[Full Video Replay] Galaxy XR: Merging Multimodal AI With Extended Reality - samsung.com
[Full Video Replay] Galaxy XR: Merging Multimodal AI With Extended Reality samsung.com
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Avoid Re-encoding Reference Images in Vision-LLM When Comparison Criteria Are User-Defined
Hi everyone, I’m working with a Vision-LLM (like Qwen-VL / LLaVA / llama.cpp-based multimodal models) where I need to compare new images against reference images. The key part of my use case is that users define the comparison criteria (e.g., fur length, ear shape, color patterns), and I’m using image-to-text models to evaluate how well a new image matches a reference according to these criteria. Currently, every time I send a prompt including the reference images, the model re-encodes them from scratch . From the logs, I can see: llama-server encoding image slice... image slice encoded in 3800–4800 ms decoding image batch ... Even for the same reference images, this happens every single request , which makes inference slow. Questions: Has anyone dealt with user-defined comparison criteria
MURMR: A Multimodal Sensing Framework for Automated Group Behavior Analysis in Mixed Reality
arXiv:2507.11797v3 Announce Type: replace Abstract: When teams coordinate in immersive environments, collaboration breakdowns can go undetected without automated analysis, directly affecting task performance. Yet existing methods rely on external observation and manual annotation, offering no annotation-free method for analyzing temporal collaboration dynamics from headset-native data. We introduce \sysname, a passive sensing pipeline that captures and analyzes multimodal interaction data from commodity MR headsets without external instrumentation. Two complementary modules address different levels of analysis: a structural module that generates automated multimodal sociograms and network metrics at both session and intra-session granularities, and a temporal module that applies unsupervis
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We Need Positive Visions of the Future
People don't want to talk about positive visions of the future, because it is not timely and because it's not the pressing problem. Preventing AI doom already seems so unlikely that caring about what happens in case we succeed feels meaningless. I agree that it seems very unlikely. But I think we still need to care about it, to some extent, even if only for psychological and strategic reasons. And I think this neglect is itself contributing to the very dynamics that make success less likely. The Desperation Engine Some people — or, arguably, many people — go to work on AI capabilities because they see it as kind of "the only hope." "So what now, if we pause AI?", they ask. The problem is that even with paused AI, the future looks grim. Institutional decay continues, aging continues, regula
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