technocracy

arXiv:2603.05637v1 Announce Type: new Abstract: The rapid adoption of foundation models has significantly expanded the capabilities of software systems, enabling them to perform complex language, reasoning, and interaction tasks that were previously difficult to automate. However, this progress has also introduced novel challenges that were largely absent in previous generations of software. In particular, the increasing integration of foundation models with external tools and resources raises new concerns regarding reliability, security, and robustness. The Model Context Protocol (MCP) […]

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Rodney v0.4.0 My Rodney CLI tool for browser automation attracted quite the flurry of PRs since I announced it last week. Here are the release notes for the just-released v0.4.0: Errors now use exit code 2, which means exit code 1 is just for for check failures. #15 New rodney assert command for running JavaScript tests, exit code 1 if they fail. #19 New directory-scoped sessions with –local/–global flags. #14 New reload –hard and clear-cache commands. #17 New […]

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When I first heard “cloud commitment insurance,” I nodded politely and had absolutely no idea what anyone was talking about. Insurance? For cloud commitments? What gets insured — the server? The discount? The contract? And who’s the insurance company here? I had several sessions with the Archera team trying to wrap my head around it. Here’s the thing that made it so confusing: their product is genuinely simple. So simple that the simplicity itself became the barrier. There […]

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Gowtham Reddy Kunduru is a lead software engineer with a successful career spanning healthcare, FinTech, and cloud architecture. His success wasn’t always assured; in fact, as he puts it, his “story began with setbacks.”  In college, I was written off as someone who would never amount to much. Being detained twice was humiliating, but it became the turning point in my life. I realized that if I didn’t take control, no one else would. So, I rebuilt myself […]

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arXiv:2506.18249v2 Announce Type: replace-cross Abstract: We introduce Principal Component Analysis guided Quantile Sampling (PCA QS), a novel sampling framework designed to preserve both the statistical and geometric structure of large scale datasets. Unlike conventional PCA, which reduces dimensionality at the cost of interpretability, PCA QS retains the original feature space while using leading principal components solely to guide a quantile based stratification scheme. This principled design ensures that sampling remains representative without distorting the underlying data semantics. We […]

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arXiv:2604.20847v1 Announce Type: new Abstract: Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to exploit the intrinsic characteristics of audio and consequently leads to suboptimal performance, particularly in cold-start scenarios. However, existing music recommendation datasets often lack rich multimodal information, such as raw audio signals and descriptive textual metadata. Moreover, current recommender system evaluation frameworks remain inadequate, as they […]

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arXiv:2601.15496v1 Announce Type: new Abstract: In this paper, we study the timeliness of actions in communication systems where actuation is constrained by control permissions or energy availability. Building on the Age of Actuation (AoA) metric, which quantifies the timeliness of actions independently of data freshness, we introduce a new metric, the emph{Age of Actuated Information (AoAI)}. AoAI captures the end-to-end timeliness of actions by explicitly accounting for the age of the data packet at the moment it is […]

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All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K%, reference calibration), each bounded by the designer’s intuition. We introduce the first transferable learned attack, enabled by the observation that fine-tuning any model on any corpus yields unlimited labeled data, since membership is known by construction. This removes the shadow model bottleneck and brings membership inference into the deep learning era: learning what matters rather than designing it, with generalization through training […]

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Federated learning has emerged as a paradigm to train models collaboratively on inherently distributed client data while safeguarding privacy. In this context, personalized federated learning tackles the challenge of data heterogeneity by equipping each client with a dedicated model. A prevalent strategy decouples the model into a shared feature extractor and a personalized classifier head, where the latter actively guides the representation learning. However, previous works have focused on classifier head-guided personalization, neglecting the potential personalized characteristics in […]

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arXiv:2510.24187v2 Announce Type: replace Abstract: We consider the adversarial linear bandits setting and present a unified algorithmic framework that bridges Follow-the-Regularized-Leader (FTRL) and Follow-the-Perturbed-Leader (FTPL) methods, extending the known connection between them from the full-information setting. Within this framework, we introduce self-concordant perturbations, a family of probability distributions that mirror the role of self-concordant barriers previously employed in the FTRL-based SCRiBLe algorithm. Using this idea, we design a novel FTPL-based algorithm that combines self-concordant regularization with efficient stochastic […]

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