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By focusing on low/mid-frequency patterns, UniSkip-Mamba outperforms existing models in forgery detection, achieving a remarkable 63.4% AP@0.95 on LAV-DF.
MG-RWKV achieves state-of-the-art TFL performance with a groundbreaking O(T) complexity, redefining efficiency in audio-visual content authenticity verification.
A tool-augmented agent achieves high compilation rates but reveals a shocking 29-point gap in semantic faithfulness, challenging the reliability of existing evaluation metrics.
FlowTrain redefines VLM training efficiency, achieving up to 1.7x throughput improvements by decoupling execution and optimizing resource allocation.
