Drawing on recent breakthroughs in cellular neurobiology and detailed biophysical modeling linking neocortical pyramidal neurons to distinct mental-state regimes, this work introduces a mathematically grounded formulation showing how models (e.g., Transformers) can implement computational principles underlying awake imaginative thought to pre-select relevant information before attention is applied via triadic modulation loops among queries ($Q$), keys ($K$), and values ($V$).~Scalability experiments on ImageNet-1K, benchmarked against a standard Vision Transformer (ViT), demonstrate significantly faster learning with reduced computational demand (fewer […]

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As Australia implemented a social media age restriction law requiring platforms like TikTok, Instagram, and YouTube to take “reasonable steps” to block Australians under 16 from creating or keeping accounts, the world waits and watches to see how it’ll play out. The ban aims to protect youth from harms like cyberbullying and mental health risks, though children can still view public content and use messaging apps, with platforms facing hefty fines for non-compliance. Is age-gating social media the […]

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arXiv:2604.08705v1 Announce Type: new Abstract: Adiabatic Quantum-Flux-Parametron (AQFP) logic is an ultra-low-power superconducting logic family with energy consumption approaching the Shannon limit, making it attractive for quantum computing control and cryogenic computing systems. Traditional AQFP designs face significant physical design challenges due to strict gate-level clocking requirements and limited interconnect lengths, leading to substantial buffer overhead and difficult timing closure. Recently, delay-line clocking of AQFP has been proposed to improve timing margins and reduce latency by enabling more […]

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Every enterprise revenue team has seen the demonstration at some point. The AI suggests a price, routes an approval, and flags a risk with apparent confidence, and the room is impressed by the fluency of it. What happens six months after go-live tends to be a different conversation, where the team is managing exceptions, investigating outputs that conflict with internal policy, and reconciling AI-generated recommendations against business rules the system had no reliable way to access in the […]

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arXiv:2601.13474v1 Announce Type: cross Abstract: The Muon optimizer, a matrix-structured algorithm that leverages spectral orthogonalization of gradients, is a milestone in the pretraining of large language models. However, the underlying mechanisms of Muon — particularly the role of gradient orthogonalization — remain poorly understood, with very few works providing end-to-end analyses that rigorously explain its advantages in concrete applications. We take a step by studying the effectiveness of a simplified variant of Muon through two case studies: matrix […]

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arXiv:2507.16749v3 Announce Type: replace-cross Abstract: Monitoring for changes in a predictive relationship represented by a fitted supervised learning model (i.e., concept drift detection) is a widespread problem in modern data-driven applications. A general and powerful Fisher score-based concept drift approach was recently proposed, in which detecting concept drift reduces to detecting changes in the mean of the model’s score vector using a multivariate exponentially weighted moving average (MEWMA). To implement the approach, the initial data must be split […]

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