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arXiv:2601.20803v1 Announce Type: new Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning of one-shot relation extraction. Specifically, we introduce...
arXiv:2601.20806v1 Announce Type: new Abstract: This study provides the first comprehensive empirical mapping of how organizational structures and research portfolios co-occur across U.S. Library and...
arXiv:2601.20807v1 Announce Type: new Abstract: In this paper, we introduce a mixed virtual element method to approximate the eigenvalues and eigenfunctions of the two-dimensional elasticity...
arXiv:2601.20810v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at code generation but struggle with complex problems. Retrieval-Augmented Generation (RAG) mitigates this issue by...
arXiv:2601.20815v1 Announce Type: new Abstract: Explanations provided by Self-explainable Graph Neural Networks (SE-GNNs) are fundamental for understanding the model's inner workings and for identifying potential...
arXiv:2601.20822v1 Announce Type: new Abstract: We consider a wireless network comprising multiple singleantenna repeaters that amplify and instantaneously re-transmit received signals in a full-duplex (FD)...
arXiv:2601.20825v1 Announce Type: new Abstract: The construction of Maximum Distance Profile (MDP) convolutional codes in general requires the use of very large finite fields. In...
arXiv:2601.20827v1 Announce Type: new Abstract: Orthogonal Time Frequency Space (OTFS) modulation offers robust performance in high-mobility scenarios by transforming time-varying channels into the delay-Doppler (DD)...
arXiv:2601.20829v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning abilities of large language models (LLMs), yet training often...
arXiv:2601.20831v1 Announce Type: new Abstract: Foundation models rely on in-context learning for personalized decision making. The limited size of this context window necessitates memory compression...
arXiv:2601.20833v1 Announce Type: new Abstract: Autonomous scientific discovery with large language model (LLM)-based agents has recently made substantial progress, demonstrating the ability to automate end-to-end...
arXiv:2601.20834v1 Announce Type: new Abstract: Language model representations often contain linear directions that correspond to high-level concepts. Here, we study the dynamics of these representations:...
arXiv:2601.20835v1 Announce Type: new Abstract: Generating 3D humans that functionally interact with 3D scenes remains an open problem with applications in embodied AI, robotics, and...
arXiv:2601.20838v1 Announce Type: new Abstract: Reward models (RMs) are central to aligning large language models (LLMs) with human values but have received less attention than...
arXiv:2601.20841v1 Announce Type: new Abstract: The Reynolds equation is derived from the incompressible Navier Stokes equations under the lubrication assumptions of a long and thin...
arXiv:2601.20843v1 Announce Type: new Abstract: This paper introduces a novel Deep Researcher architecture designed to generate detailed research reports on complex PhD level topics by...
arXiv:2601.20844v1 Announce Type: new Abstract: This paper studies the minimal dimension required to embed subset memberships ($m$ elements and ${m\choose k}$ subsets of at most...
arXiv:2601.20845v1 Announce Type: new Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require domain-specific...
arXiv:2601.20846v1 Announce Type: new Abstract: Whereas reinforcement learning has been applied with success to a range of robotic control problems in complex, uncertain environments, reliance...
arXiv:2601.20848v1 Announce Type: new Abstract: Despite growing efforts to mitigate unfairness in recommender systems, existing fairness-aware methods typically fix the fairness requirement at training time...
arXiv:2601.20852v1 Announce Type: new Abstract: Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving...
arXiv:2601.20854v1 Announce Type: new Abstract: Tabular data remains a challenging domain for generative models. In particular, the standard Variational Autoencoder (VAE) architecture, typically composed of...
arXiv:2601.20856v1 Announce Type: new Abstract: Although the capabilities of large language models have been increasingly tested on complex reasoning tasks, their long-horizon planning abilities have...
arXiv:2601.20857v1 Announce Type: new Abstract: Neural Radiance Fields and 3D Gaussian Splatting have advanced novel view synthesis, yet still rely on dense inputs and often...