arXiv:2603.01719v1 Announce Type: new Abstract: Conformal prediction (CP) provides finite-sample, distribution-free marginal coverage, but standard conformal regression intervals can be inefficient under heteroscedasticity and skewness. In particular, popular constructions such as conformalized quantile regression (CQR) often inherit a fixed notion of center and enforce equal-tailed errors, which can displace the interval away from high-density regions and produce unnecessarily wide sets. We propose Co-optimization for Adaptive Conformal Prediction (CoCP), a framework that learns prediction intervals by jointly optimizing a […]

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In this quiz, you’ll test your understanding of ChatterBot: Build a Chatbot With Python. You’ll revisit how ChatterBot learns from conversation data, how it picks replies based on similarity to what it’s already seen, and how it can pull in a local LLM to round out its responses. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn […]

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Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes learned relationships difficult to interpret and prone to overfitting as the extent of nonlocal information grows. We address this challenge by introducing data-driven integration kernels, a framework that adds structure to nonlocal operator learning by explicitly separating nonlocal information aggregation from local nonlinear […]

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From Wired: “We’re changing Fable 5’s safeguards for frontier LLM development to make them visible.” Anthropic said in a statement to WIRED. “We made the wrong tradeoff and we apologize for not getting the balance right.” Anthropic now says it’s changing course, and that Claude Fable 5’s safeguards for AI development will be visible to users. If the company suspects a user is trying to use Claude to build a highly capable AI it will alert them that […]

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arXiv:2601.07144v2 Announce Type: replace Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group fairness requiring that the probability of matching two individuals from any two given groups in the OT plan satisfies a predefined target. We first propose a modified Sinkhorn algorithm to compute perfectly fair transport plans efficiently. Since exact fairness can significantly degrade matching quality in practice, […]

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TL;DR I don’t know what I believe about AI anymore. Some days I’m excited. Some days I’m scared. Some days I’m just tired of the shouting. And honestly? That’s okay. We don’t need to pick a team. The useful thing isn’t being “for AI” or “against AI.” The useful thing is staying honest about how complicated this feels—and letting myself sit with the discomfort long enough to understand what’s actually at stake. Also, heavy metal got there before […]

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arXiv:2603.02222v1 Announce Type: new Abstract: MedCalc-Bench is a widely used benchmark for evaluating LLM performance on clinical calculator tasks, with state-of-the-art direct prompting scores plateauing around 35% on the Verified split (HELM MedHELM leaderboard) and the best published approach-RL with verifiable rewards-reaching 74%. We present three contributions that challenge the benchmark’s current framing. First, we conduct a systematic audit of the benchmark’s calculator implementations, identifying and fixing over 20 errors ranging from critical formula inaccuracies to runtime bugs […]

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