arXiv:2604.01303v1 Announce Type: new Abstract: We present a framework for compositional program verification based on polynomial functors in dependent type theory. In this framework, polynomial functors serve as program interfaces, Kleisli morphisms for the free monad monad serve as implementations, and dependent polynomials encode pre/postcondition specifications. We show that implementations and their verifications compose via wiring diagrams, and that Mealy machines provide a compositional coalgebraic operational semantics. We identify the abstract categorical structure underlying this compositionality as a […]

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arXiv:2601.16446v1 Announce Type: new Abstract: Deep learning models are effective for sequential data modeling, yet commonly used activation functions such as ReLU, LeakyReLU, and PReLU often exhibit gradient instability when applied to noisy, non-stationary financial time series. This study introduces BrownianReLU, a stochastic activation function induced by Brownian motion that enhances gradient propagation and learning stability in Long Short-Term Memory (LSTM) networks. Using Monte Carlo simulation, BrownianReLU provides a smooth, adaptive response for negative inputs, mitigating the dying […]

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arXiv:2603.06726v1 Announce Type: new Abstract: Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series foundation models (TSFMs) effectively capture temporal dependencies, they typically underutilize cross-variate correlations and non-periodic patterns that are essential for price forecasting. Conversely, regression models excel at capturing feature interactions but are limited to future-available inputs, ignoring crucial historical drivers that are unavailable at forecast time. To bridge this gap, we propose FutureBoosting, a novel […]

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arXiv:2602.20651v1 Announce Type: cross Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presenting both challenges and opportunities for functional data analysis. However, existing methods face a critical trade-off: conventional functional models are limited by linearity, whereas deep learning approaches lack interpretable region selection for sparse effects. To bridge these gaps, we propose a sparse Bayesian functional deep neural network (sBayFDNN). It learns adaptive functional […]

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The Generative Pre-trained Transformer (GPT) series has changed artificial intelligence from a research curiosity to a mainstream tool, empowering innumerable interactions daily. The realization about the evolution of GPT versions over the years reveals how each one launched breakthroughs in language understanding and practical applications across industries. GPT Version Comparison Table Features Release Date Parameters Training Data Text Quality Capabilities Accessibility Architecture Improvements Safety & Alignment Use Cases Integration Languages Supported Pricing Plan GPT-1 June 2018 117M Books […]

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About a month ago, Elon Musk boldly stated that “work will be optional” while discussing the future impact of AI on the workforce. While I assumed this was to evoke emotion in the general public, I felt stirred to explore this further. As adults, many of us are focused on just fulfilling basic needs of food, shelter, and safety. Psychologist Abraham Maslow’s hierarchy of needs states that we reach our full potential when we go beyond basic needs […]

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A block editor Is a document editor The textbook Compose text-input pattern is clean on a whiteboard: keep the document in immutable state, feed the current value into a remembered TextFieldState, and push edits back through callbacks. For ordinary input, that model is fine. It breaks the moment Enter stops meaning insert a newline and starts meaning split this block, preserve formatting, and keep the document valid for serialization. The document is an ordered list of structural units — paragraphs, headings, quotes, todos, list […]

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arXiv:2604.01404v1 Announce Type: new Abstract: Language models can answer many entity-centric factual questions, but it remains unclear which internal mechanisms are involved in this process. We study this question across multiple language models. We localize entity-selective MLP neurons using templated prompts about each entity, and then validate them with causal interventions on PopQA-based QA examples. On a curated set of 200 entities drawn from PopQA, localized neurons concentrate in early layers. Negative ablation produces entity-specific amnesia, while controlled […]

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