Published on December 7, 2025 2:46 AM GMT “How are you coping with the end of the world?” journalists sometimes ask me, and the true answer is something they have no hope of understanding and I have no hope of explaining in 30 seconds, so I usually answer something like, “By having a great distaste for drama, and remembering that it’s not about me.” The journalists don’t understand that either, but at least I haven’t wasted much time […]

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Causal Thinking in the Age of Big Data: Modern Econometrics for Data Scientists. The advent of big data has revolutionized how business is done. Predictive models now rule over modern analytics stacks from recommendation engines to demand forecasting and fraud detection. However, as data scientists increasingly impact policy and strategy, the inherent limitation of prediction-only thinking has become obvious: prediction does not imply causation. Knowing what is going to happen isn’t the same as knowing why it’s going […]

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arXiv:2507.09905v3 Announce Type: replace-cross Abstract: In multi-source learning with discrete labels, distributional heterogeneity across domains poses a central challenge to developing predictive models that transfer reliably to unseen domains. We study multi-source unsupervised domain adaptation, where labeled data are available from multiple source domains and only unlabeled data are observed from the target domain. To address potential distribution shifts, we propose a novel Conditional Group Distributionally Robust Optimization (CG-DRO) framework that learns a classifier by minimizing the worst-case […]

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arXiv:2601.22365v1 Announce Type: new Abstract: The Gilbert-Pollak Conjecture citep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in the Euclidean plane, the Steiner minimum tree has length at least $sqrt{3}/2 approx 0.866$ times that of the Euclidean minimum spanning tree (the Steiner ratio). A sequence of improvements through the 1980s culminated in a lower bound of $0.824$, with no substantial progress reported over the past three decades. Recent advances in LLMs have […]

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Parallelization in Reinforcement Learning is typically employed to speed up the training of a single policy, where multiple workers collect experience from an identical sampling distribution. This common design limits the potential of parallelization by neglecting the advantages of diverse exploration strategies. We propose K-Myriad, a scalable and unsupervised method that maximizes the collective state entropy induced by a population of parallel policies. By cultivating a portfolio of specialized exploration strategies, K-Myriad provides a robust initialization for Reinforcement […]

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Built a text classifier that automatically rates code comment quality to help with documentation reviews. **Quick Stats:** – 🎯 94.85% accuracy on test set – 🤖 Fine-tuned DistilBERT (66.96M params) – 🆓 MIT License (free to use) – ⚡ Easy integration with Transformers **Categories:** 1. Excellent (100% precision) – Comprehensive, clear documentation 2. Helpful (89% precision) – Good but could be better 3. Unclear (100% precision) – Vague or confusing 4. Outdated (92% precision) – Deprecated/TODO comments **Try […]

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We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method allows us to variationally integrate out the input variables of the Gaussian process and compute a lower bound on the exact marginal likelihood of the nonlinear latent variable model. The maximization of the variational lower bound provides a Bayesian training procedure that is robust to overfitting and can automatically select the dimensionality of the nonlinear […]

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arXiv:2601.16219v1 Announce Type: new Abstract: Large Language Models (LLMs) excel in general tasks but often struggle with hallucinations when handling domain-specific or institutional knowledge absent from their pre-training. We present an offline response-based knowledge distillation method that develops high-accuracy specialized assistants under constrained hardware resources. We evaluate three distinct data strategies: general domain adaptation (15,000 lines), unstructured knowledge injection (2,000 lines), and a context-aware synthetic dataset (500 lines) generated by a teacher model. To minimize computational costs, we […]

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arXiv:2601.21083v1 Announce Type: new Abstract: As large language models improve, so do their offensive applications: frontier agents now generate working exploits for under $50 in compute (Heelan, 2026). Defensive incident response (IR) agents must keep pace, but existing benchmarks conflate action execution with correct execution, hiding calibration failures when agents process adversarial evidence. We introduce OpenSec, a dual-control reinforcement learning environment that evaluates IR agents under realistic prompt injection scenarios. Unlike static capability benchmarks, OpenSec scores world-state-changing containment […]

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