language models require a new form of oversight: capability-based monitoring
arXiv:2511.03106v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) in healthcare has been accompanied by scrutiny of their oversight. Existing monitoring approaches, inherited from traditional machine learning (ML), are task-based and founded on assumed performance degradation arising from dataset drift. In contrast, with LLMs, inevitable model degradation due to changes in […]
FP-AbDiff: Improving Score-based Antibody Design by Capturing Nonequilibrium Dynamics through the Underlying Fokker-Planck Equation
arXiv:2511.03113v1 Announce Type: cross Abstract: Computational antibody design holds immense promise for therapeutic discovery, yet existing generative models are fundamentally limited by two core challenges: (i) a lack of dynamical consistency, which yields physically implausible structures, and (ii) poor generalization due to data scarcity and structural bias. We introduce FP-AbDiff, the first antibody generator to […]