Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2602.12043v1 Announce Type: cross Abstract: Obtaining reliable inferences with traditional difference-in-differences (DiD) methods can be difficult. Problems can arise when both outcomes and errors are serially correlated, when there are few clusters or few treated clusters, when cluster sizes vary greatly, and in various other cases. In recent years, recognition of the “staggered adoption” problem has shifted the focus away from inference towards consistent estimation of treatment effects. One of the most popular new estimators is the CSDID […]

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arXiv:2602.03975v1 Announce Type: new Abstract: Test-time computation has become a primary driver of progress in large language model (LLM) reasoning, but it is increasingly bottlenecked by expensive verification. In many reasoning systems, a large fraction of verifier calls are spent on redundant or unpromising intermediate hypotheses. We study reasoning under a emph{verification-cost-limited} setting and ask how verification effort should be allocated across intermediate states. We propose a state-level selective verification framework that combines (i) deterministic feasibility gating over […]

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arXiv:2603.05687v1 Announce Type: new Abstract: Contact-Grounded Policy (CGP) enables fine-grained, contact-rich dexterous manipulation by grounding multi-point contacts through predicting the actual robot state and tactile feedback, and by using a learned contact-consistency mapping to convert these predictions into controller-executable targets for a compliance controller. CGP supports both dense tactile arrays and vision-based tactile sensors mounted on the hand. We collect demonstrations via teleoperation in both simulation and on a physical robot, and evaluate CGP across multiple dexterous manipulation […]

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This study proposes a federated contrastive learning based distributed anomaly detection framework to address privacy protection requirements in IoT environments. The framework builds local encoders on each node to embed high-dimensional time series and network behavior features, and uses representation alignment to reduce distribution differences across devices. Based on this, a contrastive learning objective is introduced to strengthen the compactness of normal patterns in the latent space and to enlarge the boundary between normal and abnormal features, which […]

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arXiv:2602.13421v1 Announce Type: new Abstract: Computation in biological systems is fundamentally energy-constrained, yet standard theories of computation treat energy as freely available. Here, we argue that variational free energy minimization under a Poisson assumption offers a principled path toward an energy-aware theory of computation. Our key observation is that the Kullback-Leibler (KL) divergence term in the Poisson free energy objective becomes proportional to the prior firing rates of model neurons, yielding an emergent metabolic cost term that penalizes […]

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Advances in multi-modal large language models (MLLMs) have inspired time series understanding and reasoning tasks, that enable natural language querying over time series, producing textual analyses of complex temporal dynamics. Recent attempts hybridize numerical time series with their visualized plots, facilitating precise value reasoning and visual structure comprehension for comprehensive time series understanding of MLLMs. However, effective cross-modal integration remains challenging due to fine-grained temporal misalignment across modalities and severe entanglement between shared and modality-specific semantics, which hinder […]

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