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MG-RWKV achieves state-of-the-art TFL performance with a groundbreaking O(T) complexity, redefining efficiency in audio-visual content authenticity verification.
Models may score well on benchmarks but often fail to meet strict perceptual requirements, revealing a hidden brittleness in multimodal evaluations.
Cycle consistency unlocks SOTA cross-view object correspondence in videos without ground-truth annotations, even enabling test-time training.
