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Singing deepfakes are a surprisingly effective attack vector, and this paper introduces a new benchmark and detection method to address the resulting security gap.
Attention's quadratic complexity is no longer a bottleneck: DASH-KV achieves linear O(N) inference without sacrificing accuracy by reformulating attention as an approximate nearest-neighbor search.
Ditch the haystack: Tri-RAG structures external knowledge into logical triplets, slashing irrelevant context and boosting RAG's reasoning power.
Video-LLMs hallucinate because they fixate on a single "anchor frame," but a simple decoder-side attention fix can dramatically improve grounding without retraining.
