As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant's goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.

High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmarks to discriminate between competing modeling approaches, yet no standardized evaluation frameworks exist for intracranial EEG (iEEG) recordings. To address this gap, we present Neuroprobe: a suite of decoding tasks for studying multi-modal language processing in the brain. Unlike scalp EEG, intracranial EEG requires invasive surgery to implant electrodes that record neural activity directly from the brain with minimal signal distortion. Neuroprobe is built on the BrainTreebank dataset, which consists of 40 hours of iEEG recordings from 10 human subjects performing a naturalistic movie viewing task. Neuroprobe serves two critical functions. First, it is a mine from which neuroscience insights can be drawn. Its high temporal and spatial resolution allows researchers to systematically determine when and where computations for each aspect of language processing occur in the brain by measuring the decodability of each feature across time and all electrode locations. Using Neuroprobe, we visualize how information flows from the superior temporal gyrus to the prefrontal cortex, and the progression from simple auditory features to more complex language features in a purely data-driven manner. Second, as the field moves toward neural foundation models, Neuroprobe provides a rigorous framework for comparing competing architectures and training protocols. We found that the linear baseline is surprisingly strong, beating frontier foundation models on many tasks. Neuroprobe is designed with computational efficiency and ease of use in mind. We make the code for Neuroprobe openly available and maintain a public leaderboard, aiming to enable rapid progress in the field of iEEG foundation models, at https://neuroprobe.dev/

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Modern laundry detergents work better in cold water. Breakthroughs, discoveries, and DIY tips sent every weekday. You may wash your clothes with hot water--many people do. That could be a mistake. Modern washing machines, and modern laundry detergent, are designed to work well at cold temperatures.

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Elon Musk endorses Advance UK , a new right-wing party led by Ben Habib that challenges Britain's traditional two-party political system.

The FBI warned transnational gangs who have helped turn a busy commercial strip in Rep. Alexandria Ocasio-Cortez’s New York City district into a crime-infested hot spot.