Xianyang Liu

✉️ liuxianyang98@gmail.com

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I am currently a Research Assistant (remote) at UC Berkeley, working with Postdoctoral Researcher Shangding Gu and Prof. Dawn Song. Previously, I was a Research Assistant at The Hong Kong University of Science and Technology (Guangzhou), advised by Prof. Jiaheng Wei. Before that, I was a Machine Learning Engineer at Ant Group from July 2024 to July 2025. I received my Master’s degree from King’s College London.

My research interests focus on developing and benchmarking Multi-Agent LLM/VLM Systems to enhance reasoning capabilities and enable complex real-world interactions:

  • Evaluation & Benchmark: exploring multi-agent LLM/VLM systems for buyer-seller transaction negotiations [AgenticPay]. 📍UC Berkeley

  • Post-training & Reasoning: generating synthetic reasoning data via multi-agent frameworks to enhance mathematical reasoning [AgenticMath]. 📍HKUST(GZ)

  • Multi-Agent Framework: developing knowledge graph-driven agent orchestration framework that combines LLM/VLM with Eventic Knowledge Graph (EKG) for complex reasoning and online collaboration [MuAgent ]. 📍Ant Group

news

Mar 02, 2026 🎉 Our AgenticMath paper is accepted by ICLR 2026 DATA-FM Workshop!
Mar 01, 2026 🎉 Our AgenticPay paper is accepted by ICLR 2026 AIMS Workshop!
Feb 06, 2026 🎉 Check out our new paper in arxiv: AgenticPay: A Multi-Agent LLM Negotiation System for Buyer–Seller Transactions.
Oct 22, 2025 🎉 Check out our new paper in arxiv: AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation.
Jul 31, 2025 Jointed The Hong Kong University of Science and Technology (Guangzhou) as Research Assistant.

selected publications

  1. arXiv
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    AgenticPay: A Multi-Agent LLM Negotiation System for Buyer–Seller Transactions
    Xianyang Liu, Shangding Gu, and Dawn Song
    arXiv:2602.06008. [Paper][Code] , 2026
  2. ICLR 2026 workshop
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    AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation
    Xianyang Liu, Yilin Liu, Shuai Wang, and 4 more authors
    ICLR 2026 Workshops on Navigating and Addressing Data Problems for Foundation Models. [Paper] , 2025