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Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
Fabio Morreale, Wiebke Hutiri, Joan Serrà, Alice Xiang, Yuki Mitsufuji
TL;DR
Generative music systems rely on attribution mechanisms that leave uncertainty about which works influenced outputs, complicating transparent compensation. The paper separates training-time from inference-time attribution and embeds inference attribution through user-selected reference songs. It argues this makes the connection between those songs and generated outputs certain, while acknowledging unresolved training-time attribution and dataset constraints.
Problem
Retrospective attribution methods rely on assumptions that leave uncertainty about which training data influenced a specific output.
Source: §2 · p. 2Method
The framework separates training-time from inference-time attribution and conditions generation on user-selected reference songs from an inference catalogue.
Source: §3.1 · p. 3, §3.2 · p. 4, §4.1 · p. 5Results
The paper proposes that direct conditioning makes the inference data contributing to a generation certain rather than speculative.
Source: §3.2 · p. 4, §8 · p. 11Takeaways & Limitations
Purpose-based compensation can distinguish personal and educational uses from commercial applications while giving artists control over generated music using their creations.
Source: §5.2 · p. 9The paper focuses on inference-time attribution because training-time attribution remains epistemically and technically uncertain.
Source: §7 · p. 11
Abstract
from arXiv · showhide
The rise of AI-generated music is diluting royalty pools and revealing structural flaws in existing remuneration frameworks, challenging the well-established artist compensation systems in the music industry. Existing compensation solutions, such as piecemeal licensing agreements, lack scalability and technical rigour, while current data attribution mechanisms provide only uncertain estimates and are rarely implemented in practice. This paper introduces a framework for a generative music infrastructure centred on direct attribution, transparent royalty distribution, and granular control for artists and rights' holders. We distinguish ontologically between the training set and the inference set, which allows us to propose two complementary forms of attribution: training-time attribution and inference-time attribution. We here favour inference-time attribution, as it enables direct, verifiable compensation whenever an artist's catalogue is used to condition a generated output. Besides, users benefit from the ability to condition generations on specific songs and receive transparent information about attribution and permitted usage. Our approach offers an ethical and practical solution to the pressing need for robust compensation mechanisms in the era of AI-generated music, ensuring that provenance and fairness are embedded at the core of generative systems.
1 Introduction
The paper identifies uncertain retrospective attribution and the lack of reliable provenance as barriers to fair compensation in generative music. It proposes attribution embedded in system design, distinguishing training-time from inference-time attribution and outlining an implementation infrastructure.
1 Introduction
- Large-scale scraping often proceeds without creator consent, transparency, or remuneration, leaving most creators unaware of or uncompensated for their work’s use.The paper describes the consequences as unconsented and unpaid labour.Source: §1 · p. 1
- Music’s established rights-management and royalty systems motivate the paper’s focus, while the proposed solution could also be adopted in other domains.Source: §1 · p. 1
- Retrospective attribution methods rely on similarity or model introspection, but their assumptions make provenance probabilistic or speculative and compensation difficult to establish.Similarity does not establish causation, and model internals may not be interpretable in human terms.Source: §1 · p. 2
- Attribution-by-design embeds attribution in generative-system architecture, distinguishes training from inference attribution, and outlines inference attribution principles and sociotechnical infrastructure.The proposed infrastructure encompasses user interaction, verification, generation, and compensation.Source: §1 · p. 2
2 Background: Attribution in Generative Modelling
Existing generative-model attribution methods are grouped as corroborative or predictive, but each relies on assumptions that limit certainty about which training data influenced an output.
2 Background: Attribution in Generative Modelling
- Corroborative methods estimate influence from output–training-data similarity, whereas predictive methods examine individual data points’ effects on model weights or gradients [Wang et al., 2024, Ko et al., 2024; Zheng et al., 2024, Lin et al., 2024].Source: §2 · p. 2
- Similarity does not prove causal influence, while predictive attribution assumes model reasoning can be interpreted by humans, leaving the source of a generation uncertain.Source: §2 · p. 2
- The discussion sets aside watermark-based corroborative attribution because recent research reports strong limitations in audio watermarking [Wen et al., 2025, O’Reilly et al., 2025, Özer et al., 2025].Source: §2 · p. 2
- Users cannot verify whether payments reach the creators whose materials influenced an output, and artists cannot ascertain whether their works were appropriately compensated.Source: §2 · pp. 2–3
3 Attribution-by-design
Attribution-by-design embeds attribution into generative music systems, distinguishing model-formation attribution from attribution to a specific generation and centring the framework on inference-time attribution.
