arXiv:2602.24230v1 Announce Type: new Abstract: Calibration$unicode{x2014}$the problem of ensuring that predicted probabilities align with observed class frequencies$unicode{x2014}$is a basic desideratum for reliable prediction with machine learning systems. Calibration error is traditionally assessed via a divergence function, using the expected divergence between predictions and empirical frequencies. Accurately estimating this quantity is challenging, especially in the multiclass setting. Here, we show how to extend a recent variational framework for estimating calibration errors beyond divergences induced induced by proper losses, to […]

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As blockchain technology evolves from specialized financial tools to foundational infrastructure for Web3, the necessity for rigorous performance validation becomes paramount. Stress testing—defined as the evaluation of system stability under extreme workloads—is critical for identifying bottlenecks in consensus mechanisms and peer-to-peer communication. This survey provides an exhaustive analysis of web-based stress testing frameworks. Unlike traditional CLI-based tools, web-based frameworks provide real-time telemetry and distributed orchestration capabilities essential for modern decentralized applications. We categorize existing literature into three generations […]

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Expressive policies based on flow-matching have been successfully applied in reinforcement learning (RL) more recently due to their ability to model complex action distributions from offline data. These algorithms build on standard policy gradients, which assume that there is no unmeasured confounding in the data. However, this condition does not necessarily hold for pixel-based demonstrations when a mismatch exists between the demonstrator’s and the learner’s sensory capabilities, leading to implicit confounding biases in offline data. We address the […]

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arXiv:2601.05399v1 Announce Type: new Abstract: CLIP and BiomedCLIP are examples of vision-language foundation models and offer strong cross-modal embeddings; however, they are not optimized for fine-grained medical retrieval tasks, such as retrieving clinically relevant radiology reports using chest X-ray (CXR) image queries. To address this shortcoming, we propose a multi-task learning framework to fine-tune BiomedCLIP and evaluate improvements to CXR image-text retrieval. Using BiomedCLIP as the backbone, we incorporate a lightweight MLP projector head trained with a multi-task […]

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arXiv:2601.10808v1 Announce Type: new Abstract: ABS+ polar codes are a generalization of Arikan polar codes that provides much faster polarization. We present an LLR-domain implementation of the SCL decoder of ABS+ polar codes. Furthermore, we optimize the SCL algorithm in order to reduce the complexity requirements for the LLRs computation. In comparison with classical polar codes, the proposed approach requires less number of arithmetic operations in the SCL decoder to obtain the fixed frame error rate (FER) at […]

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arXiv:2602.20231v1 Announce Type: new Abstract: Latent action representations learned from unlabeled videos have recently emerged as a promising paradigm for pretraining vision-language-action (VLA) models without explicit robot action supervision. However, latent actions derived solely from RGB observations primarily encode appearance-driven dynamics and lack explicit 3D geometric structure, which is essential for precise and contact-rich manipulation. To address this limitation, we introduce UniLACT, a transformer-based VLA model that incorporates geometric structure through depth-aware latent pretraining, enabling downstream policies to […]

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Since the first edition entitled “0 and 1 — From Elemental Math to Quantum AI” and released in early 2025, a lot of progress has been made. Fascinating new results have been uncovered and proved by the author, many still leading to interesting quantum dynamics. In 100 pages, the new material presented here goes far beyond any articles and books published so far on the topic. This second edition offers a trip deep into the most elusive and […]

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