Video large language models (Vid-LLMs), which excel in diverse video-language tasks, can be effectively constructed by adapting image-pretrained vision-language models (VLMs).However, this adaptation remains challenging, as it requires processing dense and temporally extended visual inputs that exceed the capacity of image-based models.This paper identifies the perception bottleneck and token overload as key challenges in extending image-based VLMs to the video domain.To address these issues, we propose D-CoDe, a training-free adaptation framework that incorporates dynamic compression and question decomposition.Specifically, dynamic compression alleviates the perception bottleneck through adaptive selection of representative frames and content-aware aggregation of spatial tokens, thereby reducing redundancy while preserving informative content.In parallel, question decomposition mitigates token overload by reformulating the original query into sub-questions, guiding the model to focus on distinct aspects of the video and enabling more comprehensive understanding.Experiments demonstrate that D-CoDe effectively improves video understanding across various benchmarks.Furthermore, strong performance on the challenging long-video benchmark highlights the potential of D-CoDe in handling complex video-language tasks.

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

Compression-based schemes have been widely studied to improve the lifetime and performance of solid-state drives (SSDs). Recently, the most popular flash-friendly file system (F2FS) started supporting compression to maximize the lifetime of NAND flash-based storage. Also, compression-based computational SSDs (CSDs) are developed due to their high performance, transparency, and easy adoption. This paper will first study the compression of F2FS and CSD to understand their features. Then, cooperative compression (COCO) is proposed to optimize performance and power consumption based on the combination of F2FS and CSD. Experiments on real devices show that COCO has encouraged optimization.