1 citations · 1 across the 9 of their papers we have counts for
10 papers
Quantum-Structured World Models (QSWMs) for Predictive Latent Dynamics
Hailong Jiang, Emran Hossain, Feng Yu +3
World models learn latent states that summarize interaction histories, evolve over time, and support prediction, simulation, or planning. Most existing world models represent these…
EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
Guilin Zhang, Chuanyi Sun, Kai Zhao +3
Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et al. (2023) showed, this proxy d…
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
Guilin Zhang, Chuanyi Sun, Kai Zhao +3
A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ev…
CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing
Guilin Zhang, Wulan Guo, Ziqi Tan +2
Deep learning applications at the network edge lead to a significant growth in AI-related carbon emissions, presenting a critical sustainability challenge. The existing edge comput…
Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments
Guilin Zhang, Wulan Guo, Ziqi Tan
Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficient…
Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
Guilin Zhang, Wulan Guo, Ziqi Tan +3
Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits perf…
Serverless GPU Architecture for Enterprise HR Analytics: A Production-Scale BDaaS Implementation
Guilin Zhang, Wulan Guo, Ziqi Tan +8
Industrial and government organizations increasingly depend on data-driven analytics for workforce, finance, and regulated decision processes, where timeliness, cost efficiency, an…
AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes
Guilin Zhang, Srinivas Vippagunta, Raghavendra Nandagopal +7
Serverless platforms such as Kubernetes are increasingly adopted in high-performance computing, yet autoscaling remains challenging under highly dynamic and heterogeneous workloads…
KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Guilin Zhang, Wulan Guo, Ziqi Tan +2
Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HP…
AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
Guilin Zhang, Wulan Guo, Ziqi Tan +1
Edge computing facilitates deep learning in resource-constrained environments, but challenges such as resource heterogeneity and dynamic constraints persist. This paper introduces…