148 citations · 245 across the 11 of their papers we have counts for
14 papers
REINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning
Zhihong Cui, Hengyu Liu, Haoran Tang +3
Agent harnesses -- runtime scaffolds that wrap an LLM's reasoning loop with tool dispatch, verification, and persistent memory -- have become the standard substrate for deploying L…
Locating and Repairing Domain Shift in VLM Trajectory Planning
Zhihong Cui, Hengyu Liu, Michael Alexander Riegler +3
Vision-Language Models (VLMs) are increasingly adopted as end-to-end driving planners, yet they suffer from cross-city domain shift. Existing methods treat this shift as a single q…
GreenPipe: Power Modeling for Containerized DNN Inference on Kubernetes Edge Nodes
Mengxue Wang, Peini Liu, Amir Taherkordi +1
Distributed DNN inference is increasingly deployed in containerized edge-cloud environments, where workloads run on-device or are exposed to remote clients over the network. Accura…
GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning
Zhihong Cui, Hengyu Liu, Zhangkai Wu +5
Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities,…
Empirical Analysis of GPU Frequency Behavior Under ML Workloads
Truong-Thanh Le, Hoang-Loc La, Amir Taherkordi +3
This work presents ongoing research on the frequency scaling behavior of NVIDIA GPUs when executing ML/AI workloads. Our preliminary findings show that, on lower-performance GPUs,…
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1
Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications. While post-training quantization (PTQ) and structural p…
LLM Compression with Jointly Optimizing Architectural and Quantization choices
Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1
Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements. While some methods address this by developing small or tiny la…
E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments
Truong-Thanh Le, Amir Taherkordi, Hoang-Loc La +3
Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themselves, practical deployment mus…
C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
Zhihong Cui, Haoran Tang, Tianyi Li +4
Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in…
Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning
Chen Chen, Peiyuan Guan, Ziru Chen +3
Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and shortlived containers triggered by events, resulting in a reduction of mo…
Context-aware Container Orchestration in Serverless Edge Computing
Peiyuan Guan, Chen Chen, Ziru Chen +3
Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries o…
A Survey of Security and Privacy Issues in V2X Communication Systems
Takahito Yoshizawa, Dave Singelée, Jan Tobias Muehlberg +4
Vehicle-to-Everything (V2X) communication is receiving growing attention from industry and academia as multiple pilot projects explore its capabilities and feasibility. With about…
Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments
Mohammad Azangoo, Amir Taherkordi, Jan Olaf Blech +1
Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs…
Active Learning for Network Traffic Classification: A Technical Study
Amin Shahraki, Mahmoud Abbasi, Amir Taherkordi +1
Network Traffic Classification (NTC) has become an important feature in various network management operations, e.g., Quality of Service (QoS) provisioning and security services. Ma…