arXiv:2604.01370v1 Announce Type: new Abstract: Digital devices like tablets, media players, and kiosks are increasingly deployed in U.S. prisons. These technologies can enable incarcerated people to access education, communicate with loved ones, and develop vital reentry skills. However, they can also introduce new privacy and security risks for incarcerated people who have little agency over their usage and contracts, and are currently carved out of many consumer protection safeguards. To investigate these issues, we conducted focus groups and […]

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Group-relative policy optimization methods train language models by generating multiple rollouts per prompt and normalizing rewards with a shared mean reward baseline. In resource-constrained settings where the rollout budget is small, accuracy often degrades. We find that noise in the shared baseline induces advantage sign flips, where some rollouts receive an incorrect advantage sign, and the update direction is reversed. To address this, we propose Median-Centered Group Relative Policy Optimization (MC-GRPO), a simple and effective solution for small-rollout […]

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arXiv:2602.20164v1 Announce Type: new Abstract: Knowledge distillation offers a transformative pathway to developing powerful, yet efficient, small language models (SLMs) suitable for resource-constrained environments. In this paper, we benchmark the performance and computational cost of distilled models against their vanilla and proprietary counterparts, providing a quantitative analysis of their efficiency. Our results demonstrate that distillation creates a superior performance-tocompute curve. We find that creating a distilled 8B model is over 2,000 times more compute-efficient than training its vanilla […]

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arXiv:2601.08953v1 Announce Type: new Abstract: Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making […]

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arXiv:2602.04596v1 Announce Type: new Abstract: TabPFN is a transformer that achieves state-of-the-art performance on supervised tabular tasks by amortizing Bayesian prediction into a single forward pass. However, there is currently no method for uncertainty decomposition in TabPFN. Because it behaves, in an idealised limit, as a Bayesian in-context learner, we cast the decomposition challenge as a Bayesian predictive inference (BPI) problem. The main computational tool in BPI, predictive Monte Carlo, is challenging to apply here as it requires […]

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Everyone else is telling you to learn more, read more, stack more frameworks, chase more insights. I’m here to say the opposite: the most powerful thing you can do this year is deliberately know less.  I’m not talking about becoming stupid. I’m talking about unlearning the borrowed certainty that’s keeping you stuck.  Because here’s what I’ve seen, in myself first, then in way too many people who message me: The smartest, most thoughtful people are often the most stalled. You’ve […]

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We study the prophet inequality, a fundamental problem in online decision-making and optimal stopping, in a practical setting where rewards are observed only through noisy realizations and reward distributions are unknown. At each stage, the decision-maker receives a noisy reward whose true value follows a linear model with an unknown latent parameter, and observes a feature vector drawn from a distribution. To address this challenge, we propose algorithms that integrate learning and decision-making via lower-confidence-bound (LCB) thresholding. In […]

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arXiv:2601.07995v1 Announce Type: new Abstract: Use cases of sentiment analysis in the humanities often require contextualized, continuous scores. Concept Vector Projections (CVP) offer a recent solution: by modeling sentiment as a direction in embedding space, they produce continuous, multilingual scores that align closely with human judgments. Yet the method’s portability across domains and underlying assumptions remain underexplored. We evaluate CVP across genres, historical periods, languages, and affective dimensions, finding that concept vectors trained on one corpus transfer well […]

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