Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL’s sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement […]

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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:2604.21933v1 Announce Type: new Abstract: Electronic health records (EHRs) have improved data accessibility but have also introduced cognitive burden for physicians, given the sheer volume and complexity of the data involved. Advances in large language models (LLMs) create new opportunities to rethink how clinicians interact with medical data through dynamic, adaptive interfaces. In this position paper, we explore how generative AI can support physicians’ information needs by enabling more dynamic interactions with patient data. Through semi-structured interviews with […]

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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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Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating system environments with actual graphical user interfaces. Each one needs to run real software. Each one needs to handle unpredictable crashes. And you […]

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arXiv:2501.02505v4 Announce Type: replace-cross Abstract: Ranking items based on pairwise comparisons is common, from using match outcomes to rank sports teams to using purchase or survey data to rank consumer products. Statistical inference-based methods such as the Bradley-Terry model, which extract rankings based on an underlying generative model, have emerged as flexible and powerful tools to tackle ranking in empirical data. In situations with limited and/or noisy comparisons, it is often challenging to confidently distinguish the performance of […]

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Recently I asked an AI agent about some project management tools recommendations and it suggested Asana, Monday.com and ClickUp. The agent was smart enough to explain why each tool fits my criteria and why it’s the best. However, what I couldn’t know was that did any of these companies pay for their tools to be on this list that was recommended to me? The agent showed no disclosure, so there was no way to check. And this is […]

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arXiv:2211.11368v5 Announce Type: replace-cross Abstract: In a mixed generalized linear model, the goal is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular class of estimators which output the top two eigenvectors of a suitable data-dependent matrix. However, despite the wide […]

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TL;DR: I taught an AI agent to scaffold an entire Clean Architecture .NET solution in seconds. Here’s what Agent Skills are, why they matter, and how this changes the way we think about AI-assisted development.** There’s a moment every .NET developer knows too well. You’ve got a green-field project, a clear architectural vision, and you’re ready to ship. Then you open Visual Studio and spend the next 15 minutes clicking through dialogs, creating solution folders, wiring up project […]

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