From brain scans to alloys: Teaching AI to make sense of complex research data

by AI Generated Robotic ContentAI/ML NewsPosted on Comments are Disabled

Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible—an assumption that can …

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MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer

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Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. …

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AdaBoN: Adaptive Best-of-N Alignment

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Recent advances in test-time alignment methods, such as Best-of-N sampling, offer a simple and effective way to steer language models (LMs) toward preferred behaviors using reward models (RM). However, these approaches can be computationally expensive, especially when applied uniformly across prompts without accounting for differences in alignment difficulty. In this work, we propose a prompt-adaptive …

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Crossmodal search with Amazon Nova Multimodal Embeddings

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Amazon Nova Multimodal Embeddings processes text, documents, images, video, and audio through a single model architecture. Available through Amazon Bedrock, the model converts different input modalities into numerical embeddings within the same vector space, supporting direct similarity calculations regardless of content type. We developed this unified model to reduce the need for separate embedding models, …

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Stanford’s AI spots hidden disease warnings that show up while you sleep

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Stanford researchers have developed an AI that can predict future disease risk using data from just one night of sleep. The system analyzes detailed physiological signals, looking for hidden patterns across the brain, heart, and breathing. It successfully forecast risks for conditions like cancer, dementia, and heart disease. The results suggest sleep contains early health …

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Discrete spatial diffusion models data while obeying scientific principles

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Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI models. Unlike generative diffusion models, the team’s Discrete Spatial Diffusion approach honors scientific and physics principles. The team validated their model on two challenging scientific applications—subsurface rock microstructures and lithium-ion battery electrodes—with promising results.

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I’m the Co-founder & CEO of Lightricks. We just open-sourced LTX-2, a production-ready audio-video AI model. AMA.

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Hi everyone. I’m Zeev Farbman, Co-founder & CEO of Lightricks. I’ve spent the last few years working closely with our team on LTX-2, a production-ready audio–video foundation model. This week, we did a full open-source release of LTX-2, including weights, code, a trainer, benchmarks, LoRAs, and documentation. Open releases of multimodal models are rare, and …

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