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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
TL;DR
AI-driven music production raises attribution and accounting demands that existing streaming systems, with opaque and concentrated royalty flows, are not designed to meet. The paper proposes a Music AI Agent architecture that retrieves musical Blocks and records their use within an interactive creative workflow. It presents this approach as a basis for traceable credit and compensation in a participatory post-streaming ecosystem.
Problem
AI music raises demand for real-time, fine-grained attribution, while streaming royalties remain opaque and concentrated and metadata fragmentation complicates accounting.
Source: §1 · p. 2Method
Results
Takeaways & Limitations
Abstract
from arXiv · showhide
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.
1 Introduction
AI-driven music creates demand for real-time, fine-grained attribution, while opaque streaming royalties and fragmented metadata leave contributions difficult to recognize and compensate. The paper proposes a Music AI Agent architecture that records Block-level usage and links creative activity to provenance and royalty settlement.
1 Introduction
- AI music creates demand for real-time, fine-grained attribution and accounting frameworks that existing models cannot yet provide.Source: §1 · p. 2
- Opaque, concentrated streaming royalties and fragmented metadata risk magnifying inequities as AI scales music production.The passage highlights risks to independent artists and cultural communities from limited recognition or compensation.Source: §1 · p. 2
- The paper frames its vision as a post-streaming ecosystem where music is adaptive, participatory, and collaborative rather than a static catalog.Source: §1 · p. 2
- The proposed Music AI Agent combines content-based RAG, generative models, and agent orchestration to record Block-level usage and link creation to provenance and real-time royalty settlement.Blocks are granular musical components such as stems or structural segments.Source: §1 · p. 2
2 Background
The background traces music AI from generative models and services to content-based retrieval and agent architectures. It presents component-level databases and orchestration as routes toward interactive creation, attribution, and contribution-proportional revenue sharing.
2 Background
- The paper situates its approach within efforts to extend language-domain LLM, RAG, and agent paradigms to media-content systems, particularly music.Source: §2 · p. 2
2.1 Generative Model
- Music audio generation spans transformer-, diffusion-, and flow-matching-based models, while commercial services vary in generation capabilities and revenue-sharing approaches.The passage notes that some full-song services lack training-data and revenue-sharing transparency, while current revenue-sharing models remain rudimentary.Source: §2.1 · p. 2
2.2 Content-based RAG
- Content-based RAG retrieves media components from databases; in music, sample retrieval and recombination can support explicit reference tracking, attribution, and revenue sharing.The passage maps indexing and parsing to Music Information Retrieval (MIR), followed by retrieval and generative models.Source: §2.2 · pp. 2–3
- Media-content RAG extends language-domain RAG by grounding generation in databases of component blocks across modalities.Source: §2.2 · p. 2
2.3 AI Agent Architecture containing Media Content
- Agent orchestration can combine media-content memory databases with tools for generating, isolating, and manipulating musical components, supporting attribution and contribution-proportional revenue sharing.Earlier Music AI Agents integrated MIR techniques, but remained algorithmically toolset-driven.Source: §2.3 · p. 3
3 Content-based Music AI Agent for Integrated Attribution
The proposed architecture combines artist-contributed Blocks in BlockDB, an interactive agent with generative tools, and a real-time Attribution Layer. In an iterative session, retrieved Blocks condition new layers and trigger royalty events tied to their original creators.
3 Content-based Music AI Agent for Integrated Attribution
- The architecture has three pillars: artist-contributed media in BlockDB, an interactive Music AI Agent with tools, and a real-time Attribution Layer.Source: §3 · p. 3
- Artists’ tracks are analyzed, separated into Blocks, and indexed with musical properties, descriptive embeddings, and creator records; stems and samples can also supplement the database.The passage says stems and samples complement decomposition to address copyright frameworks not covered by that process alone.Source: §3 · p. 3
- In each session turn, the agent retrieves relevant Blocks to condition new layers, and each Block’s use triggers an attribution event linked to its original creators.The Attribution Layer aggregates session events and allocates royalties under transparent rules, reflecting granular use rather than market-share approximations.Source: §3 · pp. 3–4
4 Discussion and Conclusion
The conclusion presents attribution-centered Music AI Agents as a structural alternative to streaming, with implications for creative practice, auditable credit, economic models, rights infrastructure, and post-streaming interaction. These are proposed directions for an adaptive and collaborative music ecosystem.
