Hi r/MachineLearning, We’re open-sourcing MCGrad, a Python package for multicalibration–developed and deployed in production at Meta. This work will also be presented at KDD 2026. The Problem: A model can be globally calibrated yet significantly miscalibrated within identifiable subgroups or feature intersections (e.g., “users in region X on mobile devices”). Multicalibration aims to ensure reliability across such subpopulations. The Solution: MCGrad reformulates multicalibration using gradient boosted decision trees. At each step, a lightweight booster learns to predict residual […]

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arXiv:2604.09854v1 Announce Type: new Abstract: LLMs have so far failed both to generate consistently compelling stories and to recognize this failure–on the leading creative-writing benchmark (EQ-Bench), LLM judges rank zero-shot AI stories above New Yorker short stories, a gold standard for literary fiction. We argue that existing rubrics overlook a key dimension of compelling human stories: narrative tension. We introduce the 100-Endings metric, which walks through a story sentence by sentence: at each position, a model predicts how […]

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arXiv:2603.08903v1 Announce Type: new Abstract: Sensemaking in collaborative work and learning is increasingly supported by GenAI systems, however, emerging evidence suggests that poorly designed GenAI systems tend to provide explicit instruction that groups passively follow, fostering over-reliance and eroding autonomous sensemaking. Group awareness tools (GATs) address this challenge through implicit guidance: rather than instructing groups on what to do, GATs externalize observable collaboration data through visualizations that reveal differences between group members to create cognitive conflict, which triggers […]

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Did the machine learning illusion go to your head? You spin up a notebook, clean a fixed set of data, and train a model until the accuracy shines. Confidence grows. The prototype is perfect, and it has excited the stakeholders. “Can this go live?” is the most dangerous question in data science. This is where most promising initiatives are cut short prematurely, or even cancelled outright, in many machine learning programs. The development of a single notebook prototype […]

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datasette PR #2689: Replace token-based CSRF with Sec-Fetch-Site header protection Datasette has long protected against CSRF attacks using CSRF tokens, implemented using my asgi-csrf Python library. These are something of a pain to work with – you need to scatter forms in templates with <input type=”hidden” name=”csrftoken” value=”{{ csrftoken() }}”> lines and then selectively disable CSRF protection for APIs that are intended to be called from outside the browser. I’ve been following Filippo Valsorda’s research here with interest, […]

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arXiv:2603.28992v1 Announce Type: new Abstract: We study training-free flow matching between two Gaussian mixture models (GMMs) using explicit velocity fields that transport one mixture into the other over time. Our baseline approach constructs component-wise Gaussian paths with affine velocity fields satisfying the continuity equation, which yields to a closed-form surrogate for the pairwise kinetic transport cost. In contrast to the exact Gaussian Wasserstein cost, which relies on matrix square-root computations, the surrogate admits a simple analytic expression derived […]

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The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitative approximation rates for ReLU networks on the class of $α$-Hölder functions $f: [0,1]^N to mathbb{R}$. The goal of this paper is to provide similar quantitative approximation results in the context of group equivariant learning, where the learned $α$-Hölder function is known to obey certain group symmetries. While there has been much interest in the […]

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arXiv:2602.23478v1 Announce Type: new Abstract: Control Barrier Functions (CBFs) are a powerful tool for ensuring robotic safety, but designing or learning valid CBFs for complex systems is a significant challenge. While Hamilton-Jacobi Reachability provides a formal method for synthesizing safe value functions, it scales poorly and is typically performed offline, limiting its applicability in dynamic environments. This paper bridges the gap between offline synthesis and online adaptation. We introduce refineCBF for refining an approximate CBF – whether analytically […]

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arXiv:2601.00167v1 Announce Type: new Abstract: Decision Transformers (DTs) have emerged as a powerful framework for sequential decision making by formulating offline reinforcement learning (RL) as a sequence modeling problem. However, extending DTs to online settings with pure RL gradients remains largely unexplored, as existing approaches continue to rely heavily on supervised sequence-modeling objectives during online finetuning. We identify hindsight return relabeling — a standard component in online DTs — as a critical obstacle to RL-based finetuning: while beneficial […]

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