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arXiv:2603.19611v1 Announce Type: new Abstract: In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks by conditioning on a small set of input-output demonstrations, …
arXiv:2603.19617v1 Announce Type: new Abstract: Federated learning (FL) has emerged as a communication-efficient algorithmic framework for distributed learning across multiple agents. While standard FL formulations …
arXiv:2603.19621v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, …
arXiv:2603.19624v1 Announce Type: new Abstract: Conventional machine learning pipelines often struggle to recognize categories absent from the original trainingset. This gap typically reduces accuracy, as …
arXiv:2603.19633v1 Announce Type: new Abstract: This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical …
arXiv:2603.19636v1 Announce Type: new Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D …
arXiv:2603.19648v1 Announce Type: new Abstract: Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely …
arXiv:2603.19653v1 Announce Type: new Abstract: Recent Deep Neural Networks (DNN) applications ask for techniques that can explain their behavior. Existing solutions, such as Feature Guided …
arXiv:2603.19664v1 Announce Type: new Abstract: The key-value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers …
arXiv:2603.19670v1 Announce Type: new Abstract: Existing analyses of reverse diffusion often propagate sampling error in the Euclidean geometry underlying \(\Wtwo\) along the entire reverse trajectory. …
arXiv:2603.19677v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily …
Policymakers debate if we even need deep ocean mining and if we can do it safely.