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This survey examines inference-efficiency techniques for video large language models, analyzing cost reductions across frame sampling, encoding, token compression, and language model stages while identifying evaluation gaps.
Video-Oasis reveals that 55% of existing video benchmarks can be solved without visual input, exposing significant capability gaps in current video understanding models. State-of-the-art models perform only marginally above random guessing on the remaining video-native challenges.
LiteFrame introduces a highly efficient video encoder for Video LLMs that uses Compressed Token Distillation to enable up to 8x more frames and 35% latency reduction while maintaining accuracy, setting a new Pareto frontier for long-form video understanding.