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

Wonil Kim, Hyeongseok Wi, Seungsoon Park, Taejun Kim, Sangeun Keum, Keunhyoung Kim, Taewan Kim, Jongmin Jung, Taehyoung Kim, Gaetan Guerrero, Mael Le Goff, Julie Po, Dongjoo Moon, Juhan Nam, Jongpil Lee

Keywords: Music AI Agent, Retrieval-Augmented Generation, Music Generation, Attribution and Copyright

TL;DR: We propose a content-based Music AI Agent framework that integrates RAG and attribution layers, enabling transparent provenance and fair revenue sharing in generative music systems.

Abstract:

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. Session example demo: https://aimusicagent.github.io/.

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Submission Number: 95