arXiv:2508.17622v3 Announce Type: replace Abstract: We study fairness-accuracy tradeoffs when a single predictive model must serve multiple demographic groups. A useful tool for understanding this tradeoff is the fairness-accuracy (FA) Pareto frontier, which characterizes the set of models that cannot be improved in either fairness or accuracy without worsening the other. While characterizing the FA frontier requires full knowledge of the data distribution, we focus on the finite-sample regime, quantifying how well a designer can approximate any point […]

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If you are like me and spend most of your time thinking about what happens inside the model,and not much on the hardware side of things this video will definitely fascinate you. Dwarkesh and Reiner Pope spent two hours at a blackboard going through the actual hardware economics of training and running LLMs and i got to learn a lot things i previously didn’tknow obviously. One of my biggest takeaways for me was the 6ND formula for calculating […]

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arXiv:2603.02159v1 Announce Type: new Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance yields principled and well-calibrated EU. Moreover, the […]

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AI agent performance is not just a modeling problem; it is fundamentally a systems problem. A modern agent combines an LLM with a harness, software that mediates the LLM’s interaction with tools and manages the cycle of reasoning and feedback: you can think of the harness as the operating system around the model. As models improve, the performance bottleneck shifts from the model’s ability to reason to the harness’s ability to translate model intent into actions and reflect […]

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Hi guys, I just implemented the RL2 algorithm (https://arxiv.org/abs/1611.02779) with PyTorch. The code is here: https://github.com/fatcatZF/RL2-Torch . I used a shared GRU feature extractor, with separate MLP heads for actor and critic. The neural network was optimized with the PPO algorithm. submitted by /u/ZitaLovesCats [link] [comments]

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arXiv:2601.18938v1 Announce Type: cross Abstract: Imputing missing node features in graphs is challenging, particularly under high missing rates. Existing methods based on latent representations or global diffusion often fail to produce reliable estimates, and may propagate errors across the graph. We propose FSD-CAP, a two-stage framework designed to improve imputation quality under extreme sparsity. In the first stage, a graph-distance-guided subgraph expansion localizes the diffusion process. A fractional diffusion operator adjusts propagation sharpness based on local structure. In […]

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arXiv:2604.13122v1 Announce Type: new Abstract: Covert wireless communication aims to establish a reliable link while hiding the transmission from an adversary. In wireless settings, uncertainty plays a central role in this tradeoff: it can help mask the signal from a warden, but it also complicates robust system design. This raises a basic question: under bounded uncertainty, are reliability and covertness governed by the same adverse conditions? If not, robust covert design cannot be reduced to a single worst-case […]

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Check Point’s Cyber Security Report 2026 shows 70% increase in cyber attacks since 2023. 60% of executives reported their organizations faced AI-powered attacks, but only 7% had deployed AI defenses at scale. Moody’s 2026 cyber outlook warns that AI-related threats will “become more prevalent and pronounced”

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