ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series
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Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.
citing papers explorer
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ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection
ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
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Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.