@inproceedings{chen-etal-2025-tcqa2,
    title = "{TCQA}$^2$: A Tiered Conversational {Q}{\&}{A} Agent in Gaming",
    author = "Chen, Ze  and
      Wei, Chengcheng  and
      Zheng, Jiewen  and
      He, Jiarong",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.realm-1.20/",
    pages = "289--297",
    ISBN = "979-8-89176-264-0",
    abstract = "This paper focuses on intelligent Q{\&}A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content. It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools. We propose a Tiered Conversational Q{\&}A Agent (TCQA$^2$), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses. Parallel modules in each tier cut latency via distributed tasks. Multiple retrievers and short-term memory boost multi-turn Q{\&}A. Hallucination and safety checks improve response quality. Player tags and long-term memory enable personalization. Real-world evaluations show TCQA$^2$ outperforms prompt-engineered LLMs and RAG-based agents in gaming Q{\&}A, personalized dialogue, and risk mitigation."
}