technocracy

Key Takeaways Laravel Prompts provides a beautiful, user-friendly interface for command-line applications with zero dependencies The package offers multiple input types including text, password, select, multiselect, confirm, search, and progress bars Laravel 12 includes Prompts natively, making CLI interactions more intuitive and visually appealing Prompts automatically handles validation, error messages, and keyboard navigation Perfect for creating installation wizards, configuration tools, and interactive artisan commands Index Introduction to Laravel Prompts Understanding Laravel Prompts Components Statistics Available Prompt Types Practical […]

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I have been recently using focal loss for heavily imbalanced image and text classification tasks and have been seeing a very large boost in a production environment. For those that don’t know how focal loss works: focal loss reduces the importance of “easy” examples so that the model can focus its learning on “hard” examples. Now i have been thinking that LLM models based on the transformer architecture are essentially an overglorified classifier during training (forced prediction of […]

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There’s a moment in every Compose developer’s journey – a quiet realization that arrives somewhere between your third nested Box and your fifth Modifier.background(). Or maybe you just bumped into excellent article about Glitch Effect from here and decided to extend your knowledge. In these moments, you don’t need another layout node; you need the Canvas. In this series, we aren’t just looking at how to draw shapes. We are going under the hood of the Compose Drawing Pipeline to understand […]

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arXiv:2601.17091v1 Announce Type: new Abstract: ROCKET (RandOm Convolutional KErnel Transform) is a feature extraction algorithm created for Time Series Classification (TSC), published in 2019. It applies convolution with randomly generated kernels on a time series, producing features that can be used to train a linear classifier or regressor like Ridge. At the time of publication, ROCKET was on par with the best state-of-the-art algorithms for TSC in terms of accuracy while being significantly less computationally expensive, making ROCKET […]

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Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains. A key challenge lies in the lack of target-domain environment interaction and reward supervision, which prevents direct policy learning. To address this challenge, we propose Bridging Dynamics Gaps for Cross-Domain Reinforcement Learning (BDGxRL), a novel framework that leverages Diffusion Schrödinger Bridge (DSB) to align source transitions with target-domain dynamics encoded in offline demonstrations. Moreover, we introduce a reward modulation mechanism that […]

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The aim of the article is to introduce a few variants of generalized quasi-continuity of multifunctions defined on a bitopological space and to study their mutual relationship. The results known for functions are extended to multifunctions which provide a wider range of relationships, mainly in terms of upper and lower semi continuities and corresponding continuities with respect to a dual bitopology. The proof procedures are based on a notion of pseudo refinement of two topologies and the Baire […]

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arXiv:2601.10754v1 Announce Type: new Abstract: AI chatbots designed as emotional companions blur the boundaries between interpersonal intimacy and institutional software, creating a complex, multi-dimensional privacy environment. Drawing on Communication Privacy Management theory and Masur’s horizontal (user-AI) and vertical (user-platform) privacy framework, we conducted in-depth interviews with fifteen users of companion AI platforms such as Replika and Character.AI. Our findings reveal that users blend interpersonal habits with institutional awareness: while the non-judgmental, always-available nature of chatbots fosters emotional safety […]

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arXiv:2602.19239v1 Announce Type: new Abstract: Large language models can follow complex procedures yet fail at a seemingly trivial final step: reporting a value they themselves computed moments earlier. We study this phenomenon as emph{procedural hallucination}: failure to execute a verifiable, prompt-grounded specification even when the correct value is present in context. In long-context binding tasks with a known single-token candidate set, we find that many errors are readout-stage routing failures. Specifically, failures decompose into Stage~2A (gating) errors, where […]

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arXiv:2602.00986v1 Announce Type: new Abstract: In this paper, we identify a sparse reward subsystem within the hidden states of Large Language Models (LLMs), drawing an analogy to the biological reward subsystem in the human brain. We demonstrate that this subsystem contains value neurons that represent the model’s internal expectation of state value, and through intervention experiments, we establish the importance of these neurons for reasoning. Our experiments reveal that these value neurons are robust across diverse datasets, model […]

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