technocracy

arXiv:2601.15337v1 Announce Type: new Abstract: Users should not be systemically disadvantaged by the language they use for interacting with LLMs; i.e. users across languages should get responses of similar quality irrespective of language used. In this work, we create a set of real-world open-ended questions based on our analysis of the WildChat dataset and use it to evaluate whether responses vary by language, specifically, whether answer quality depends on the language used to query the model. We also […]

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arXiv:2601.15036v1 Announce Type: cross Abstract: Factorizable joint shift (FJS) was proposed as a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, it has been observed that FJS actually arises from consecutive label and covariate (or vice versa) shifts. Research into FJS so far has been confined to the case of categorical label spaces. We propose a framework for analysing distribution shift in the case of general label spaces, thus covering both […]

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Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts […]

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arXiv:2406.03707v2 Announce Type: replace-cross Abstract: Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture aspects of the syntax and semantics of language. But what should embeddings represent? We connect the autoregressive prediction objective to the idea of constructing predictive sufficient statistics to summarize the information contained in a sequence of observations, and use this connection to identify three settings where the optimal […]

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The global regularity of solutions to the two-dimensional (2D) incompressible Navier-Stokes equations (NSE) is disproved by demonstrating the existence of singularities in plane Couette flow. Using tools from the Sobolev space theory, the energy dissipation analysis, and the energy gradient theory (Dou, 2025), it is shown that the flow develops “velocity discontinuities” under generic disturbances, where the velocity is not differentiable, forming singularities of the Navier-Stokes equation. Thus, the solution fails to be in C^1(OmegaX[0, T )) for […]

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arXiv:2601.15348v1 Announce Type: new Abstract: Repeated exposure to violence and abusive content in music and song content can influence listeners’ emotions and behaviours, potentially normalising aggression or reinforcing harmful stereotypes. In this study, we explore the use of generative artificial intelligence (GenAI) and Large Language Models (LLMs) to automatically transform abusive words (vocal delivery) and lyrical content in popular music. Rather than simply muting or replacing a single word, our approach transforms the tone, intensity, and sentiment, thus […]

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arXiv:2601.07959v1 Announce Type: new Abstract: We study stable matchings that are robust to preference changes in the two-sided stable matching setting of Gale and Shapley [GS62]. Given two instances $A$ and $B$ on the same set of agents, a matching is said to be robust if it is stable under both instances. This notion captures desirable robustness properties in matching markets where preferences may evolve, be misreported, or be subject to uncertainty. While the classical theory of stable […]

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Wireless Sensor Networks (WSNs) have diverse applications in urban, industrial and environmental monitoring. However, the design complexity of this type of network is high, due to conflicting objectives such as latency, energy consumption, connectivity and coverage. This article addresses the need for structured and reproducible approaches to developing WSNs. We propose a modular and scalable system designed to integrate simulators and evolutionary algorithms for multi-objective optimization in WSNs. We present a formalized process and supporting architecture that combines […]

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arXiv:2507.05216v3 Announce Type: replace-cross Abstract: Many automated decision systems (ADS) are designed to solve prediction problems — where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders […]

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arXiv:2501.10806v3 Announce Type: replace-cross Abstract: Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point iterations where both time-scales have contractive mappings. In this work, we broaden the scope of such analyses by considering settings where the slower time-scale has a non-expansive mapping. For such algorithms, the slower time-scale can be viewed as a stochastic inexact Krasnoselskii-Mann iteration. We also […]

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