1 citations · 1 across the 12 of their papers we have counts for
4 papers · 1 filter
From Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG
Zlatan Feric, Amir Taherin, Yanzhi Wang +1
Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieved context lengthens the prom…
Collaborative Compression for Large-Scale MoE Deployment on Edge
Yixiao Chen, Yanyue Xie, Ruining Yang +6
The Mixture of Experts (MoE) architecture is an important method for scaling Large Language Models (LLMs). It increases model capacity while keeping computation cost low. However,…
Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs
Amir Taherin, Juyi Lin, Arash Akbari +5
Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platform…
Digital Avatars: Framework Development and Their Evaluation
Timothy Rupprecht, Sung-En Chang, Yushu Wu +9
We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor,…