High-fidelity telepresence requires the reconstruction of photorealistic 3D avatars in real-time to facilitate immersive interaction. Current solutions face a dichotomy: they are either computationally expensive multi-view systems (e.g., Codec Avatars) or lightweight mesh-based approximations that suffer from the “uncanny valley” effect due to a lack of high-frequency detail. In this paper, we propose Mono-Splat, a novel framework for reconstructing high-fidelity, animatable human avatars from a single monocular webcam video stream. Our method leverages 3D Gaussian Splatting (3DGS) combined […]

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arXiv:2603.15774v1 Announce Type: new Abstract: Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classification of tumors. However, the extremely high resolution of WSIs makes end-to-end training impractical compared to typical image analysis tasks. To address this, most approaches use pre-trained feature extractors to obtain fixed representations of whole slides, which are then combined with Multiple Instance Learning (MIL) for downstream tasks. These feature extractors are typically pre-trained […]

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This is the README for OpenLoop, a feedback collection platform that’s currently live and functional: n ╔══════════════════════════════════════════════════════════╗ ║ ║ ║ ⚠️ IMPORTANT DISCLAIMER ⚠️ ║ ║ ║ ║ This project was ENTIRELY conceived, built, debugged, ║ ║ deployed, and is managed by autonomous AI agents. ║ ║ No humans wrote any code here. ║ ║ ║ ║ This disclaimer is the ONLY piece of human-written ║ ║ content in this repo. ║ ║ ║ ╚══════════════════════════════════════════════════════════╝ That disclaimer is real. […]

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arXiv:2509.22124v2 Announce Type: replace Abstract: Regularized linear regression is central to machine learning, yet its high-dimensional behavior with informative priors remains poorly understood. We provide the first exact asymptotic characterization of training and test risks for maximum a posteriori (MAP) regression with Gaussian priors centered at a domain-informed initialization. Our framework unifies ridge regression, least squares, and prior-informed estimators, and — using random matrix theory — yields closed-form risk formulas that expose the bias-variance-prior tradeoff, explain double descent, […]

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arXiv:2604.15451v1 Announce Type: new Abstract: Large-scale visual learning is increasingly limited by training cost. Existing knowledge distillation methods transfer from a stronger teacher to a weaker student for compression or final-accuracy improvement. We instead investigate distillation to accelerate the training of strong students. We propose a generalizable plug-and-play recipe that freezes a weaker teacher, applies distillation only in early training, and turns it off once the student reaches and surpasses teacher-level performance. For ImageNet and CIFAR classification, this […]

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The challenge with AI in open source security has transitioned from an AI slop tsunami into more of a … plain security report tsunami. Less slop but lots of reports. Many of them really good. I’m spending hours per day on this now. It’s intense. — Daniel Stenberg, lead developer of cURL Tags: daniel-stenberg, security, curl, generative-ai, ai, llms, ai-security-research

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arXiv:2602.12407v1 Announce Type: new Abstract: Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot telemetry remains a major barrier. We introduce MiDAS, an open-source, platform-agnostic system enabling time-synchronized, non-invasive multimodal data acquisition across surgical robotic platforms. Methods: MiDAS integrates electromagnetic and RGB-D hand tracking, foot pedal sensing, and surgical video capturing without requiring proprietary robot interfaces. We validated MiDAS on the open-source Raven-II and the clinical da Vinci Xi by […]

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[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] This guide would not have been possible without the help and feedback from many people. Special thanks to Yasaman Bahri for her feedback, support, and mentoring. Thank you to Kumar Krishna Agrawal, Sam Schoenholz, and Jeffrey Pennington for their valuable input and guidance. Finally, thank you to our group members Chris Akers, Brian Friedenberg, Sajel Shah, Vincent Su, Witold Szejgis, for their curiosity, commitment […]

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Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, existing GPFs generally conduct prompting over node’s all feature dimensions which is obviously redundant and also be sensitive to node feature noise. To overcome this issue, for the first time, this paper proposes learning sparse […]

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