arXiv:2601.01699v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) is a flexible framework that combines multiple specialized submodels (“experts”), by assigning covariate-dependent weights (“gating functions”) to each expert, and have been commonly used for analyzing heterogeneous data. Existing statistical MoE formulations typically assume constant coefficients, for covariate effects within the expert or gating models, which can be inadequate for longitudinal, spatial, or other dynamic settings where covariate influences and latent subpopulation structure evolve across a known dimension. We propose a […]

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Preliminaries Basic machine learning concepts Probability Theory concepts expectation correlated random variable Analysis of Committees1 The committee is a native inspiration for how to combine several models(or we can say how to combine the outputs of several models). For example, we can combine all the models by: [ y_{COM}(X)=frac{1}{M}sum_{m=1}^My_m(X)tag{1} ] Then we want to find out whether this average prediction of models is better than every one of them.

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Unveiling what it describes as the most capable model series yet for professional knowledge work, OpenAI launched GPT-5.2 today. The model was trained and deployed on NVIDIA infrastructure, including NVIDIA Hopper and GB200 NVL72 systems. It’s the latest example of how leading AI builders train and deploy at scale on NVIDIA’s full-stack AI infrastructure. Pretraining: The Bedrock of Intelligence AI models are getting more capable thanks to three scaling laws: pretraining, post-training and test-time scaling. Reasoning models, which […]

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arXiv:2512.07541v2 Announce Type: replace Abstract: Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across low to high dimensions. This versatile approach is applicable to Euclidean and graph-structured data with unknown distributions, while maintaining control over error probabilities. Theoretically, we demonstrate that the algorithm achieves high detection power when the magnitude of the change surpasses the lower bound of the minimax separation rate, which scales on the […]

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The continued scaling of large-scale AI and HPC systems increasingly encounters limits that are not imposed by raw compute capacity, but by the dynamics of interconnects that bind distributed components into a single execution fabric. As system size, heterogeneity, and synchronization demands grow, performance degradation manifests in non-linear and often opaque ways, leading to a collapse of effective cost per performance despite sustained investment in additional hardware. Classical performance and network metrics, while necessary, fail to capture the […]

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Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how […]

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arXiv:2601.02457v1 Announce Type: new Abstract: Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and language foundation models to directly solve dense tasks through multi-view renderings and text queries. While promising, these pipelines require expensive inference over multiple renderings, depend heavily on large language-model (LLM) prompt engineering for captions, and fail to exploit the inherent 3D geometry of shapes. We address this gap by […]

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