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
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first inappropriate token and either picks a substitute from the model's candidate tokens...
Lei Yang, Meng-Yin Liu, Jia Wang et al.· 0 citations
Large Language Models (LLMs) often face challenges in performing reliable multi-hop reasoning due to issues such as incomplete evidence chains and hallucinations. Incorporating knowledge graphs (KGs) can mitigate these problems, but existing approaches either suffer from suboptimal accuracy or are computationally exp...
