标题： 从生成到归属：流媒体时代后的音乐AI代理架构 显示英文标题

标题： From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era

摘要： 生成式人工智能正在重塑音乐创作，但其快速增长暴露了在归属、权利管理和经济模式方面的结构性缺口。 与过去从现场表演到录音、下载和流媒体的媒体转变不同，人工智能改变了音乐的整个生命周期，消除了创作、分发和变现之间的界限。 然而，现有的流媒体系统，其版税流动不透明且集中，无法应对人工智能驱动生产规模和复杂性的挑战。 我们提出了一种基于内容的音乐人工智能代理架构，通过块级检索和代理协调，将归属直接嵌入创作流程中。 该系统设计用于迭代和基于会话的交互，将音乐组织成细粒度的组件（块），存储在块数据库中；每次使用都会触发一个归属层事件，实现透明的来源追溯和实时结算。 该框架将人工智能从一种生成工具重新定位为公平人工智能媒体平台的基础设施。 通过实现细粒度归属、公平补偿和参与式互动，它指向了一个后流媒体范式，在这个范式中，音乐不再作为静态目录，而是作为协作和适应性生态系统发挥作用。 显示英文摘要

摘要： Generative AI is reshaping music creation, but its rapid growth exposes structural gaps in attribution, rights management, and economic models. Unlike past media shifts, from live performance to recordings, downloads, and streaming, AI transforms the entire lifecycle of music, collapsing boundaries between creation, distribution, and monetization. However, existing streaming systems, with opaque and concentrated royalty flows, are ill-equipped to handle the scale and complexity of AI-driven production. We propose a content-based Music AI Agent architecture that embeds attribution directly into the creative workflow through block-level retrieval and agentic orchestration. Designed for iterative, session-based interaction, the system organizes music into granular components (Blocks) stored in BlockDB; each use triggers an Attribution Layer event for transparent provenance and real-time settlement. This framework reframes AI from a generative tool into infrastructure for a Fair AI Media Platform. By enabling fine-grained attribution, equitable compensation, and participatory engagement, it points toward a post-streaming paradigm where music functions not as a static catalog but as a collaborative and adaptive ecosystem.