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Current attention mechanisms misjudge the relevance of visual information, risking the loss of crucial context in multimodal dialog systems.
Not every missing modality needs to be repaired for optimal sentiment analysis, and SIEVE learns to make this decision dynamically at the sample level.
Forget brittle heuristics – NestedKV's multi-timescale anomaly detection and surprise-gated routing lets you compress KV caches far more aggressively without sacrificing long-context performance.
