Miami, Florida, April 7th, 2026, Chainwire/–MetaWin confirms more than $13 million in player rewards across Cashdrops, competitions, races and exclusive member benefits Online casino MetaWin has announced that it will return more than $13 million to players through its ongoing loyalty rewards program, as a show of appreciation for the loyalty and support of the community that has helped build the platform over time. The program includes direct Cashdrops, weekly competitions, monthly races and NFT holder-only benefits, and […]

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A neat blog post by Mayank Pratap Singh with excellent visuals introducing ViTs from the ground up. The post covers: Patch embedding Positional encodings for Vision Transformers Encoder-only models ViTs for classification Benefits, drawbacks, & real-world applications for ViTs Fine-tuning a ViT for image classification. Full blogpost here: https://www.vizuaranewsletter.com/p/vision-transformers Additional Resources: An Image is Worth 16×16 Words https://arxiv.org/abs/2010.11929 Yannic Kilcher Discussion of the paper https://www.youtube.com/watch?v=TrdevFK_am4 Generating Long Sequences with Sparse Transformers https://arxiv.org/abs/1904.10509 Generative Pretraining from Pixels https://proceedings.mlr.press/v119/chen20s.html I’ve […]

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[Guest post by Salil Vadhan; Boaz’s note: I am thrilled to see this award. Luca’s expository writing on his blog, surveys, and lecture notes was and is an amazing resource. Luca had a knack for explaining some of the most difficult topics in intuitive ways that cut to their heart. I hope it will inspire more people to follow in footsteps.] The Trevisan Award for Expository Work is a new SIGACT award  created in memory of Luca Trevisan (1971-2024), with […]

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arXiv:2602.10314v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) have emerged as a promising approach for generative modeling in discrete spaces. By generating sequences in any order and allowing for parallel decoding, they enable fast inference and strong performance on non-causal tasks. However, this flexibility comes with a training complexity trade-off: MDMs train on an exponentially large set of masking patterns, which is not only computationally expensive, but also creates a train–test mismatch between the random masks used […]

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arXiv:2601.16461v1 Announce Type: new Abstract: We study lossy source coding under a distortion measure defined by the negative log-likelihood induced by a prescribed conditional distribution $P_{X|U}$. This emph{log-likelihood distortion} models compression settings in which the reconstruction is a semantic representation from which the source can be probabilistically generated, rather than a pointwise approximation. We formulate the corresponding rate-distortion problem and characterize fundamental properties of the resulting rate-distortion function, including its connections to lossy compression under log-loss, classical rate-distortion […]

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Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline–from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows–building on discussions from the “SusML workshop: Towards sustainable exploration of chemical spaces with machine learning” held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological […]

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arXiv:2601.22338v1 Announce Type: new Abstract: LLMs are popular among clinicians for decision-support because of simple text-based interaction. However, their impact on clinicians’ performance is ambiguous. Not knowing how clinicians use this new technology and how they compare it to traditional clinical decision-support systems (CDSS) restricts designing novel mechanisms that overcome existing tool limitations and enhance performance and experience. This qualitative study examines how clinicians (n=12) perceive different interaction modalities (text-based conversation with LLMs, interactive and static UI, and […]

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arXiv:2601.05427v1 Announce Type: new Abstract: In many multi-agent systems, agents interact repeatedly and are expected to settle into equilibrium behavior over time. Yet in practice, behavior often drifts, and detecting such deviations in real time remains an open challenge. We introduce a sequential testing framework that monitors whether observed play in repeated games is consistent with equilibrium, without assuming a fixed sample size. Our approach builds on the e-value framework for safe anytime-valid inference: by “betting” against equilibrium, […]

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arXiv:2603.02376v1 Announce Type: new Abstract: Custom CUDA kernel development is essential for maximizing GPU utilization in large-scale distributed LLM training and inference, yet manually writing kernels that jointly leverage both computation and communication remains a labor-intensive and error-prone process. Prior work on kernel optimization has focused almost exclusively on computation, leaving communication kernels largely untouched even though they constitute a significant share of total execution time. We introduce CUCo, a training-free agent-driven workflow that automatically generates high-performance CUDA […]

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The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge required to annotate graph data. In this context, Few-Shot Graph Learning (FSGL) approaches have been developed over the years. Through sophisticated neural architectures and customized training pipelines, these approaches enhance model adaptability to new label distributions. However, compromises in textcolor{black}{the model’s} robustness and interpretability can result […]

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