arXiv:2505.18526v2 Announce Type: replace Abstract: Learning expressive kernels while retaining tractable inference remains a central challenge in scaling Gaussian processes (GPs) to large and complex datasets. We propose a scalable GP regressor based on deep basis kernels (DBKs). Our DBK is constructed from a small set of neural-network-parameterized basis functions with an explicit low-rank structure. This formulation immediately enables linear-complexity inference with respect to the number of samples, possibly without inducing points. DBKs provide a unifying perspective that […]

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Relevance of this work is determined by the fact that despite the widespread use of keyword sets as the most common approach for collecting thematic information, there are few publications dedicated to the study of frequent term sets in bibliometric research. Usually, pairs of terms co-occurrence are used to construct the network, as in VOSviewer. Research objective. 1. Testing the impact of adjusting the construction of the IEEE term co-occurrence graph by increasing the significance of “strong links,” […]

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arXiv:2602.06098v1 Announce Type: new Abstract: Coding agents are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored. We provide a probabilistic framework for two dominant paradigms: code selection after generation using the execution environment, and code generation conditioned on environment feedback. First, we formalize several well-established selection heuristics as environment-aware estimators of code correctness. We theoretically prove that estimators based on fuzzy functional similarity add an inductive bias and strictly dominate […]

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Within many crypto communities, the words democracy and decentralization are often used interchangeably. They appear to represent well-meaning concepts based on “the power of the people,” and some projects attempt to demonstrate fairness with them. When we hear them together, it becomes tempting to treat them as twins. We can say they do share a house and borrow each other’s clothes, but they’re not the same thing. It’s important to understand that each word emphasizes a different aspect […]

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arXiv:2601.16449v1 Announce Type: new Abstract: Understanding human emotions from multimodal signals poses a significant challenge in affective computing and human-robot interaction. While multimodal large language models (MLLMs) have excelled in general vision-language tasks, their capabilities in emotional reasoning remain limited. The field currently suffers from a scarcity of large-scale datasets with high-quality, descriptive emotion annotations and lacks standardized benchmarks for evaluation. Our preliminary framework, Emotion-LLaMA, pioneered instruction-tuned multimodal learning for emotion reasoning but was restricted by explicit face […]

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Subject-driven text-to-image generation presents a significant challenge: faithfully reproducing a specific subject’s identity within novel, text-described scenes. Existing solutions typically involve computationally expensive model fine-tuning or less performant training-free methods. This paper introduces Content-Adaptive Grafting (CAG), a novel, efficient, and entirely training-free framework designed to achieve high subject fidelity and strong text alignment. CAG operates without modifying the underlying generative model’s weights, instead leveraging intelligent noise initialization and adaptive feature fusion during inference. Our framework comprises Initial Structure […]

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arXiv:2602.04031v1 Announce Type: new Abstract: Tabular Language Models (TLMs) have been claimed to achieve emergent generalization for tabular prediction. We conduct a systematic re-evaluation of Tabula-8B as a representative TLM, utilizing 165 datasets from the UniPredict benchmark. Our investigation reveals three findings. First, binary and categorical classification achieve near-zero median lift over majority-class baselines and strong aggregate performance is driven entirely by quartile classification tasks. Second, top-performing datasets exhibit pervasive contamination, including complete train-test overlap and task-level leakage […]

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arXiv:2601.10828v1 Announce Type: new Abstract: High-order Lie derivatives are essential in nonlinear systems analysis. If done symbolically, their evaluation becomes increasingly expensive as the order increases. We present a compact and efficient numerical approach for computing Lie derivatives of scalar, vector, and covector fields using the MATLAB ADTAYL package. The method exploits a fact noted by R”obenack: that these derivatives coincide, up to factorial scaling, with the Taylor coefficients of expressions built from a Taylor expansion about a […]

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