AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding

• URL: http://arxiv.org/abs/2604.08077v1
• Date: Thu, 09 Apr 2026 10:48:32 GMT
• Title: AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding
• Abstract summary: This paper introduces AdaSpark, an adaptive sparsity framework designed to address long-range temporal modeling limitations.<n>AdaSpark significantly reduces computational load by up to 57% FLOPs while preserving fine-grained, long-range dependencies, as validated on challenging hour-scale video benchmarks.
• Score: 57.46793076689158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fine-grained perception through irreversible information disposal or inhibit long-range temporal modeling via rigid, predefined sparse patterns. This paper introduces AdaSpark, an adaptive sparsity framework designed to address these limitations. AdaSpark first partitions video inputs into 3D spatio-temporal cubes. It then employs two co-designed, context-aware components: (1) Adaptive Cube-Selective Attention (AdaS-Attn), which adaptively selects a subset of relevant video cubes to attend for each query token, and (2) Adaptive Token-Selective FFN (AdaS-FFN), which selectively processes only the most salient tokens within each cube. An entropy-based (Top-p) selection mechanism adaptively allocates computational resources based on input complexity. Experiments demonstrate that AdaSpark significantly reduces computational load by up to 57% FLOPs while maintaining comparable performance to dense models and preserving fine-grained, long-range dependencies, as validated on challenging hour-scale video benchmarks.

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