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arXiv:2602.21320v1 Announce Type: new Abstract: Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement …
arXiv:2602.21321v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale large vision and …
arXiv:2602.21328v1 Announce Type: new Abstract: We study the problem of opportunistic approachability: a generalization of Blackwell approachability where the learner would like to obtain stronger …
arXiv:2602.21340v1 Announce Type: new Abstract: Representing the past in a compressed, efficient, and informative manner is a central problem for systems trained on sequential data. …
arXiv:2602.21342v1 Announce Type: new Abstract: Representation learning has been essential for graph machine learning tasks such as link prediction, community detection, and network visualization. Despite …
arXiv:2602.21371v1 Announce Type: new Abstract: Multi-Head Attention (MHA) is the core computational primitive underlying modern Large Language Models (LLMs). However, MHA suffers from a fundamental …
arXiv:2602.21390v1 Announce Type: new Abstract: We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that …
arXiv:2602.21408v1 Announce Type: new Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian …
arXiv:2602.21415v1 Announce Type: new Abstract: Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available …
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent …
arXiv:2602.21426v1 Announce Type: new Abstract: We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. …
arXiv:2602.21429v1 Announce Type: new Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. …