arXiv:2601.18626v3 Announce Type: replace-cross Abstract: Natural gradients have long been studied in deep reinforcement learning due to their fast convergence properties and covariant weight updates. However, computing natural gradients requires inversion of the Fisher Information Matrix (FIM) at each iteration, which is computationally prohibitive in nature. In this paper, we present an efficient and scalable natural policy optimization technique that leverages a rank-1 approximation to full inverse-FIM. We theoretically show that under certain conditions, a rank-1 approximation to […]

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arXiv:2601.06429v1 Announce Type: cross Abstract: Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelets that capture class-discriminative temporal features. To bridge this gap, we propose UniShape, a unified shape-aware foundation model designed for time series classification. UniShape incorporates a shape-aware adapter that adaptively aggregates multiscale discriminative subsequences (shapes) into […]

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arXiv:2601.02444v1 Announce Type: new Abstract: The rapid advancement of speech synthesis technologies, including text-to-speech (TTS) and voice conversion (VC), has intensified security and privacy concerns related to voice cloning. Recent defenses attempt to prevent unauthorized cloning by embedding protective perturbations into speech to obscure speaker identity while maintaining intelligibility. However, adversaries can apply advanced purification techniques to remove these perturbations, recover authentic acoustic characteristics, and regenerate cloneable voices. Despite the growing realism of such attacks, the robustness of […]

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SOND, a startup led by Bose’s former head of sleep products, emerged from stealth with $7M in funding for its AI-powered sleep earbuds.

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Modern production systems generate more data than most developers can realistically process. Every request emits logs. Every service exports metrics. Every dependency introduces another layer of signals. In theory, this should make systems easier to understand. In practice, it does the opposite. Dashboards become dense, alerts become noisy, and when something breaks, the same questions still come up. What is actually wrong? Who is affected? Where do you even start? The problem is not observability. It is interpretation. […]

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Not everything is about saving time. It’s about solving a real problem and things that matter. Everywhere you look, someone’s building an AI tool to save you time. There’s an app to plan your meals, one to write your emails, another to manage your money automatically. It’s all supposed to make life effortless. But lately, I’ve started to wonder if effort itself is the point. A few years ago, I set up a budgeting app that tracked everything. […]

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arXiv:2602.13204v1 Announce Type: new Abstract: Because wireless communication is dynamic and has inherent defects, routing algorithms are crucial in the quickly evolving field of mobile ad hoc networks, or MANETs This study looks at the many security problems that MANETs encounter. These problems, which pose major risks to network performance, include flooding, sinkholes, and black hole assaults to address these challenges. We introduce the Hybrid Secure Routing Protocol (HSRP), which enhances the security and robustness of routing operations […]

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As AI agents begin executing trades, managing risk, and interacting with markets through natural language, exchanges are quietly undergoing one of their most significant architectural shifts since the invention of the order book itself. The platforms that win the next cycle may not be the ones with the deepest order books, they may be the ones that are the most programmable, the most interoperable, and the most agent-ready. Victor Wu is building that shift from the inside at […]

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arXiv:2604.09689v2 Announce Type: new Abstract: Machine learning progress has historically prioritized model-centric innovations, yet achievable performance is frequently capped by the intrinsic complexity of the data itself. In this work, we isolate and quantify the impact of instance density (measured by face count) as a primary driver of data complexity. Rather than simply observing that “crowded scenes are harder,” we rigorously control for class imbalance to measure the precise degradation caused by density alone. Controlled experiments on the […]

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