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Preprint
Oct 2026
Video-MemComp: Taylor-Guided Sensitivity-Aware Memory Management for Streaming Video Understanding
The rapid growth of video data has created an urgent need for efficient video understanding systems that can handle long and continuous visual inputs, posing significant challenges for Multimodal Large Language Models (MLLMs) due to severe memory bottlenecks. Moreover, existing compression methods often overlook the ex...
Yong-Hui Niu, Yu-Hao Ji, Chang Nie et al.
· 0 citations
Open access
Oct 2026
Adaptive storage optimization for deep learning workloads in converged HPC-cloud environments: a reinforcement learning approach
A deep reinforcement learning framework is introduced that combines Proximal Policy Optimization and Soft Actor-Critic with a Transformer-based state encoder to predict workload phase transitions rather than merely respond to them and indicates that temporally aware storage control can improve the efficiency of distrib...
H. Baghdasaryan, H. Astsatryan
· Scientific Reports · 0 citations
Open access
Sep 2026
An efficient on-policy actor–critic architecture for adaptive bitrate streaming using deep reinforcement learning
The proposed GRASP algorithm outperforms Comyco and other cutting-edge DRL-based ABR methods under a variety of network situations and reduces the stall ratio compared to Comyco up to 67% and 70% for 5G and 4G networks respectively.
Mandan Naresh, Paresh Saxena, Manik Gupta
· Scientific Reports · 0 citations
Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging
Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-constrained, single-GPU systems. However, this benefit is difficult to realize because expert parameters dominate memory, and token-level routing is dynamic, unpredictab...
Sanjali Yadav, Bahar Asgari
· 0 citations
Preprint
Oct 2026
CtrlCache: Accelerating Interactive Video World Models with Control-Aware Caching
Interactive video world models need to generate each video chunk efficiently while responding faithfully to user controls. Many systems use chunk-wise autoregressive generation with few-step denoising, but each chunk still requires several costly denoising iterations. Training-free caching can reduce this cost, yet exi...
Shang-Ye Song, Dong Gong, Hong Jia et al.
· 0 citations
Sep 2026
ESM: energy-efficient bitrate adaptation for video streaming with deep reinforcement learning
Experimental results demonstrate that the proposed ESM algorithm achieves well-balanced multi-objective performance for mobile video streaming under hardware resource constraints, and the achieved optimization gains will fluctuate slightly across different combinations of video content and network conditions.
Wen-Long Ke, Ying Wang, Yong Zhang
· Cluster Computing · 0 citations