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arXiv:2601.20709v1 Announce Type: new Abstract: Biomedical researchers face increasing challenges in navigating millions of publications in diverse domains. Traditional search engines typically return articles as...
arXiv:2601.20714v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents often struggle in real-world applications where environmental conditions are non-stationary, particularly when reward functions shift or...
arXiv:2601.20716v1 Announce Type: new Abstract: Decentralized Identifiers (DIDs) are increasingly deployed on distributed ledgers, yet systematic cross-platform evidence on their operational behavior remains limited. We...
arXiv:2601.20718v1 Announce Type: new Abstract: A $t$-spanner of an undirected $n$-vertex graph $G$ is a sparse subgraph $H$ of $G$ that preserves all pairwise distances...
arXiv:2601.20720v1 Announce Type: new Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving. Modular pipelines...
arXiv:2601.20722v1 Announce Type: new Abstract: Portals have many applications in the field of computer graphics. Recently, they have found use as a way of artificially...
arXiv:2601.20723v1 Announce Type: new Abstract: We study binary coordination games over graphs under log-linear learning when neighbor actions are conveyed through explicit noisy communication links....
arXiv:2601.20727v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in consequential decisions across healthcare, finance, employment, and public services. Yet accountability remains...
arXiv:2601.20729v1 Announce Type: new Abstract: The Cox Proportional Hazards (PH) model is widely used in survival analysis. Recently, artificial neural network (ANN)-based Cox-PH models have...
arXiv:2601.20730v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however,...
arXiv:2601.20731v1 Announce Type: new Abstract: This paper examines how Large Language Models (LLMs) reproduce societal norms, particularly heterocisnormativity, and how these norms translate into measurable...
arXiv:2601.20732v1 Announce Type: new Abstract: As digital environments (data distribution) are in flux, with new GUI data arriving over time-introducing new domains or resolutions-agents trained...
arXiv:2601.20735v1 Announce Type: new Abstract: We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constraints, like durations...
arXiv:2601.20737v1 Announce Type: new Abstract: Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Despite growing...
arXiv:2601.20738v1 Announce Type: new Abstract: Biased gradient compression with error feedback (EF) reduces communication in federated learning (FL), but under non-IID data, the residual error...
arXiv:2601.20742v1 Announce Type: new Abstract: "Compression Tells Intelligence", is supported by research in artificial intelligence, particularly concerning (multimodal) large language models (LLMs/MLLMs), where compression efficiency...
arXiv:2601.20745v1 Announce Type: new Abstract: As large language models (LLMs) continue to scale, deployment is increasingly bottlenecked by the memory wall, motivating a shift toward...
arXiv:2601.20747v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support and mental health-related interactions outside clinical settings, yet little is...
arXiv:2601.20749v1 Announce Type: new Abstract: How do students develop AI literacy through everyday practice rather than formal instruction? While normative AI literacy frameworks proliferate, empirical...
arXiv:2601.20750v1 Announce Type: new Abstract: We deal with the numerical solution of the time-dependent partial differential equations using the adaptive space-time discontinuous Galerkin (DG) method....
arXiv:2601.20753v1 Announce Type: new Abstract: Preference-Conditioned Policy Learning (PCPL) in Multi-Objective Reinforcement Learning (MORL) aims to approximate diverse Pareto-optimal solutions by conditioning policies on user-specified...
arXiv:2601.20755v1 Announce Type: new Abstract: As large language models (LLMs) move from research to production, understanding how inference engines behave in real time has become...
arXiv:2601.20756v1 Announce Type: new Abstract: Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from partial...
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...