arXiv:2602.21252v1 Announce Type: new Abstract: Security monitoring systems typically treat anomaly detection as identifying statistical deviations from observed data distributions. In cryptographic traffic analysis, however, violations are defined not by rarity but by explicit policy constraints, including key reuse prohibition, downgrade prevention, and bounded key lifetimes. This fundamental mismatch limits the interpretability and adaptability of conventional anomaly detection methods. We introduce INTACT (INTent-Aware Cryptographic Traffic), a policy-conditioned framework that reformulates violation detection as conditional constraint learning. Instead of […]

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arXiv:2601.21831v1 Announce Type: new Abstract: We introduce the use of latent subspaces in the exponential parameter space of product manifolds of categorial distributions, as a tool for learning generative models of discrete data. The low-dimensional latent space encodes statistical dependencies and removes redundant degrees of freedom among the categorial variables. We equip the parameter domain with a Riemannian geometry such that the spaces and distances are related by isometries which enables consistent flow matching. In particular, geodesics become […]

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Constrained Markov decision processes (CMDPs) provide a principled model for handling constraints, such as safety and other auxiliary objectives, in reinforcement learning. The common approach of using additive-cost constraints and dual variables often hinders off-policy scalability. We propose a Control as Inference formulation based on stochastic decision horizons, where constraint violations attenuate reward contributions and shorten the effective planning horizon via state-action-dependent continuation. This yields survival-weighted objectives that remain replay-compatible for off-policy actor-critic learning. We propose two violation […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Governing and Scaling AI Agents: Operational Excellence and the Road Ahead By @denisp [ 23 Min read ] Success isn’t building the agent; it’s managing it. From “AgentOps” to ROI dashboards, here is the operational playbook for scaling Enterprise AI. Read More. The Seven […]

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arXiv:2407.01111v2 Announce Type: replace-cross Abstract: Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizing distribution discrepancies between treatment groups in latent space, focusing on global alignment. However, the fruitful aspect of local proximity, where similar units exhibit similar outcomes, is often overlooked. In this study, we propose Proximity-enhanced CounterFactual Regression (CFR-Pro) to exploit proximity for enhancing representation balancing within the HTE estimation context. Specifically, […]

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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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The past few weeks have been tough for Anthropic. The California-based AI company and OpenAI rival Anthropic is now in a legal dispute with the U.S. Department of Defense (DoD). The reason? Well, Anthropic has denied the Pentagon’s demand for unrestricted access to its AI models in military settings. In the negotiations table, the AI company had been seeking assurances from the Pentagon that its AI systems would not be used for certain activities, including mass surveillance of […]

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arXiv:2603.00688v1 Announce Type: new Abstract: Reading has always been an integral part of both professional and personal life. Character and layout recognition and understanding by computers are well-explored areas. Nevertheless, how characters and layout are read and perceived by humans remains relatively underexplored. This work contributes to the field of human-document interaction (HDI) by investigating the effects of character and layout personalization on readability. The paper presents an empirical study on how parts-of-speech (POS)-based character and layout modifications […]

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arXiv:2602.04402v2 Announce Type: replace Abstract: Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only existing users of an app) and/or the whole population (e.g., all potential app users). This raises the question of how well models generalize under performativity. For example, how well can we draw insights about new app users based on existing users when both of them react to the app’s predictions? We address this […]

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This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day course designed to teach machine learning concepts to students aged 12 to 17 through programming-free robotics activities. Machine Learning with Bricks is an open source platform and combines interactive visualizations with LEGO robotics to teach three core algorithms: KNN, linear regression, and Q-learning. Students learn by collecting data, training models, and interacting with robots via a web-based interface. Pre- and post-surveys with 14 students […]

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