technocracy

In this paper, we focus on the online stochastic optimization problems in which the random parameters follow time-varying distributions. At each round t, decision is obtained from solving current optimization problem.Then samples are drawn from distributions which are updated after obtaining decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. For solving the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which […]

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arXiv:2604.18775v1 Announce Type: new Abstract: Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarely. In this work, we present an empirical study of output based jailbreak detection under realistic conditions using the JailbreakBench Behaviors dataset and multiple generator models with varying alignment strengths. We evaluate both a lexical TF-IDF detector and a generation inconsistency based detector across different sampling budgets. Our results show that single output evaluation systematically […]

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arXiv:2601.21105v1 Announce Type: new Abstract: Natural-language user profiles have recently attracted attention not only for improved interpretability, but also for their potential to make recommender systems more steerable. By enabling direct editing, natural-language profiles allow users to explicitly articulate preferences that may be difficult to infer from past behavior. However, it remains unclear whether current natural-language-based recommendation methods can follow such steering commands. While existing steerability evaluations have shown some success for well-recognized item attributes (e.g., movie genres), […]

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arXiv:2604.07532v1 Announce Type: new Abstract: Vehicular Ad Hoc Networks (VANETs) are vulnerable to intelligent attackers who exploit the homogeneous treatment of traffic events in existing trust models. These attackers accumulate reputation by reporting correctly on low-priority events and then inject false data during safety-critical situations – a strategy that current approaches cannot detect because they ignore event severity and location criticality in trust calculations. This paper addresses this gap through three contributions. First, it introduces event-aware and location-aware […]

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arXiv:2602.09127v1 Announce Type: new Abstract: Recent generative and tool-using AI systems can surface a large volume of candidates at low marginal cost, yet only a small fraction can be checked carefully. This creates a decoder-side bottleneck: downstream decision-makers must form reliable posteriors from many public records under scarce attention. We formalize this regime via Attention-Constrained Inference (ACI), in which a cheap screening stage processes $K$ records and an expensive verification stage can follow up on at most $B$ […]

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arXiv:2203.00554v2 Announce Type: replace Abstract: Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly unrelated units together. To overcome this problem, we develop theoretical results which motivate the use of neural networks to obtain non-trivial, multivariate balancing scores of a chosen level of coarseness, in contrast […]

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Release: datasette-files 0.1a3 I’m working on integrating datasette-files into other plugins, such as datasette-extract. This necessitated a new release of the base plugin. owners_can_edit and owners_can_delete configuration options, plus the files-edit and files-delete actions are now scoped to a new FileResource which is a child of FileSourceResource. #18 The file picker UI is now available as a <datasette-file-picker> Web Component. Thanks, Alex Garcia. #19 New from datasette_files import get_file Python API for other plugins that need to access […]

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