March 2026

Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. This issue is especially problematic in data-intensive structure-property learning tasks such as Image-to-Spectrum (Im2Spec) and Spectrum-to-Image (Spec2Im) translations, where standard active learning strategies can mistakenly prioritize poor-quality measurements. We introduce a gated active learning framework that combines curiosity-driven sampling with a physics-informed quality control filter based on the Simple Harmonic Oscillator model fits, allowing the […]

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World models that predict future states from video remain limited by flat latent representations that entangle objects, ignore causal structure, and collapse temporal dynamics into a single scale. We present HCLSM, a world model architecture that operates on three interconnected principles: object-centric decomposition via slot attention with spatial broadcast decoding, hierarchical temporal dynamics through a three-level engine combining selective state space models for continuous physics, sparse transformers for discrete events, and compressed transformers for abstract goals, and causal […]

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Release: datasette-files 0.1a3 I’m working on integrating datasette-files into other plugins, such as datasette-extract. This necessitated a new release of the base plugin. owners_can_edit and owners_can_delete configuration options, plus the files-edit and files-delete actions are now scoped to a new FileResource which is a child of FileSourceResource. #18 The file picker UI is now available as a <datasette-file-picker> Web Component. Thanks, Alex Garcia. #19 New from datasette_files import get_file Python API for other plugins that need to access […]

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Reinforcement learning (RL) has shown promise for trading, yet most open-source backtesting environments assume negligible or fixed transaction costs, causing agents to learn trading behaviors that fail under realistic execution. We introduce three Gymnasium-compatible trading environments — MACE (Market-Adjusted Cost Execution) stock trading, margin trading, and portfolio optimization — that integrate nonlinear market impact models grounded in the Almgren-Chriss framework and the empirically validated square-root impact law. Each environment provides pluggable cost models, permanent impact tracking with exponential […]

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In the world of voice AI, the difference between a helpful assistant and an awkward interaction is measured in milliseconds. While text-based Retrieval-Augmented Generation (RAG) systems can afford a few seconds of ‘thinking’ time, voice agents must respond within a 200ms budget to maintain a natural conversational flow. Standard production vector database queries typically add 50-300ms of network latency, effectively consuming the entire budget before an LLM even begins generating a response. Salesforce AI research team has released […]

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For centuries, mathematicians worked on problems where the convergence is either smooth or does not happen. Now the concept of chaotic convergence is mainstream, popularized by the stochastic gradient descent in deep neural networks, central to LLMs. In my most recent book here, I discuss many cases involving various types of chaotic convergence. In some examples, the chaos exhibits a fractal structure. In others, patterns are strikingly similar to quantum dynamics. This article — a new addition to […]

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In your opinion, what are some of the most interesting/relevant open questions in RL right now? In any topic like inverse RL, imitation learning, model-based RL or more frontier lab focused like model-free, deep-RL, or RLHF-related questions. submitted by /u/sassafrassar [link] [comments]

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Clinical trials are characterized by high costs, extended timelines, and substantial operational risk, yet reliable prospective methods for predicting trial success before initiation remain limited. Existing artificial intelligence approaches often focus on isolated metrics or specific development stages and frequently rely on variables unavailable at the trial design phase, limiting real-world applicability. We present a hierarchical latent risk-aware machine learning framework for prospective prediction of clinical trial operational success using a curated subset of TrialsBank, a proprietary AI-ready […]

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Every major AI model goes through RLHF — thousands of paid contractors rating AI outputs to teach models what good looks like. But here’s what bothers me: These contractors are paid per task — incentivized to finish fast not feel deeply. They’re rating synthetic scenarios not real emotional situations. They burn out after thousands of repetitive evaluations. The result is AI that passes every benchmark but fails every real human moment. OpenAI spent $100M+ on this process. And […]

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