AI 中文总结
研究为智能商业提出可验证全球事件时间线，由规范事件模式等四组件构成，还引入欺诈标记和数据集谱系模型。原型实现实证结果显示，其在处理事件速度、验证时间、证明大小及验证性能上有优势。
AI 中文摘要
诸如AP2和ACP之类的智能商业协议定义了安全的智能体发起交易的机制，但未提供跨异构域的可互操作、防篡改的可审计性或事件的可验证时间排序。本文通过为智能商业提出一个可验证的全球事件时间线来解决这些差距，该时间线由四个核心组件构建而成：强制确定性序列化的规范事件模式、确保可重复排序而不依赖同步时钟的确定性批处理形成、提供对数成本包含证明的基于默克尔树的只追加承诺以及建立防篡改时间主干的区块链锚定。在此基础上，我们引入了一个加密签名的欺诈标记，通过不可伪造的溯源链将风险标签与锚定证据绑定，以及一个数据集谱系模型，实现可重复、防篡改的人工智能训练管道。原型实现的实证结果表明：默克尔树在47毫秒内处理50,000个事件；无论批大小如何，端到端验证在0.013毫秒内完成；包含证明大小从1,000个事件时的320字节对数增长到50,000个事件时的512字节；在50,000个事件时，基于默克尔树的验证比线性扫描性能优14.4倍。
英文摘要
Agentic commerce protocols such as AP2 and ACP define mechanisms for secure agent-initiated transactions but do not provide interoperable, tamper-evident auditability or verifiable temporal ordering of events across heterogeneous domains. This paper addresses these gaps by proposing a verifiable global event timeline for agentic commerce, constructed from four core components: canonical event schemas that enforce deterministic serialization, deterministic batch formation ensuring reproducible ordering without reliance on synchronized clocks, Merkle-based append-only commitments providing logarithmic-cost inclusion proofs, and blockchain anchoring establishing a tamper-evident temporal backbone. Building on this infrastructure, we introduce a cryptographically signed fraud marker that binds risk labels to anchored evidence through an unforgeable provenance chain, and a dataset lineage model enabling reproducible, tamper-evident AI training pipelines. Empirical results from a prototype implementation demonstrate: Merkle tree construction processes 50,000 events in 47 milliseconds; end-to-end verification completes in under 0.013 milliseconds regardless of batch size; inclusion proof sizes grow logarithmically from 320 bytes at 1,000 events to 512 bytes at 50,000 events; and Merkle-based verification outperforms linear scan by 14.4x at 50,000 events.
Comments18 pages, 4 tables, 1 figure. Preprint also available on Zenodo: https://doi.org/10.5281/zenodo.19060285
