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arXiv:2601.20757v1 Announce Type: new Abstract: For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for...
arXiv:2601.20758v1 Announce Type: new Abstract: We present ScaleFree, a GPU-accelerated adaptive Kernel Density Estimation (KDE) algorithm for scalable, interactive multiscale point cloud exploration. With this...
arXiv:2601.20759v1 Announce Type: new Abstract: Building on the collaborative Equational Theories project initiated by Terence Tao fifteen months ago, and combining it with ideas coming...
arXiv:2601.20760v1 Announce Type: new Abstract: Re-inforcement learning from human feedback (RLHF) has been effective in the task of AI alignment. However, one of the key...
arXiv:2601.20761v1 Announce Type: new Abstract: In this letter, we address the problem of developing quantum state tomography (QST) methods that remain valid at any time...
arXiv:2601.20764v1 Announce Type: new Abstract: Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads....
arXiv:2601.20765v1 Announce Type: new Abstract: A fundamental challenge in offline reinforcement learning is distributional shift. Scarce data or datasets dominated by out-of-distribution (OOD) areas exacerbate...
arXiv:2601.20772v1 Announce Type: new Abstract: COMET-SG1 is a lightweight, stability-oriented autoregressive regression model designed for time-series prediction on edge and embedded AI systems. Unlike recurrent...
arXiv:2601.20773v1 Announce Type: new Abstract: Deployed machine learning systems must continuously evolve as data, architectures, and regulations change, often without access to original training data...
arXiv:2601.20774v1 Announce Type: new Abstract: Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a...
arXiv:2601.20775v1 Announce Type: new Abstract: This paper advances the theoretical understanding of active learning label complexity for decision trees as binary classifiers. We make two...
arXiv:2601.20776v1 Announce Type: new Abstract: This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance control. Unlike...
arXiv:2601.20779v1 Announce Type: new Abstract: In an ordinal election, two candidates are said to be perfect clones if every voter ranks them adjacently. The independence...
arXiv:2601.20781v1 Announce Type: new Abstract: Gaussian process regression uses data measured at sensor locations to reconstruct a spatially dependent function with quantified uncertainty. However, if...
arXiv:2601.20783v1 Announce Type: new Abstract: Modern blockchain applications benefit from the ability to specify sequencing constraints on the transactions that interact with them. This paper...
arXiv:2601.20784v1 Announce Type: new Abstract: Neuro-symbolic AI systems integrate neural perception with symbolic reasoning to enable data-efficient, interpretable, and robust intelligence beyond purely neural models....
arXiv:2601.20789v1 Announce Type: new Abstract: Open-weight coding agents should hold a fundamental advantage over closed-source systems: they can be specialized to private codebases, encoding repository-specific...
arXiv:2601.20791v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models have achieved rapid progress, yet their demographic biases, particularly gender bias, remain largely unexplored. We present...
arXiv:2601.20792v1 Announce Type: new Abstract: Privacy policies are supposed to provide notice. But what if substantive information appears only where users skip it? We identify...
arXiv:2601.20796v1 Announce Type: new Abstract: Transformer-based multimodal large language models often exhibit in-context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do transformers...
arXiv:2601.20797v1 Announce Type: new Abstract: This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and...
arXiv:2601.20799v1 Announce Type: new Abstract: We propose a systematic framework for constructing geometric integrators for Hamiltonian systems on Jacobi manifolds. By combining Poissonization of Jacobi...
arXiv:2601.20800v1 Announce Type: new Abstract: We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence...
arXiv:2601.20802v1 Announce Type: new Abstract: Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods...