arXiv:2606.06785v1 Announce Type: new Abstract: We study finite-sample change detection for one-dimensional noisy dynamical systems using partition-based empirical approximations of stationary behaviour. Given observations from an interval-valued process, we partition the state space, estimate a finite transition matrix from observed transitions between partition elements, and apply a small Doeblin-type regularisation to ensure a unique stationary distribution. From an initial reference segment, we compute a baseline empirical stationary distribution (widehat{pi}_{0,rho}). For each later sliding window, we compute (widehat{pi}_{t,rho}) and […]

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arXiv:2602.20358v1 Announce Type: new Abstract: The study of stable matchings usually relies on the assumption that agents’ preferences over the opposite side are complete and known. In many real markets, however, preferences might be uncertain and revealed only through costly interactions such as interviews. We show how to reach interim-stable matchings, under which all matched pairs must have interviewed and agents use expected utilities whenever true values remain unknown, while minimizing both the expected number of interviews and […]

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arXiv:2602.18528v1 Announce Type: new Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy. While previous works have tackled this problem, we find that SOTA methods suffer from catastrophic forgetting, where the model’s performance drops well below the source model due to continual parameter updates at test-time. In this work, […]

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arXiv:2602.15965v1 Announce Type: new Abstract: The upcoming IEEE-P3109 standard for low-precision floating-point arithmetic can become the foundation of future machine learning hardware and software. Unlike the fixed types of IEEE-754, P3109 introduces a parametric framework defined by bitwidth, precision, signedness, and domain. This flexibility results in a vast combinatorial space of formats — some with as little as one bit of precision — alongside novel features such as stochastic rounding and saturation arithmetic. These deviations create a unique […]

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Table of Links Abstract and 1. Introduction Related Work and Background Analysis 3.1 Limitations about Existing ReLUficatio 3.2 dReLU Are Neurons in Expert still Sparsely Activated? dReLU Sparsification Experiments Results 6.1 Downstream Tasks Performance 6.2 Sparsity of Sparsified Models Practical Inference Speedup Evaluation 7.1 Experiments Setting 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.4 Deploy LLMs on mobile phones Conclusion and References A. Appendix / supplemental material B. Limitation C. Broader Impact Abstract Exploiting activation sparsity […]

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In preparation for a lightning talk I’m giving at PyCon US this afternoon I decided to figure out how many names OpenClaw has actually had since that first commit back in November. Thanks to this first_line_history.py tool (code here) the answer, according to the Git history of the OpenClaw README, is: Warelay → CLAWDIS → CLAWDBOT → Clawdbot → Moltbot →🦞 OpenClaw Or in detail (the output from the tool): 2025-11-24T11:23:15+01:00 16dfc1a # Warelay — WhatsApp Relay CLI […]

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arXiv:2603.00668v1 Announce Type: new Abstract: Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real […]

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