STEM education researchers are often interested in identifying moments of students’ mechanistic reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segments with a high concentration of such reasoning. We offer a solution in the form of an interpretable machine learning model that outputs time-varying probabilities that individual students are engaging in acts of mechanistic reasoning, leveraging evidence from their own utterances as well as contributions from the rest of […]

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Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was actually thinking and doing at each step. OpenAI team is taking a direct swing at that problem with the release of Euphony, a […]

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We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML) controllers are increasingly deployed in (autonomous) cyber-physical systems because of their ability to solve complex decision-making tasks. However, their accuracy can degrade sharply in unfamiliar environments, creating significant safety concerns. Traditional ensemble methods aim to improve robustness by averaging or voting across multiple controllers, yet this often dilutes the specialized strengths that individual controllers exhibit in different operating contexts. We argue that, […]

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arXiv:2601.04263v1 Announce Type: new Abstract: Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Current knowledge distillation in time series is predominantly based on logit and feature aligning techniques originally developed for computer vision tasks. These methods do not explicitly account for temporal data and fall short in two key aspects. First, the mechanisms by which the transferred knowledge helps the student […]

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Last week, BBC journalist Thomas Germain wrote a fake blog post claiming he was the world’s greatest hot-dog-eating tech journalist. Within 24 hours, ChatGPT and Google were repeating it as fact. Claude, made by Anthropic, wasn’t fooled. One AI out of three caught the lie. The entire industry treated that as an interesting footnote. An engineer would call it the most important finding in the article. Two Inspectors Approved a Defective Part I am 75 years old. I […]

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I am taking a course on reinforcement learning and to pass the exam I need to propose and implement a project. After some thought, I came up with the idea of applying reinforcement learning to the problem of finding a sequence of actions, specifically, paper folds, that transform a flat sheet of paper into a desired target shape, given an origami model. It is a kind of inverse kinematics problem, but instead of robots, it is for sheets […]

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The classification of mental health is challenging for a variety of reasons. For one, there is overlap between the mental health issues. In addition, the signs of mental health issues depend on the context of the situation, making classification difficult. Although fine-tuning transformers has improved the performance for mental health classification, standard cross-entropy training tends to create entangled feature spaces and fails to utilize all the information the transformers contain. We present a new framework that focuses on […]

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