technocracy

arXiv:2602.21928v1 Announce Type: cross Abstract: Root cause analysis (RCA) in networked industrial systems, such as supply chains and power networks, is notoriously difficult due to unknown and dynamically evolving interdependencies among geographically distributed clients. These clients represent heterogeneous physical processes and industrial assets equipped with sensors that generate large volumes of nonlinear, high-dimensional, and heterogeneous IoT data. Classical RCA methods require partial or full knowledge of the system’s dependency graph, which is rarely available in these complex networks. […]

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Financial forecasting is challenged by non-stationarity, volatility clustering, and regime transitions. Many neural forecasting pipelines use expressive encoders but fixed decoder mappings, which can reduce robustness under distribution shift. This paper introduces RADIAN, a forecasting architecture that keeps representation learning regime-agnostic and applies deterministic, causal regime conditioning only in fusion and decoding modules, using a four-dimensional regime vector computed from normalized one-step target returns within the input window. RADIAN is evaluated under a fixed protocol on nine hourly […]

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arXiv:2602.22352v1 Announce Type: new Abstract: With the continuous growth of neural network scales, low-precision quantization is widely used in edge accelerators. Classic multi-threshold activation hardware requires 2^n thresholds for n-bit outputs, causing a rapid increase in hardware cost as precision increases. We propose a reconfigurable activation hardware, GRAU, based on piecewise linear fitting, where the segment slopes are approximated by powers of two. Our design requires only basic comparators and 1-bit right shifters, supporting mixed-precision quantization and nonlinear […]

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Elon Musk’s legal effort to dismantle OpenAI may hinge on how its for-profit subsidiary enhances or detracts from the frontier lab’s founding mission of ensuring that humanity benefits from artificial general intelligence.

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arXiv:2601.15363v1 Announce Type: new Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and perform suboptimally in online, non-stationary scenarios. We propose SmoothFBO, the first algorithm for non-stationary FBO with both theoretical guarantees and practical scalability. SmoothFBO introduces a time-smoothed stochastic hypergradient estimator that reduces variance through a window parameter, enabling stable outer-loop updates with sublinear regret. Importantly, the classical parametric bilevel case […]

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arXiv:2602.11059v1 Announce Type: new Abstract: This paper addresses the issue of inversion in cases where (1) the observation system is modeled by a linear transformation and additive noise, (2) the problem is ill-posed and regularization is introduced in a Bayesian framework by an a prior density, and (3) the latter is modeled by a diffusion process adjusted on an available large set of examples. In this context, it is known that the issue of posterior sampling is a […]

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In this quiz, you’ll test your understanding of How to Use OpenCode for AI-Assisted Python Coding. By working through these questions, you’ll revisit how to install OpenCode, connect it to an AI provider, configure project context with AGENTS.md, and take advantage of features like mid-session model switching and built-in language servers. If you’d like a broader look at AI-assisted Python development, you can also follow the Python Coding With AI learning path. [ Improve Your Python With 🐍 […]

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Audio is a fundamental modality for analyzing speech, music, and environmental sounds. Although pretrained audio models have significantly advanced audio understanding, they remain fragile in real-world settings where data distributions shift over time. In this work, we present the first systematic benchmark for audio continual learning (CL) with pretrained models (PTMs), together with a comprehensive analysis of its unique challenges. Unlike in vision, where parameter-efficient fine-tuning (PEFT) has proven effective for CL, directly transferring such strategies to audio […]

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