4 Discussion and Conclusion
- Attribution-centered Music AI Agents are presented as a structural alternative to the existing streaming paradigm and a signal of next-generation AI media platforms.Source: §4 · p. 4
Transformation of Creative Practice
AI Attribution
- Component-level attribution creates verifiable, auditable records that replace aggregate, opaque streaming accounting with traceable flows of credit and compensation.The passage says each creative action becomes a documented contribution, strengthening accountability.Source: §4 · p. 4
Economic Models and Fan Participation
- Granular contribution records support usage-based micropayments beyond pooled subscription royalties, while fans can become remixing, co-creating, and artist-supporting stakeholders.Source: §4 · p. 4
Infrastructure and Copyright Integration
- Attribution event logging is described as compatible with registries such as ISRC, ISWC, and IPI, complementing existing rights-management systems.The passage connects transparent contribution records and automated settlements to a Fair AI Media Platform.Source: §4 · p. 4
Post-Streaming Outlook
- The post-streaming outlook is an agentic ecosystem where creation, consumption, and settlement converge in real time under transparent rules.The passage casts AI as a mediator of attribution, evaluation, and economic interaction.Source: §4 · p. 4
A.1 System Overview
The system combines a modular music database, generative and analytical tools, session-based multi-agent orchestration, and a real-time Attribution Layer. Its iterative, component-based workflow is designed for interactive generation, granular control, and attribution.
A.1 System Overview
- The architecture integrates BlockDB, generative and analytical tools, session-based multi-agent orchestration, and an automated Attribution Layer for royalty distribution.These are its four core pillars.Source: §A.1 · p. 8
- The agent interprets user intent and manages task execution in an iterative, human-in-the-loop creative workflow.Users build music through sessions rather than a single prompt for an entire track.Source: §A.1 · p. 8
- Music is assembled from components such as rhythm and melody, allowing users to generate individual parts as smaller, constrained tasks.The paper contrasts this modular approach with monolithic models that generate entire tracks from one prompt.Source: §A.1 · p. 8
- The session-based approach is the foundation of the system’s attribution mechanism.Source: §A.1 · p. 8
A.2.1 The Block as a Fundamental Unit
The system treats a Block as a discrete audio segment of one stem within a composition’s structural section. Timbral and temporal categories organize Blocks so the agent can query musical ideas in compositional context.
A.2.1 The Block as a Fundamental Unit
- A Block is a discrete audio segment from one instrumental or vocal stem, situated within a specific structural section of a composition.Source: §A.2.1 · p. 8
- Blocks are categorized along timbral and temporal axes, covering instrumental role and typical song position.Examples of song positions include intro, verse, and chorus; examples of instrumental roles include drums, bass, piano, and synth.Source: §A.2.1 · p. 8
- The dual-axis structure acts as a compositional grammar, making context-specific musical concepts queryable rather than relying on timbre matching alone.The passage gives “a highfrequency melodic element for a chorus” as an example of a queryable object.Source: §A.2.1 · p. 8
A.2.2 Enabling Retrieval-Augmented Generation (RAG) and Attribution
Each Block combines audio with metadata, including musical properties, captions, multimodal embeddings, and its creator. Retrieval can condition generation or provide a symbolic melodic condition, and each retrieval triggers attribution to the original creator.
