arXiv:2602.13413v1 Announce Type: cross Abstract: We develop a worst-case complexity theory for stochastically preconditioned stochastic gradient descent (SPSGD) and its accelerated variants under heavy-tailed noise, a setting that encompasses widely used adaptive methods such as Adam, RMSProp, and Shampoo. We assume the stochastic gradient noise has a finite $p$-th moment for some $p in (1,2]$, and measure convergence after $T$ iterations. While clipping and normalization are parallel tools for stabilizing training of SGD under heavy-tailed noise, there is […]

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arXiv:2601.09770v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) and reinforcement learning (RL) have driven progress in GUI automation. However, most existing methods rely on static, one-shot visual inputs and passive perception, lacking the ability to adaptively determine when, whether, and how to observe the interface. We present GUI-Eyes, a reinforcement learning framework for active visual perception in GUI tasks. To acquire more informative observations, the agent learns to make strategic decisions on both whether and […]

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arXiv:2412.11308v2 Announce Type: replace Abstract: Predictive models often degrade in performance due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detection methods often rely on metrics such as accuracy or marginal variable distributions, which may fail to capture subtle but important conceptual changes. This paper proposes a novel method, Profile […]

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:::info Authors: Chengxuan Ying, yingchengsyuan@gmail.com (Dalian University of Technology) Tianle Cai, tianle.cai@princeton.edu (Princeton University) Shengjie Luo, luosj@stu.pku.edu.cn (Peking University) Shuxin Zheng, shuz@microsoft.com (Microsoft Research Asia) Guolin Ke, guoke@microsoft.com (Microsoft Research Asia) Di He, dihe@microsoft.com (Microsoft Research Asia) Yanming Shen, shen@dlut.edu.cn (Dalian University of Technology) Tie-Yan Liu, tyliu@microsoft.com (Microsoft Research Asia) ::: Abstract The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance […]

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Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane — from order timestamps and routing records to shipping manifests — yet predicting delivery delays remains predominantly reactive. Existing predictive approaches typically treat this problem either as a tabular classification task, ignoring network topology, or as a time-series anomaly detection task, overlooking the spatial dependencies of the supply chain graph. To bridge this gap, we propose a hybrid deep learning framework for proactive […]

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arXiv:2603.23659v1 Announce Type: new Abstract: When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B–72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns — e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian […]

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I just sent the January edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In the newsletter for January: LLM predictions for 2026 Coding agents get even more attention Clawdbot/Moltbot/OpenClaw went very viral Kakapo breeding season is off to a really strong start New options for sandboxes Web browsers are the “hello world” of coding agent swarms Sam Altman addressed the Jevons paradox for […]

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How Tokenization Is Turning Gold Into a Functional Asset Amid a Fragmenting Financial System “Gold is money. Everything else is credit.” J.P. Morgan said that in testimony before Congress in 1912. For most of the century that followed, the statement felt like a relic. Today it reads less like history and more like a description of how the system is quietly reconfiguring itself. Over the past three years, central banks have accumulated gold at the fastest pace in […]

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How ShareChat engineers rebuilt a low-latency ML feature store on ScyllaDB after an initial scalability failure — and what they learned along the way The demand for low-latency machine learning feature stores is higher than ever, but actually implementing one at scale remains a challenge. That became clear when ShareChat engineers Ivan Burmistrov and Andrei Manakov took the P99 CONF 23 stage to share how they built a low-latency ML feature store based on ScyllaDB. This isn’t a […]

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arXiv:2603.12304v1 Announce Type: new Abstract: This paper introduces a novel optimization framework that fundamentally integrates the Minimum Description Length (MDL) principle into the training dynamics of deep neural networks. Moving beyond its conventional role as a model selection criterion, we reformulate MDL as an active, adaptive driving force within the optimization process itself. The core of our method is a geometrically-grounded cognitive manifold whose evolution is governed by a textit{coupled Ricci flow}, enriched with a novel textit{MDL Drive} […]

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