Can traditional equity markets and cryptocurrency exchanges coexist on the same platform? BitMEX, the derivatives exchange that has operated since 2014 without losing customer funds to hacks, is testing this proposition with a month-long campaign offering 70,000 USDT in prizes to traders who engage with equity perpetual contracts. The campaign, running from February 12 to March 12, 2026, targets users who trade contracts based on Apple (AAPL), Tesla (TSLA), and other publicly traded companies, marking a continuation of […]

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arXiv:2601.18849v1 Announce Type: new Abstract: Dynamic Neural Radiance Fields (NeRF) have demonstrated considerable success in generating high-fidelity 3D models of talking portraits. Despite significant advancements in the rendering speed and generation quality, challenges persist in accurately and efficiently capturing mouth movements in talking portraits. To tackle this challenge, we propose an automatic method based on blink embedding and hash grid landmarks encoding in this study, which can substantially enhance the fidelity of talking faces. Specifically, we leverage facial […]

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arXiv:2601.02609v1 Announce Type: cross Abstract: Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB–14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states–exceeding even A100-40GB capacity. We present Chronicals, an open-source training framework achieving 3.51x speedup over Unsloth through four synergistic optimizations: (1) fused Triton kernels eliminating 75% of memory traffic via RMSNorm (7x), SwiGLU (5x), and QK-RoPE (2.3x) fusion; (2) Cut Cross-Entropy reducing logit memory from 5GB to 135MB through […]

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arXiv:2602.05790v1 Announce Type: cross Abstract: Fast computation of a matrix product $W^top X$ is a workhorse of modern LLMs. To make their deployment more efficient, a popular approach is that of using a low-precision approximation $widehat W$ in place of true $W$ (“weight-only quantization”). Information theory demonstrates that an optimal algorithm for reducing precision of $W$ depends on the (second order) statistics of $X$ and requires a careful alignment of vector quantization codebook with PCA directions of $X$ […]

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arXiv:2603.05575v1 Announce Type: new Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a black-box machine-learning predictor is available. The goal is to perform statistical inference on conditional functionals evaluated at a fixed test point, such as conditional means, without imposing a parametric model for the conditional relationship. Our approach combines localization with prediction-based variance reduction. First, we introduce a reproducing kernel-based localization method that learns a data-adaptive […]

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Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems has thus far been limited by existing task suites. They suffer from many issues, such as: poor evaluation methodologies; data contamination; and containing saturated or very similar problems. Here, we introduce DiscoGen, a procedural generator of algorithm discovery tasks for machine learning, such as developing optimisers for reinforcement learning or loss functions for image […]

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Meta’s Scam Ads Are Finally Being Challenged — And It’s Long Overdue After years of warnings from consumer advocates, regulators and defrauded users, Meta Platforms is finally being dragged into court over what critics say has been an open-secret business model: knowingly allowing scam advertisements to run across Facebook and Instagram in the name of profit. The U.S. Virgin Islands has filed a lawsuit against the social media giant, alleging Meta deliberately profited from scam advertising while publicly […]

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arXiv:2602.03951v1 Announce Type: new Abstract: Robust generalization under distribution shift remains difficult to monitor and optimize in the absence of target-domain labels, as models with similar in-distribution accuracy can exhibit markedly different out-of-distribution (OOD) performance. While prior work has focused on training-time regularization and low-order representation statistics, little is known about whether the geometric structure of learned embeddings provides reliable post-hoc signals of robustness. We propose a geometry-based diagnostic framework that constructs class-conditional mutual k-nearest-neighbor graphs from in-distribution […]

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