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arXiv:2601.17912v2 Announce Type: new Abstract: Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of...
arXiv:2601.17915v1 Announce Type: new Abstract: LLM agents excel when environments are mostly static and the needed information fits in a model's context window, but they...
arXiv:2601.17916v1 Announce Type: new Abstract: Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocardiogram (ECG). Large Language...
arXiv:2601.17917v2 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to...
arXiv:2601.17918v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) hold significant promise for medical applications, yet their deployment is often constrained by insufficient alignment and...
arXiv:2601.17920v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) close the loop between experiment design, automated execution, and data-driven decision making, and they provide a demanding...
arXiv:2601.17921v1 Announce Type: new Abstract: Low-rank adaption (LoRA) is a representative method in the field of parameter-efficient fine-tuning (PEFT), and is key to Democratizating the...
arXiv:2601.17923v1 Announce Type: new Abstract: Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this...
arXiv:2601.17927v1 Announce Type: new Abstract: Controllable image generation is fundamental to the success of modern generative AI, yet it faces a critical trade-off between semantic...
arXiv:2601.17931v1 Announce Type: new Abstract: The map of elections framework is a methodology for visualizing and analyzing election datasets. So far, the framework was restricted...
arXiv:2601.17933v1 Announce Type: new Abstract: We propose a perspective in which learning is an intrinsically dissipative process. Forgetting and regularization are not heuristic add-ons but...
arXiv:2601.17934v1 Announce Type: new Abstract: Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due...
arXiv:2601.17935v1 Announce Type: new Abstract: Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering....
arXiv:2601.17937v1 Announce Type: new Abstract: AI alignment relies on annotator judgments, yet annotation pipelines often treat annotators as interchangeable, obscuring how their social position shapes...
arXiv:2601.17939v1 Announce Type: new Abstract: In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones,...
arXiv:2601.17940v1 Announce Type: new Abstract: Large-scale ML accelerators rely on large numbers of PEs, imposing strict bounds on the area and energy budget of each...
arXiv:2601.17942v1 Announce Type: new Abstract: Text-to-SQL has emerged as a prominent research area, particularly with the rapid advancement of large language models (LLMs). By enabling...
arXiv:2601.17944v1 Announce Type: new Abstract: We consider a setting in which a group of agents share resources that must be allocated among them in each...
arXiv:2601.17946v1 Announce Type: new Abstract: Large Language Model (LLM) chatbots like ChatGPT have emerged as cognitive scaffolding for autistic users, yet the tension between their...
arXiv:2601.17947v1 Announce Type: new Abstract: Mechanical properties of red blood cells (RBCs) are promising biomarkers for hematologic and systemic disease, motivating microfluidic assays that probe...
arXiv:2601.17950v1 Announce Type: new Abstract: The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from...
arXiv:2601.17952v1 Announce Type: new Abstract: Interpretability remains a key challenge for deploying large language models (LLMs) in clinical settings such as Alzheimer's disease progression diagnosis,...
arXiv:2601.17954v1 Announce Type: new Abstract: We investigate the neural Actor Critic algorithm using shallow neural networks for both the Actor and Critic models. The focus...
arXiv:2601.17957v1 Announce Type: new Abstract: Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and...