arXiv:2602.21160v2 Announce Type: replace Abstract: In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a single scalar, mutual information (MI), that cannot distinguish whether a model’s ignorance involves a benign or safety-critical class. We decompose MI into a per-class vector $C_k(x)=sigma_k^{2}/(2mu_k)$, with $mu_k{=}mathbb{E}[p_k]$ and $sigma_k^2{=}mathrm{Var}[p_k]$ across posterior samples. The decomposition follows from a second-order Taylor expansion of the entropy; the $1/mu_k$ weighting corrects boundary suppression and makes $C_k$ comparable […]

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arXiv:2603.26741v1 Announce Type: new Abstract: We study language-conditioned visual navigation (LCVN), in which an embodied agent is asked to follow a natural language instruction based only on an initial egocentric observation. Without access to goal images, the agent must rely on language to shape its perception and continuous control, making the grounding problem particularly challenging. We formulate this problem as open-loop trajectory prediction conditioned on linguistic instructions and introduce the LCVN Dataset, a benchmark of 39,016 trajectories and […]

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arXiv:2603.26756v1 Announce Type: new Abstract: Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResNet addressed this through residual connections, but these fixed short-circuits cannot adapt to varying input complexity or selectively emphasize task relevant features across network hierarchies. This study introduces GradAttn, a hybrid CNN-transformer framework that replaces fixed residual connections with attention-controlled gradient flow. By extracting multi-scale CNN features at different depths and regulating them through self-attention, […]

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Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes. A necessary prerequisite is to identify subgroups of patients who respond differently to different therapies. Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials. Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method. The resulting time-specific responses to treatment are characterised using […]

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Machine Learning (ML) is fundamentally reshaping education, offering tools to personalize instruction, automate assessment, and predict student outcomes. This paper provides a comprehensive overview of ML’s role in education, tracing its evolution from early computer-assisted instruction to today’s generative artificial intelligence (AI). We explore key applications, including intelligent tutoring systems, early warning systems for at-risk students, and automated essay scoring, highlighting their potential to address the long-standing challenge of individualized learning at scale. However, this technological integration is […]

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arXiv:2603.10010v1 Announce Type: new Abstract: We introduce a multi-faceted automated red teaming framework in which the goal is to generate multi-modal adversarial conversations that would break a target model and introduce various expansions that would result in more effective and efficient adversarial conversations. The introduced expansions include: 1. Horizontal expansion in which the goal is for the red team model to self-improve and generate more effective conversation starters that would shape a conversation. 2. Vertical expansion in which […]

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PEPE turned people who did things quickly to be millionaires. That’s not hyperbole. The returns that the early investors got transformed lives. PEPE is currently trading at a price of $0.000006619 and has a market capitalization of $2.78B, and such geometric returns are more difficult to recreate mathematically. Pepeto ($PEPETO) offers a similar opportunity, currently available at $0.000000176 during presale. The difference? You are reading it before the people find out about it. This guide walks through exactly […]

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arXiv:2602.04972v1 Announce Type: new Abstract: The adoption of generative AI in education has accelerated dramatically in recent years, with Large Language Models (LLMs) increasingly integrated into learning environments in the hope of providing personalized support that enhances learner engagement and knowledge retention. However, truly personalized support requires access to meaningful Learning Context (LC) regarding who the learner is, what they are trying to understand, and how they are engaging with the material. In this paper, we present a […]

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