ONOTE is a multi-format benchmark that applies a deterministic pipeline to expose a disconnect between perceptual accuracy and music-theoretic comprehension in leading omnimodal AI models.
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MSAO cuts end-to-end latency by 30% and resource overhead by 30-65% for multimodal LLM inference through sparsity-aware edge-cloud offloading while preserving accuracy.
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ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence
ONOTE is a multi-format benchmark that applies a deterministic pipeline to expose a disconnect between perceptual accuracy and music-theoretic comprehension in leading omnimodal AI models.
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MSAO: Adaptive Modality Sparsity-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
MSAO cuts end-to-end latency by 30% and resource overhead by 30-65% for multimodal LLM inference through sparsity-aware edge-cloud offloading while preserving accuracy.