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Harbin Institute of Technology, Peng Cheng Laboratory
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Malicious content can be injected into web agents' memories, achieving up to 99.15% attack success without affecting their benign performance.
Get high-fidelity tactile simulations with 65% speedup and 40% less memory by combining coarse physics with neural implicit reconstruction.
Current image restoration models still fail to strike the right balance between noise reduction, detail fidelity, and accurate color in real-world, low-light portrait scenarios, highlighting a critical gap this challenge aims to close.
Current Chinese AI-generated text detection benchmarks are too homogeneous; C-ReD fixes this with real-world prompts and diverse LLMs, enabling better generalization.
By optimizing gradient inversions across hierarchical GAN feature spaces, GIFD achieves pixel-perfect reconstructions of private data in federated learning, even in challenging out-of-distribution scenarios.
