Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowledge restructuring, we introduce Dream2Learn (D2L), a framework in which a model autonomously generates structured synthetic experiences from its own internal representations and uses them for self-improvement. Rather than reconstructing past data as in generative replay, D2L enables a classifier to create novel, semantically distinct dreamed classes that are coherent with its learned knowledge yet do […]

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arXiv:2602.03973v1 Announce Type: new Abstract: Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors […]

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Online continual learning (OCL) methods adapt to changing environments without forgetting past knowledge. Similarly, online time series forecasting (OTSF) is a real-world problem where data evolve in time and success depends on both rapid adaptation and long-term memory. Indeed, time-varying and regime-switching forecasting models have been extensively studied, offering a strong justification for the use of OCL in these settings. Building on recent work that applies OCL to OTSF, this paper aims to strengthen the theoretical and practical […]

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Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops to the new DGX Spark, to build personalized, always-on AI assistants like OpenClaw without paying a massive “token tax” for every action. The landscape of modern AI is shifting rapidly. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI powered by platforms like OpenClaw. Whether it […]

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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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arXiv:2603.12482v1 Announce Type: new Abstract: Page-level calligraphy synthesis requires balancing glyph precision with layout composition. Existing character models lack spatial context, while page-level methods often compromise brushwork detail. In this paper, we present textbf{CalliMaster}, a unified framework for controllable generation and editing that resolves this conflict by decoupling spatial planning from content synthesis. Inspired by the human cognitive process of “planning before writing”, we introduce a coarse-to-fine pipeline textbf{(Text $rightarrow$ Layout $rightarrow$ Image)} to tackle the combinatorial complexity […]

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arXiv:2602.09959v1 Announce Type: cross Abstract: We study the problem of learning multi-index models (MIMs), where the label depends on the input $boldsymbol{x} in mathbb{R}^d$ only through an unknown $mathsf{s}$-dimensional projection $boldsymbol{W}_*^mathsf{T} boldsymbol{x} in mathbb{R}^mathsf{s}$. Exploiting the equivariance of this problem under the orthogonal group $mathcal{O}_d$, we obtain a sharp harmonic-analytic characterization of the learning complexity for MIMs with spherically symmetric inputs — which refines and generalizes previous Gaussian-specific analyses. Specifically, we derive statistical and computational complexity lower bounds […]

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We study a speculative trading problem within the exploratory reinforcement learning (RL) framework of Wang et al. [2020]. The problem is formulated as a sequential optimal stopping problem over entry and exit times under general utility function and price process. We first consider a relaxed version of the problem in which the stopping times are modeled by the jump times of Cox processes driven by bounded, non-randomized intensity controls. Under the exploratory formulation, the agent’s randomized control is […]

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Modern iOS applications that have evolved over multiple years often accumulate complex UI layers, deeply integrated analytics, and carefully tuned performance optimizations. These applications typically rely on UIKit as the foundational framework, making any transition to SwiftUI a non-trivial architectural decision. SwiftUI introduces a declarative paradigm that improves developer productivity and maintainability, but migrating large production systems requires careful orchestration to avoid regressions in performance, reliability, and user experience. A structured migration approach becomes essential when the goal […]

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