A.2.2 Enabling Retrieval-Augmented Generation (RAG) and Attribution
- Each Block stores audio alongside musical properties, descriptive captions, multimodal embeddings, and a record of its creator.Musical properties include timbral and temporal type, BPM, key, and bar length.Source: §A.2.2 · p. 8
- Retrieved Block audio can directly condition generation, or the Block can be transcribed into a symbolic format such as MIDI to provide a melodic condition.Source: §A.2.2 · p. 8
- Retrieving a Block triggers attribution to its original creator, supporting attribution tracking throughout the creative process.Source: §A.2.2 · p. 8
A.2.3 Artist Submissions and Content Ingestion
BlockDB accepts both finished tracks for decomposition and standalone stems or samples. The resulting components enter a systematic ingestion process, with links to original works supporting attribution.
A.2.3 Artist Submissions and Content Ingestion
- Artists can submit finished tracks for decomposition into timbral stems and temporal sections, which are indexed as attributable Blocks linked to the original work.Examples include vocals, drums, and bass as stems, and verse and chorus as sections.Source: §A.2.3 · p. 8
- Artists and producers can also contribute standalone stems and samples directly.Source: §A.2.3 · p. 8
- The direct-contribution pathway is intended to incorporate pre-cleared components and address complex copyright frameworks; all submissions undergo systematic ingestion into the library.Source: §A.2.3 · p. 9
A.3 A Unified Framework for Generative and Analytical Tools
The section presents a modular music-agent workflow: specialized tools handle diverse musical tasks and inputs, while agents plan, retrieve attributable Blocks, execute tasks, and support iterative user interaction.
A.3.1 A Diverse and Extensible Toolset
- The modular toolset combines generation tasks such as stem and voice synthesis with music-understanding tools, and new tools can be added without changing the core architecture.The listed understanding tools include source separation, MIDI transcription, classification, captioning, and structure analysis.Source: §A.3.1 · p. 9
A.3.2 Generalized Conditional Framework
- The agent chains tools across text, symbolic music, and audio; for example, lyrics can produce MIDI that is then used with lyrics to synthesize vocals.Text includes prompts and lyrics, symbolic music includes MIDI, and audio includes raw waveforms.Source: §A.3.2 · p. 9
A.4 The Music AI Agent: An Interactive and Sequential Workflow
- The multi-agent architecture coordinates user interaction, resource retrieval, and tool execution in a sequential, iterative, human-in-the-loop workflow.Figure 4 depicts the workflow from prompt and audio analysis through Block retrieval to generated stems.Source: §A.4 · p. 9, Figure 4 · p. 10
1. Intent Agent: Devising a Task Plan
- The Intent Agent turns a high-level creative goal into a dependency graph specifying which tools to use, in what sequence, and how data flows between them.For the example request, the plan transcribes scratch melody audio, retrieves a synth sound, then supplies MIDI, drum-track context, and retrieved prompt audio to stem generation.Source: §1 · pp. 9–10
2. Query Agent: Retrieving Attributable Assets
- The Query Agent converts plan requirements into BlockDB queries, and retrieving a Block immediately sends a usage event to the Attribution Layer.In the example, it finds a suitable audio Block to serve as prompt audio for stem generation.Source: §2 · p. 10
3. Task Execution and Session-based Iterative Loop
- After gathering conditions, the system executes tools in sequence, returns generated output with a natural-language explanation, and lets users refine or extend the result in later turns.The output of one tool, such as transcription MIDI, can become the next tool’s input.Source: §3 · p. 10
A.5 Attribution Layer
- Each retrieved Block immediately triggers an attribution event that logs its original creator, and the artist dashboard reports Block use and revenue.The system is designed to track and assign credit at the moment a Block is used.Source: §A.5 · p. 10, Figure 5 · p. 11
A.6 System Extensibility via Model Context Protocol (MCP)
- Through Model Context Protocol (MCP), the platform can interface with external environments such as DAWs for an extended creative workflow.Source: §A.6 · p. 11