arXiv:2602.02502v1 Announce Type: new Abstract: Continual learning in natural language processing plays a crucial role in adapting to evolving data and preventing catastrophic forgetting. Despite significant progress, existing methods still face challenges, such as inefficient parameter reuse across tasks, risking catastrophic forgetting when tasks are dissimilar, and the unnecessary introduction of new parameters for each task, which hampers knowledge sharing among similar tasks. To tackle these issues, we propose a Sparse Adapter Fusion Method (SAFM), which dynamically fuses […]

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arXiv:2603.25832v1 Announce Type: new Abstract: Plasma modeling is central to the design of nuclear fusion reactors, yet simulating collisional plasma kinetics from first principles remains a formidable computational challenge: the Vlasov-Maxwell-Landau (VML) system describes six-dimensional phase-space transport under self-consistent electromagnetic fields together with the nonlinear, nonlocal Landau collision operator. A recent deterministic particle method for the full VML system estimates the velocity score function via the blob method, a kernel-based approximation with $O(n^2)$ cost. In this work, we […]

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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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This study aims to develop and improve machine learning-based post-processing models for precipitation, temperature, and wind speed predictions using the Mesoscale Model (MSM) dataset provided by the Japan Meteorological Agency (JMA) for 18 locations across Japan, including plains, mountainous regions, and islands. By incorporating meteorological variables from grid points surrounding the target locations as input features and applying feature selection based on correlation analysis, we found that, in our experimental setting, the LightGBM-based models achieved lower RMSE than […]

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Top 10 Physical AI Models NVIDIA Isaac GR00T N-Series (N1.5 / N1.6 / N1.7) Google DeepMind Gemini Robotics 1.5 Physical Intelligence π0 / π0.5 / π0.7 Figure AI Helix OpenVLA Octo AGIBOT BFM and GCFM Gemini Robotics On-Device NVIDIA Cosmos World Foundation Models SmolVLA (HuggingFace LeRobot) The gap between language model capabilities and robotic deployment has been narrowing considerably over the past 18 months. A new class of foundation models — purpose-built not for text generation but for […]

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Mean-field reinforcement learning (MF-RL) scales multi-agent RL to large populations by reducing each agent’s dependence on others to a single summary statistic — the mean action. However, this reduction requires every agent to act at every time step; when some agents are idle, the mean action is simply undefined. Addressing asynchrony therefore requires a different summary statistic — one that remains defined regardless of which agents act. The population distribution $μin Δ(mathcal{O})$ — the fraction of agents at […]

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Precision mental health requires treatment decisions that account for heterogeneous symptoms reflecting multiple clinical domains. However, existing methods for estimating individualized treatment effects (ITE) rely on a single summary outcome or a specific set of observed symptoms or measures, which are sensitive to symptom selection and limit generalizability to unmeasured yet clinically relevant domains. We propose DRIFT, a new maximin framework for estimating robust ITEs from high-dimensional item-level data by leveraging latent factor representations and adversarial learning. DRIFT […]

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arXiv:2603.22442v1 Announce Type: new Abstract: Cryptographic migration driven by algorithm deprecation, regulatory change, and post-quantum readiness requires more than an inventory of cryptographic assets. Existing Cryptographic Bills of Materials (CBOMs) are typically tool- or inventory-derived. They lack architectural intent, rationale, and security context, limiting their usefulness for migration planning. This paper introduces Security-Aware Architecture Tradeoff Analysis Method (SATAM), a security-aware adaptation of scenario-based architecture evaluation that derives an architecture-grounded, context-sensitive CBOM. SATAM integrates established approaches: ATAM, arc42, STRIDE, […]

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