2 citations · 5 across the 14 of their papers we have counts for
4 papers · 1 filter
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments
Kefu Chen, Xin Ai, Qiange Wang +2
Graph Neural Networks (GNNs) have achieved remarkable success in various applications. Sampling-based GNN training, which conducts mini-batch training on sampled subgraphs, has bec…
ATCC: Adaptive Concurrency Control for Unforeseen Agentic Transactions
Weixing Zhou, Zhiyou Wang, Zeshun Peng +3
Data agents, empowered by Large Language Models (LLMs), introduce a new paradigm in transaction processing. Unlike traditional applications with fixed patterns, data agents run onl…
Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation
Qiange Wang, Chaoyi Chen, Jingqi Gao +3
This paper argues that reliable end-to-end graph data analytics cannot be achieved by retrieval- or code-generation-centric LLM agents alone. Although large language models (LLMs)…
AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence
Geunbin Yu
As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminish…