New Research Proposes Sequential Compression to Reduce Memory Bottlenecks in Multimodal AI Models

A recent technical study tackles the issue of memory usage in multimodal large language models (MLLMs) that handle visual inputs such as high-resolution images and lengthy videos. These models encounter major limitations during inference due to the storage of numerous vision tokens in key-value caches. Existing methods only compress redundant vision tokens after all inputs have been processed, resulting in elevated peak memory usage during the prefill phase. The research reveals that MLLMs possess structural regularities and representational redundancies that can be utilized to control memory growth throughout the inference. The authors suggest a sequential input-compression technique that maintains a fixed memory budget, aiming to manage memory expansion from the outset. This work emphasizes the increased memory requirements when scaling to richer visual representations, underscoring the importance of efficient cache management for practical applications. The paper was cross-submitted on arXiv with the identifier 2604.16734v1.