arXiv:2602.24207v1 Announce Type: cross Abstract: The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we see, and later use to retrain on. This dynamic was formalized by Perdomo et al. 2020 in their work on performative prediction. Our main result is an unconditional reduction showing that any no-regret algorithm deployed in performative settings converges to a (mixed) performatively stable equilibrium: a solution in which models […]

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Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous nature of IoT data, stemming from differences in device capabilities, data formats, and communication constraints, poses significant challenges to maintaining both global model performance and privacy. In the context […]

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How are you, hacker? 🪐 What’s happening in tech today, January 16, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Prohibition went into effect in the United States in 1920, Space Shuttle Columbia disintegrated upon re-entry in 2003, First Youtube video was posted in 2006, and we present you with these top quality stories. From The Secret Math Behind Every Creative Breakthrough to How to Build a DAO from Scratch with […]

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Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has been extraordinarily successful for well-defined tasks where goals are fixed and evaluation criteria are explicit. However, if artificial systems are to move toward true autonomy-operating over long horizons and across evolving contexts-objectives may become ill-defined, shifting, or entirely absent. In such settings, a […]

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Ladybird adopts Rust, with help from AI Really interesting case-study from Andreas Kling on advanced, sophisticated use of coding agents for ambitious coding projects with critical code. After a few years hoping Swift’s platform support outside of the Apple ecosystem would mature they switched tracks to Rust their memory-safe language of choice, starting with an AI-assisted port of a critical library: Our first target was LibJS , Ladybird’s JavaScript engine. The lexer, parser, AST, and bytecode generator are […]

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arXiv:2602.13321v1 Announce Type: new Abstract: Detecting jailbreak attempts in clinical training large language models (LLMs) requires accurate modeling of linguistic deviations that signal unsafe or off-task user behavior. Prior work on the 2-Sigma clinical simulation platform showed that manually annotated linguistic features could support jailbreak detection. However, reliance on manual annotation limited both scalability and expressiveness. In this study, we extend this framework by using experts’ annotations of four core linguistic features (Professionalism, Medical Relevance, Ethical Behavior, and […]

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Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority voting consensus. However, existing TTRL methods rely exclusively on positive pseudo-labeling strategies. Such reliance becomes vulnerable under challenging scenarios where answer distributions are highly dispersed, resulting in weak consensus that inadvertently reinforces incorrect trajectories as supervision signals. In this paper, we propose SCRL (Selective-Complementary Reinforcement Learning), a robust test-time reinforcement learning framework that effectively mitigates label […]

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Was drafting CVPR Rebuttal (after convincing myself to give a shot for days) and one of the reviewers had asked us to provide evidence for a particular statement, so we are planning to cite papers for it. Are we allowed to use additional page for references? Thanks submitted by /u/Forsaken-Order-7376 [link] [comments]

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