May 2026

Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable rewards drives emergent reasoning on math and code but depends on a programmatic verifier that cannot reach open-ended tasks, while preference optimization handles open-ended generation yet forgoes the continuous exploration that powers online RL. Closing this gap requires a verifier for open-ended quality, but a scalar reward model is the wrong shape for the job. Quality is multi-dimensional, and […]

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Unmanned Aerial Vehicles in dynamic environments face telemetry outages, structural vibrations, and regime-dependent noise that invalidate the stationary covariance assumptions of classical Kalman filters. The Sage-Husa Kalman Filter (SHKF) estimates noise statistics online, but its reliance on a static, scalar forgetting factor forces a strict compromise between steady-state stability and transient responsiveness. We introduce the N-Deep Recurrent Sage-Husa Filter (NDR-SHKF), which replaces this scalar parameter with a vector-valued memory attenuation policy learned by a hierarchical recurrent network operating […]

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Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that ignore stable, intra-patient variability. As such, population-based interpretation can mask meaningful deviation from an individual’s baseline, risking delayed disease detection. To remedy this, there have been increasing efforts to personalize blood biomarker interpretation using individual testing histories. However, these methods may overfit to sparse data, inflating false-positive rates and unnecessary follow-up, and can also unwittingly include unrecognized or subclinical disease. […]

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Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based quantum simulators. These devices generally generate data in the form of images; we […]

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So I’ve been working with AI/ML for the past couple of years, and it has been an amazing experience. I still remember using GPT-2 for the first time and being completely blown away by it. Seeing how far the technology has come since then is honestly mind-blowing. I genuinely love working in AI, learning about it, and experimenting with new tools and ideas. But over the past couple of years, something has started to weigh on me: the […]

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The global race to develop AI-powered smart glasses is picking up speed with tech companies everywhere rushing to build light, wearable devices that deliver real-time digital experiences. Big consumer brands tend to grab all the headlines but plenty of hardware startups are quietly creating the key technologies that will define wearable computing’s future. LetinAR is carving out its place as a major supplier inside the emerging AI glasses ecosystem. Instead of making consumer devices themselves, LetinAR is focusing […]

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I want to control my drone with RL by outputting joystick commands. What’s generally better for sim2real: controlling in acro mode (body rates, rad/s) or angle mode (attitude targets, rad)? My intuition is that angle control provides a higher abstraction layer, which may reduce sim2real issues and allow lower control frequency. But it also requires strong consistency between the low-level PID attitude controller on the real drone and in simulation. submitted by /u/Big_Pin_5549 [link] [comments]

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