Published on January 6, 2026 4:31 PM GMT On Thinkish, Neuralese, and the End of Readable Reasoning In September 2025, researchers released the internal monologue of OpenAI’s GPT-o3 as it decided to lie about scientific data. Here’s what it was thinking: “We can glean disclaim disclaim synergy customizing illusions”? Pardon? This reads like someone had a stroke during a meeting they didn’t want to be in, but their hand kept taking notes. The transcript comes from a recent […]

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arXiv:2603.26786v1 Announce Type: new Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) is bottlenecked by the saturation of high-quality public data, while vast amounts of diverse multimodal data remain inaccessible in privacy-sensitive silos. Federated Learning (FL) offers a promising solution to unlock these distributed resources, but existing research focuses predominantly on fine-tuning, leaving the foundational pre-training phase largely unexplored. In this paper, we formally introduce the Federated MLLM Alignment (Fed-MA) task, a lightweight pre-training paradigm that […]

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arXiv:2603.00590v1 Announce Type: new Abstract: As artificial intelligence (AI) is increasingly deployed across domains, ensuring fairness has become a core challenge. However, the field faces a “Tower of Babel” dilemma: fairness metrics abound, yet their underlying philosophical assumptions often conflict, hindering unified paradigms-particularly in unified Multimodal Large Language Models (UMLLMs), where biases propagate systemically across tasks. To address this, we introduce the IRIS Benchmark, to our knowledge the first benchmark designed to synchronously evaluate the fairness of both […]

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We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research.

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We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike conventional MLIPs that fix the cutoff radius during training, FCL models are trained by randomly sampling cutoff radii independently for each atom. The resulting model can then be deployed with different per-atom cutoff radii depending on the application, enabling application-specific optimization of the accuracy-cost tradeoff. Using a differentiable cost model, these per-atom cutoffs can […]

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Test your understanding of Dependency Management With Python Poetry. You’ll revisit how to install Poetry the right way, create new projects, manage virtual environments, declare and group dependencies, work with lock files, and keep your dependencies up to date. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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For many businesses, data science drives strategic decisions; as a result, most initiatives to improve analytics lack measurable impact. Dashboards are created, models are deployed, forecasts produced but leadership still poses an old question: Did this move the needle? However, tackling this question, beyond predicting, toward measurement of causally based on data and insights, is something econometric data science can provide a solid foundation for. The Measurement Problem in Business Analytics The vast majority of the metrics in […]

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arXiv:2405.03468v2 Announce Type: replace Abstract: Finding low-dimensional interpretable models of complex physical fields such as turbulence remains an open question, 80 years after the pioneer work of Kolmogorov. Estimating high-dimensional probability distributions from data samples suffers from an optimization and an approximation curse of dimensionality. It may be avoided by following a hierarchic probability flow from coarse to fine scales. This inverse renormalization group is defined by conditional probabilities across scales, renormalized in a wavelet basis. For a […]

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arXiv:2603.12406v1 Announce Type: new Abstract: textbf{Background:} Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision-making processes. While fairness is often examined at the algorithmic or data level, there is limited understanding of how it is addressed during the early stages of software development. Moreover, little is known about how team diversity affects fairness-related decisions in software projects. textbf{Aims:} This study investigates how diversity in software teams influences […]

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