arXiv:2506.01722v3 Announce Type: replace-cross Abstract: We consider the problem setting of prediction with expert advice with possibly heavy-tailed losses, i.e. the only assumption on the losses is an upper bound on their second moments, denoted by $theta$. We develop adaptive algorithms that do not require any prior knowledge about the range or the second moment of the losses. Existing adaptive algorithms have what is typically considered a lower-order term in their regret guarantees. We show that this lower-order […]

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Every technologist, at some point, is tempted by the idea of creating a digital version of themselves. Not a simple chatbot that answers questions, but a true digital persona — an AI that doesn’t just know what you know, but thinks how you think. This project, which I call Damian AI, was my attempt to build just that: a conversational agent grounded in my public work and architected to mirror my own systematic, logic-driven cognitive model. The journey was a […]

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The increasing reliance on human preference feedback to judge AI-generated pseudo labels has created a pressing need for principled, budget-conscious data acquisition strategies. We address the crucial question of how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI. Our solution, grounded in semi-parametric inference, casts the budget allocation problem as a monotone missing data framework. Building on this formulation, we introduce Preference-Calibrated Active Learning (PCAL), a novel method that learns the […]

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Learning solution operators for differential equations with neural networks has shown great potential in scientific computing, but ensuring their stability under input perturbations remains a critical challenge. This paper presents a robust self-supervised neural operator framework that enhances stability through adversarial training while preserving accuracy. We formulate operator learning as a min-max optimization problem, where the model is trained against worst-case input perturbations to achieve consistent performance under both normal and adversarial conditions. We demonstrate that our method […]

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Large language models (LLMs) have significantly advanced artificial intelligence, yet their high com-putational, energy, and privacy costs pose substantial challenges. In contrast, Small Language Models(SLMs), typically with fewer than 15 billion parameters, have emerged as efficient alternatives. Thissurvey provides a comprehensive analysis of the SLM landscape, tracing their evolution and examiningarchitectural innovations that enhance efficiency. A novel multi-axis taxonomy is introduced to classifySLMs by genesis, architecture, and optimization goals, offering a structured framework for this field.Performance benchmarks are […]

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Five years ago, when I was thinking about the choice of my Master’s degree, I remember asking many people for advice. I was very grateful to my friends, as well as random people on LinkedIn and Reddit, who helped me make many important decisions. Fast forward to today. Having graduated from the university almost two years ago, I have pretty fresh and current experience of being a “young professional” in the tech industry. Now I am honoured to […]

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arXiv:2601.09896v1 Announce Type: new Abstract: Visual generative AI models are trained using a one-size-fits-all measure of aesthetic appeal. However, what is deemed “aesthetic” is inextricably linked to personal taste and cultural values, raising the question of whose taste is represented in visual generative AI models. In this work, we study an aesthetic evaluation model–LAION Aesthetic Predictor (LAP)–that is widely used to curate datasets to train visual generative image models, like Stable Diffusion, and evaluate the quality of AI-generated […]

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Preliminaries Performance learning Perceptron learning rule Supervised Hebbian learning LMS Form LMS to Backpropagation1 The LMS algorithm is a kind of ‘performance learning’. And we have studied several learning rules(algorithms) till now, such as ‘Perceptron learning rule’ and ‘Supervised Hebbian learning’. And they were based on the idea of the physical mechanism of biological neuron networks. Then performance learning was represented. Because of its outstanding performance, we go further and further away from natural intelligence into performance learning.

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We study a purely local inverse problem for non-reversible Randers metrics ( F = |cdot|_g + beta ) defined on smooth oriented surfaces. Using only the lengths of sufficiently small closed curves around a point ( p ), we prove that the exterior derivative ( dbeta(p) ) can be uniquely and stably recovered. Moreover, we establish that ( dbeta(p) ) is the only second-order local invariant retrievable from such local length measurements. Our approach is entirely metric-based, independent […]

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