Dual-Signal Adaptive KV-Cache Optimization for Long-Form Video Understanding in Vision-Language Models

• URL: http://arxiv.org/abs/2602.14236v1
• Date: Sun, 15 Feb 2026 17:06:02 GMT
• Title: Dual-Signal Adaptive KV-Cache Optimization for Long-Form Video Understanding in Vision-Language Models
• Authors: Vishnu Sai, Dheeraj Sai, Srinath B, Girish Varma, Priyesh Shukla,
• Abstract summary: Vision-Language Models (VLMs) face a critical memory bottleneck when processing long-form video content.<n>We propose Sali-Cache, a novel a priori optimization framework that implements dual-signal adaptive caching.
• Score: 1.0811962707568015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) face a critical memory bottleneck when processing long-form video content due to the linear growth of the Key-Value (KV) cache with sequence length. Existing solutions predominantly employ reactive eviction strategies that compute full attention matrices before discarding tokens, resulting in substantial computational waste. We propose Sali-Cache, a novel a priori optimization framework that implements dual-signal adaptive caching through proactive memory management. By integrating a temporal filter based on optical flow analysis for detecting inter-frame redundancy and a spatial filter leveraging saliency detection for identifying visually significant regions, Sali-Cache intelligently manages memory allocation before entering computationally expensive attention operations. Experimental evaluation on the LLaVA 1.6 architecture demonstrates that our method achieves a 2.20x compression ratio in effective memory usage while maintaining 100% accuracy across BLEU, ROUGE-L, and Exact Match metrics. Furthermore, under identical memory budget constraints, Sali-Cache preserves context-rich features over extended temporal durations without degrading model performance, enabling efficient processing of long-form video content on consumer-grade hardware.

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