Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.
arXiv:2610.08782v1 Announce Type: cross Abstract: Existing methods for 4D hand-object reconstruction often rely on costly per-sequence optimization, while generative approaches typically synthesize i...
arXiv:2605.12922v2 Announce Type: replace Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instruc...
arXiv:2605.24154v2 Announce Type: replace Abstract: Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users an...
arXiv:2610.05166v2 Announce Type: replace Abstract: A safe action is not necessarily a viable one. A frozen vision-language-action (VLA) policy can favor a locally admissible move that leaves no poli...
arXiv:2610.05828v2 Announce Type: replace Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially redu...
arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models...
arXiv:2402.02399v3 Announce Type: replace-cross Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research p...
arXiv:2508.05880v3 Announce Type: replace-cross Abstract: Understanding human emotions is central to user-facing AI applications, safety alignment, and the simulation of human behavior. As emotional...
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especiall...
arXiv:2508.19819v3 Announce Type: replace-cross Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly str...
arXiv:2601.19595v2 Announce Type: replace-cross Abstract: The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and tr...
arXiv:2603.23184v2 Announce Type: replace-cross Abstract: Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback,...
arXiv:2605.18850v2 Announce Type: replace-cross Abstract: We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to effici...
arXiv:2605.18882v2 Announce Type: replace-cross Abstract: LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, si...
The paper introduces D3S2, a diffusion‑guided dataset distillation framework tailored for semantic segmentation. It tackles long‑tailed class imbalance, pixel‑wise alignment, and high computational cost by first selecting class‑balanced masks and then synthesizing images with a pretrained diffusion model conditioned on those masks. Guided diffusion sampling further refines the data with segmentation‑consistency and class‑wise feature matching losses, achieving superior performance at a 1% compression rate on ADE20K and COCO‑Stuff.
arXiv:2606.16193v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representat...
arXiv:2610.04898v2 Announce Type: replace-cross Abstract: This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortes...
The paper introduces Best‑of‑N Guidance (BoNG), a method that embeds the Best‑of‑N sampling principle directly into the reverse diffusion process of generative models. By performing online BoN selection over denoising particles and using the current best particle as a guidance signal, BoNG steers the particle population toward higher‑reward regions during generation. Empirical results show that BoNG outperforms both standard BoN and SMC across 36 comparisons, achieving the best performance in 29 cases and supporting multi‑output generation with significant speedup.
arXiv:2610.06814v2 Announce Type: replace-cross Abstract: World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet...
The paper investigates the sample complexity of estimating the Fréchet Inception Distance (FID) between high‑dimensional Gaussian distributions when one distribution is known. It establishes tight bias and variance bounds for the empirical plug‑in estimator, shows how higher‑order extrapolation reduces bias, and introduces a new Relative Taylor Debiasing (RTD) algorithm that achieves optimal sample complexity. Experiments on synthetic data and ImageNet confirm the theoretical predictions and demonstrate RTD’s superior accuracy with limited samples.