3.1 Training vs. Inference Dataset and Attribution
- The framework separates training data that shapes model parameters from an inference catalogue users draw on at generation time; these sets may overlap or remain distinct.It also distinguishes training-time attribution to model formation from inference-time attribution to a specific output, arguing that they require different epistemological approaches.Source: §3.1 · pp. 3–4
- Because training-time attribution remains epistemologically and technically intractable, the framework favours inference-time attribution for direct, verifiable tracing of reference material to generated outputs.It seeks to minimise the role of training data in processing inference material, whose contribution may not be attributable in principle.Source: §3.1 · p. 4
3.2 Framework
- Users select specific catalogue songs to condition generation, creating a direct connection that makes each output’s reference sources certain rather than speculative.The framework proposes transparent, enforceable creator compensation based on contracts with the service provider.Source: §3.2 · p. 4
- The framework permits contracts requiring licensing for training and/or inference, attribution royalties, and purpose limitations on specified uses.Source: §3.2 · p. 4
- It identifies contributing sources during generation and provides users and rights holders with immediate, transparent attribution relationships for specific outputs.Source: §3.2 · p. 4
- The process comprises user interaction, verification of artists’ consent conditions, generation conditioned on referenced songs, and compensation of their creators.Source: §3.2 · p. 4
4 User Interaction
The interaction design lets users choose reference songs directly or find them through mediated selection, with control ranging from whole songs to specific musical elements, audio, or segments. Retrieval can help users navigate the inference catalogue, while the user retains the final choice of reference songs.
4.1 Unmediated (Direct) Selection
- Direct selection makes attribution unmediated: users choose catalogue songs to inspire generation, so no ad-hoc attribution calculation is needed.Users can select songs to influence the whole output or a specific part.Source: §4.1 · p. 5
4.1.1 Levels of Control
- Control can target whole songs, selected parameters, audio supplied by the user, or specific temporal segments.Parameter-level input can assign different song references to elements such as guitar and voice; audio-level input can apply reference-derived attributes to a user track.Source: §4.1.1 · pp. 5–6
4.1.2 Selection Mechanisms
- Direct-selection interfaces can use a hierarchy, text search, or recommendations; recommendations rank and filter songs and are therefore mediated.Hierarchies can organize songs by genre, artist, and album, among other structures.Source: §4.1.2 · p. 6
4.2 Mediated (Indirect) Selection
- Mediated selection suits users who can describe their intentions but may lack the musical knowledge or cultural references to choose a song directly.Additional songs can also be selected randomly or automatically for unspecified parameters, with the system identifying those references to users.Source: §4.2 · pp. 6–7
4.2.1 Retrieval model
- A retrieval model can map text and audio into a joint embedding space to find inference-set songs relevant to a user's textual description.Examples include MuLan and CLAP.Source: §4.2.1 · p. 7
- The retrieval model ranks songs by prompt similarity and presents the top K for preview, but users choose the final references.Similarity helps navigate candidates; it does not determine attribution.Source: §4.2.1 · p. 7
4.2.2 User Prompts
- Text prompts allow nuanced requests but may be interpreted unreliably; predefined categories can reduce ambiguity and improve retrieval and generation quality.Categories can also standardize requests for mapping to catalogue metadata and rights-management requirements.Source: §4.2.2 · p. 8
5 Verification and Compensation
The framework checks reference songs against rights holders’ consent and distribution conditions before generation. It combines granular, revocable permissions with use-specific policies and transparent records, and can redirect users toward compliant alternatives when verification fails.
