arXiv:2601.16294v1 Announce Type: new Abstract: General Matrix Multiplication (GEMM) is the cornerstone of Deep Learning and HPC workloads; accordingly, academia and industry have heavily optimized this kernel. Modern platforms with matrix multiplication accelerators exhibit high FLOP/Byte machine balance, which makes implementing optimal matrix multiplication challenging. On modern CPU platforms with matrix engines, state-of-the-art vendor libraries tune input tensor layouts, parallelization schemes, and cache blocking to minimize data movement across the memory hierarchy and maximize throughput. However, the best […]

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arXiv:2601.01362v1 Announce Type: cross Abstract: Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite increasing advances in foundation model research, the relationship between such large language models (LLMs) and their calibration remains an open area of research. In this work, we look at a critical gap in the calibration of LLMs within multilingual settings, in an attempt to better understand how the data scarcity […]

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Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture the fine spatio-temporal scales which govern the evolution of these systems. However, these simulations are often high-fidelity in nature, and can be computationally very expensive. Hence, generating data from these expensive simulations becomes a bottleneck in an end-to-end engineering design process. Spatio-temporal surrogate modeling of these dynamical systems has been a popular data-driven solution to tackle this computational bottleneck. This is […]

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arXiv:2603.20300v1 Announce Type: new Abstract: Software systems have traditionally been designed for human interaction, emphasizing graphical user interfaces, usability, and cognitive alignment with end users. However, recent advances in large language model (LLM)-based agents are changing the primary consumers of software systems. Increasingly, software is no longer only used by humans, but also invoked autonomously by AI agents through structured interfaces. In this paper, we argue that software engineering is undergoing a paradigm shift from human-oriented interfaces to […]

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arXiv:2603.15818v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are subtle affective states where a person shows conflicting signals through different channels — saying one thing while their face or voice tells another story. Recognising these states automatically is valuable in clinical settings, but it is hard for machines because the key evidence lives in the emph{disagreements} between what is said, how it sounds, and what the face shows. We present textbf{ConflictAwareAH}, a multimodal framework built for this […]

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arXiv:2602.09028v1 Announce Type: new Abstract: This paper reformulates Fermat’s Last Theorem as an embedding problem of information-geometric structures. We reinterpret the Fermat equation as an $n$-th moment constraint, constructing a statistical manifold $mathcal{M}_n$ of generalized normal distributions via the Maximum Entropy Principle. By Chentsov’s Theorem, the natural metric is the Fisher information metric ($L^2$); however, the global structure is governed by the $L^n$ moment constraint. This reveals a discrepancy between the local quadratic metric and the global $L^n$ […]

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Written to clarify ideas that bewildered even his closest friends, Teilhard de Chardin explains his lifelong struggle: reconciling love of God with love of the world. From childhood, he sought the Absolute—first in metal, then matter, finally the cosmos itself. His revelation: Christ doesn’t compete with the universe but completes it, making every human endeavor a form of worship toward one divine convergence.

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Label distribution learning (LDL) requires the learner to predict the degree of correlation between each sample and each label. To achieve this, a crucial task during learning is to leverage the correlation among labels. Deep Forest (DF) is a deep learning framework based on tree ensembles, whose training phase does not rely on backpropagation. DF performs in-model feature transform using the prediction of each layer and achieves competitive performance on many tasks. However, its exploration in the field […]

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