February 2026

Many real-world control problems, ranging from finance to robotics, evolve in continuous time with non-uniform, event-driven decisions. Standard discrete-time reinforcement learning (RL), based on fixed-step Bellman updates, struggles in this setting: as time gaps shrink, the $Q$-function collapses to the value function $V$, eliminating action ranking. Existing continuous-time methods reintroduce action information via an advantage-rate function $q$. However, they enforce optimality through complicated martingale losses or orthogonality constraints, which are sensitive to the choice of test processes. These […]

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For the past few years, I have spent time trying to bend machine learning into something useful for Amazon FBA demand forecasting. On paper, it did sound straightforward to me with all these past sales in and future demand out. But in reality, Amazon’s data is messy, has so many missing pieces, and changes all the time, which makes it hard to build a reliable model. If you have ever wondered why your Prophet model looked great in […]

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Sparsity is a well-studied technique for compressing deep neural networks (DNNs) without compromising performance. In deep reinforcement learning (DRL), neural networks with up to 5% of their original weights can still be trained with minimal performance loss compared to their dense counterparts. However, most existing methods rely on unstructured fine-grained sparsity, which limits hardware acceleration opportunities due to irregular computation patterns. Structured coarse-grained sparsity enables hardware acceleration, yet typically degrades performance and increases pruning complexity. In this work, […]

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We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research.

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The primary focus of multi-agent reinforcement learning (MARL) has been to study interactions among a fixed number of agents embedded in an environment. However, in the real world, the number of agents is neither fixed nor known a priori. Moreover, an agent can decide to create other agents (for example, a cell may divide, or a company may spin off a division). In this paper, we propose a framework that allows agents to create other agents; we call […]

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Despite legal mandates for the right to be forgotten, AI operators routinely fail to comply with data deletion requests. While machine unlearning (MU) provides a technical solution to remove personal data’s influence from trained models, ensuring compliance remains challenging due to the fundamental gap between MU’s technical feasibility and regulatory implementation. In this paper, we introduce the first economic framework for auditing MU compliance, by integrating certified unlearning theory with regulatory enforcement. We first characterize MU’s inherent verification […]

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If you’ve used Spring Batch in a production environment, you’ve likely received this advice countless times. Sometimes it succeeds. Many times it does not. When it does fail, it can lead to unexpected issues like timeouts, database overload, memory strain, or unnoticed slowdowns that only become apparent when service level agreements (SLAs) are not met. The real problem isn’t Spring Batch. It’s the assumption that one static thread pool size fits all runtime conditions. In this article, I’ll […]

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Introduction: Audio as a First-Class Intelligence Modality Audio is one of the most information-dense and perceptually sensitive modalities through which humans interact with the world. Unlike vision or text, audio is continuous in time, governed by physical acoustics, and immediately evaluated by the human auditory system with millisecond-level sensitivity. These properties make audio uniquely challenging for artificial intelligence systems. Even small distortions—phase discontinuities, temporal smearing, and spectral artifacts—can result in degraded intelligibility, listener fatigue, or loss of immersion. […]

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