5 Verification and Compensation
- Generation proceeds only when rights holders have opted in to use selected reference songs for a defined purpose, with verification checking consent and rights-management conditions.The process is intended to align generative-inference compensation with established practices such as sampling.Source: §5 · p. 8
5.1 Nuanced Consent
- Consent is granular rather than all-or-nothing: permissions can apply to specific songs or song aspects, default to opt-out, and be revoked.Artists, rights holders, or representatives can update permissions.Source: §5.1 · p. 8
5.2 Distribution Policies
- Distribution policies distinguish private, non-commercial, and commercial uses, and verification clears a use only when the reference song permits it.Commercial use can require higher compensation than private or preview use; purpose-based compensation is intended to preserve access for personal and educational uses while compensating rights holders for commercial applications.Source: §5.2 · pp. 8–9
5.3 Transparency as Precondition for Compensation
- Attribution and verification outcomes must be logged so rights holders can validate compensation, while output metadata can support tracing on digital platforms.Generated outputs can be associated with rights holders because reference-song contributions are determined before generation.Source: §5.3 · p. 9
5.4 Recommendation and Discovery as Alternatives to Verification
- When verification fails, recommendation and discovery interactions can guide users toward permitted inputs or other music that fits their intent.These interactions can continue over multiple turns until user intent and consent and licensing conditions converge.Source: §5.4 · p. 9
6 Generation
The generation process conditions synthesis on reference songs, with users able to assign songs distinct functions. The framework treats musical dimensions as entangled and proposes training the model to learn their interactions for recombination.
6 Generation
- Reference songs are encoded into audio tokens for the generative model, and each can be paired with a textual description or flag specifying its function.For example, songs may supply mood, genre, or situation; the figure assigns mood to the top song and genre to the middle song.Source: §6 · p. 9, Figure 7 · p. 10
- Parameter-level selection is more complex because musical dimensions such as guitar style and melodic contour are often deeply entangled.Mood, genre, and situation can also overlap, so treating them as perfectly separable categories would be reductive.Source: §6 · pp. 9–10
- The framework treats partial disentanglement as a practical abstraction, provided the model learns relationships among dimensions rather than treating them as perfectly separate.Training examples could pair complementary roles from different songs or use alternate performances that vary timbre while preserving melody and harmony.Source: §6 · p. 10
- Conditioning anchors new material in reference songs so their influence can be traced to their creators, with disentanglement enabling selective use of a song’s aspects.The paper relates this approach to retrieval-augmented generation, trajectory guidance, and latent editing.Source: §6 · p. 10
7 Limitations and Future Work
The framework focuses on inference-time attribution because training-time attribution remains epistemically and technically uncertain. Its scope also faces retrieval constraints and the overlooked use of human-created data in auxiliary modules.
7 Limitations and Future Work
- The paper focuses on inference-time attribution, leaving training-time attribution for future work because robust methods remain uncertain.Interim training-time options include flat-rate compensation or predictive estimates, which remain uncertain.Source: §7 · p. 11
- Retrieval depends on the scope and diversity of training and inference data, so rare, specific, or unseen audio scenarios may be poorly served.Desired reference material may be absent from the indexed catalogue or poorly represented in the model’s embedding space.Source: §7 · p. 11
- Attribution discussions have overlooked auxiliary modules, although indispensable components such as encoders and alignment models also rely on human-created data.The authors argue an attribution-by-design system should offer compensation to creators whose data trains these modules.Source: §7 · p. 11
8 Conclusions
The conclusion presents attribution-by-design as a sociotechnical framework that separates training and inference data and hard-codes provenance for generated outputs. It also connects that provenance to artist preferences, royalty distribution, and user-facing control.
8 Conclusions
- Separating training and inference datasets distinguishes training-time attribution from inference-time attribution.The paper proposes identifying with certainty which inference data contributed to a generation, rather than estimating attribution scores.Source: §8 · p. 11
- Attribution-by-design embeds provenance in a sociotechnical architecture that can collect artists’ usage preferences and support automated royalty distribution..Source: §8 · p. 11
- Users can condition generations on songs they choose and know that part of their payment goes directly to the referenced artist..Source: §8 · p. 11
Ethical Disclosure
The ethical disclosure recognizes that generative AI may displace artists and that direct attribution may not compensate everyone. It proposes complementary support and notes environmental and resource constraints on deployment.
Ethical Disclosure
- Generative AI may displace artists, and some may not receive compensation through direct attribution.The authors suggest positive bias in the LMM and complementary public funds, collective licensing, or targeted subsidies.Source: Ethical Disclosure · pp. 11–12
- Training and operating these systems incur sustainability costs, and the authors advocate practices to offset their carbon footprint..Source: Ethical Disclosure · p. 12
- Implementation may be feasible only for organizations with sufficient financial resources and access to a large music catalogue.The authors call for research and collaboration to improve accessibility and equity across the industry.Source: Ethical Disclosure · p. 12