AgenticPay：面向买卖交易的多智能体大语言模型协商系统

摘要

基于大语言模型（LLM）的智能体正被日益期望能自主进行协商、协调与交易，然而现有基准测试缺乏评估多智能体间语言驱动经济互动的系统性场景。我们推出AgenticPay——一个面向自然语言驱动的多智能体买卖谈判的基准测试与仿真框架。该框架模拟了买卖双方拥有私有约束和产品相关估值的市场环境，要求通过多轮语言协商（而非单纯数字竞价）达成协议。该框架支持涵盖双边议价到多对多市场的110余种任务类型，配备结构化行动提取机制及可行性、效率与福利等量化指标。对前沿专有及开源权重LLM的测试表明，现有模型在谈判性能上存在显著差距，并凸显出长程战略推理的挑战，由此确立AgenticPay作为研究智能体商业与语言化市场交互的基础平台。代码与数据集详见：https://github.com/SafeRL-Lab/AgenticPay。

English

Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation framework for multi-agent buyer-seller negotiation driven by natural language. AgenticPay models markets in which buyers and sellers possess private constraints and product-dependent valuations, and must reach agreements through multi-round linguistic negotiation rather than numeric bidding alone. The framework supports a diverse suite of over 110 tasks ranging from bilateral bargaining to many-to-many markets, with structured action extraction and metrics for feasibility, efficiency, and welfare. Benchmarking state-of-the-art proprietary and open-weight LLMs reveals substantial gaps in negotiation performance and highlights challenges in long-horizon strategic reasoning, establishing AgenticPay as a foundation for studying agentic commerce and language-based market interaction. Code and dataset are available at the link: https://github.com/SafeRL-Lab/AgenticPay.

AgenticPay：面向买卖交易的多智能体大语言模型协商系统

AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions

摘要

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