Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionali...
Jia-Liang Huang, Hong-Xuan Tang, Jing-Chang Chen et al.· 3 citations
This work proposes the Cluster-Ranked Identifier (CRID), which decouples DocID into semantic clustering and business-value ranking, yielding collision-free identifiers that support incremental updates via intra-cluster reranking.
Gui Ling, Zhihong Chen, Yu Li et al.· arXiv.org· 2 citations
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains...
Dan Ou, Gui Ling, Haokai Wan et al.· 0 citations
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