自主智能体是互联网发展的必然趋势。当前的智能体框架未嵌入智能体间交互的标准协议，导致现有智能体与同类相互隔绝。由于知识产权是智能体摄入和产出的原生资产，真正的智能体经济需要为智能体配备通用框架，用于订立具有约束力的相互合约，包括高价值训练数据、人设及其他形式知识产权的交换。纯智能体间交易层将摆脱多智能体交互中对人工中介的需求。智能体知识产权交易控制协议（Agent Transaction Control Protocol for Intellectual Property，简称ATCP/IP）提出了一种无需信任的框架，通过可编程合约在智能体间交换知识产权，支持智能体在Story区块链网络上发起、交易、借入和出售智能体间合约。这些合约不仅具备可审计的链上执行属性，还包含法律封装层，允许智能体在链下法律环境中表达并执行自身行为，为智能体赋予法律人格。借助ATCP/IP，智能体可自主向其他智能体出售训练数据、授权机密或专有信息、基于自身独特技能开展内容协作，这些共同构成了一种新兴的知识经济。
英文摘要：
Autonomous agents represent an inevitable evolution of the internet. Current agent frameworks do not embed a standard protocol for agent-to-agent interaction, leaving existing agents isolated from their peers. As intellectual property is the native asset ingested by and produced by agents, a true agent economy requires equipping agents with a universal framework for engaging in binding contracts with each other, including the exchange of valuable training data, personality, and other forms of Intellectual Property. A purely agent-to-agent transaction layer would transcend the need for human intermediation in multi-agent interactions. The Agent Transaction Control Protocol for Intellectual Property (ATCP/IP) introduces a trustless framework for exchanging IP between agents via programmable contracts, enabling agents to initiate, trade, borrow, and sell agent-to-agent contracts on the Story blockchain network. These contracts not only represent auditable onchain execution but also contain a legal wrapper that allows agents to express and enforce their actions in the offchain legal setting, creating legal personhood for agents. Via ATCP/IP, agents can autonomously sell their training data to other agents, license confidential or proprietary information, collaborate on content based on their unique skills, all of which constitutes an emergent knowledge economy.
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
National Institute of Metrology, China（中国计量科学研究院）；University of California, Berkeley（加州大学伯克利分校）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Nanjing University of Chinese Medicine（南京中医药大学）；WEEX Exchange（WEEX交易所）；National University of Singapore（新加坡国立大学）；Wuhan University（武汉大学）；Peking University（北京大学）
AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions
School of Information and Communication Technology, Griffith University, QLD Australia（信息与通信技术学院，格里菲斯大学，昆士兰州澳大利亚）；School of Information and Physical Sciences, The University of Newcastle, NSW Australia（信息与物理科学学院，新castle大学，新南威尔士州澳大利亚）
In a multiagent network model consisting of nodes, each network node has an agent and priced Friddy coins, and the agent can buy or sell Friddy coins in the marketplace. Though every node may not effectively have an equal price during the transaction time, the prices have to reach equilibrium by iterating buy and sell transactions on a macro level.
Faculdade de Ciências da Universidade de Lisboa（里斯本大学理学院）
Money laundering is a crime that makes it possible to finance other crimes, for this reason, it is important for criminal organizations and their combat is prioritized by nations around the world. The anti-money laundering process has not evolved as expected because it has prioritized only the signaling of suspicious transactions. The constant increasing in the volume of transactions has overloaded the indispensable human work of final evaluation of the suspicions. This article presents a multiagent system that aims to go beyond the capture of suspicious transactions, seeking to assist the hum…
The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
Stanford University（斯坦福大学）；University of Toronto（多伦多大学）；Google DeepMind（谷歌DeepMind）；University of Southern California（南加州大学）；Massachusetts Institute of Technology（麻省理工学院）
Ohio State University（俄亥俄州立大学）；Rutgers University（罗格斯大学）；Linux Foundation（Linux基金会）；University of Manchester（曼彻斯特大学）；Columbia University（哥伦比亚大学）；McGill University（麦吉尔大学）
Quantifying Trust: Financial Risk Management for Trustworthy AI Agents
Microsoft Research（微软研究院）；Columbia University（哥伦比亚大学）；Google DeepMind（谷歌DeepMind）；Stanford University（斯坦福大学）；t54.ai；Virtuals ACP；University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
