Paper Citation Record · LEDGER

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI

As of 10 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2507.03599.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.03599 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:09:33.863859Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:09:30.476741Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T20:09:34.388498Z

Reference resolution

68 of 68 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 47cdee41-5988-4ed8-b088-dc24782edb17 · outbound

This paper cites MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI

Reference 1

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Reference 2

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Unresolved cited work

Reference 3

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Observation 174bb687-686d-45dc-8cec-92ee115ebbc9 · outbound

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Reference 4

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This paper cites This situation underscores different levels of com- mitment across the community, particularly regarding crit- ical categories such as training data, source code, and li- censing.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI This situation underscores different levels of com- mitment across the community, particularly regarding crit- ical categories such as training data, source code, and li- censing

Reference 5

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Observation 0ca9ed7a-2e24-4d19-837b-e4f26c02626d · outbound

This paper cites Given the lack of transparency and accountabil- ity in these systems, we advocate for open models.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Given the lack of transparency and accountabil- ity in these systems, we advocate for open models

Reference 6

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Observation c3370365-4e36-437b-a852-28af42f3c8d3 · outbound

This paper cites Par- ticipants were informed about the scope and purpose of the study, as well as the intended use of the collected data.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Par- ticipants were informed about the scope and purpose of the study, as well as the intended use of the collected data

Reference 7

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 3e9b05cf-8b34-4d6b-a795-9cf344f66219 · outbound

This paper cites Computational creativ- ity and music generation systems: An introduction to the state of the art,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Computational creativ- ity and music generation systems: An introduction to the state of the art,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.952465Z

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Observation 13276796-d742-4aa7-b6a6-cc32496fdd17 · outbound

This paper cites Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:09:34.369889Z

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Observation 9c8db908-1537-44b9-9957-0c49b33f1cde · outbound

This paper cites Artificial intelligence and music: Open questions of copyright law and engineering praxis,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Artificial intelligence and music: Open questions of copyright law and engineering praxis,

Reference 11

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Observation fffc59a4-3bd1-4f02-9499-e019776eee59 · outbound

This paper cites The ethical implications of generative au- dio models: A systematic literature review,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The ethical implications of generative au- dio models: A systematic literature review,

Reference 12

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Observation 81380d9e-f3fe-45db-a5e7-ab1c9c5f60b1 · outbound

This paper cites Transparency in music-generative ai: A systematic literature review,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Transparency in music-generative ai: A systematic literature review,

Reference 13

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Observation cfae2933-b4a2-4b49-aad2-ac94d5021a37 · outbound

This paper cites A shift in artistic prac- tices through artificial intelligence,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI A shift in artistic prac- tices through artificial intelligence,

Reference 14

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Observation 9a37c7c9-b604-44b3-8310-283a406e199a · outbound

This paper cites Ex- tracting training data from diffusion models,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Ex- tracting training data from diffusion models,

Reference 15

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Observation bca32a87-0277-417d-9e63-6be20afa5092 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Quantifying Memorization Across Neural Language Models

Reference 16

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Observation 64dfc0fa-3c6a-49ad-b53c-6b3dc97173dc · outbound

This paper cites Generation or repli- cation: Auscultating audio latent diffusion models,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Generation or repli- cation: Auscultating audio latent diffusion models,

Reference 17

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Observation ac495392-64a9-491a-918f-a4cf2c1b2660 · outbound

This paper cites On reproducible ai: Towards reproducible research, open science, and digital scholarship in ai publications,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI On reproducible ai: Towards reproducible research, open science, and digital scholarship in ai publications,

Reference 18

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Observation ca5433d1-d6cf-45d7-aecb-7275dad98c91 · outbound

This paper cites Rethinking open source generative ai: open-washing and the eu ai act,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Rethinking open source generative ai: open-washing and the eu ai act,

Reference 19

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Observation 6d3ae638-94c3-4e91-9f38-0bd3a2b1d21f · outbound

This paper cites Why open-source generative ai models are an ethical way forward for science,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Why open-source generative ai models are an ethical way forward for science,

Reference 20

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Observation 07fea01b-b28e-4f46-b5e7-6519410f7e8f · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI OLMo: Accelerating the Science of Language Models

Reference 21

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Observation 46ed25c3-63d6-46f5-8006-aa12688dd4b4 · outbound

This paper cites The gradient of generative ai release: Methods and considerations,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The gradient of generative ai release: Methods and considerations,

Reference 22

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Observation d28ed27c-28ba-4199-8274-76d6e3937da6 · outbound

This paper cites FactSheets: Increasing trust in AI services through supplier’s declarations of conformity,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI FactSheets: Increasing trust in AI services through supplier’s declarations of conformity,

Reference 23

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Observation af9dfaaf-4798-424c-8250-4460ada68824 · outbound

This paper cites Datasheets for datasets,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Datasheets for datasets,

Reference 24

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Observation 35ecb293-f155-4c1b-b493-a37fa6d01f1c · outbound

This paper cites The dataset nutrition label,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The dataset nutrition label,

Reference 25

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This paper cites Model cards for model reporting,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Model cards for model reporting,

Reference 26

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Observation 93213459-0c2c-4144-9462-c5bb7458b81a · outbound

This paper cites Between Copyright and Computer Science: The Law and Ethics of Generative AI.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Between Copyright and Computer Science: The Law and Ethics of Generative AI

Reference 27

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This paper cites The Open Source AI Definition – version 1.0,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The Open Source AI Definition – version 1.0,

Reference 28

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This paper cites Open-source AI must reveal its training data, per new OSI definition,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Open-source AI must reveal its training data, per new OSI definition,

Reference 29

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The Open Source AI Definition: What the Critics Say,

Reference 30

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The Open Source AI Definition is a step forward in defining openness in AI,

Reference 31

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This paper cites Ai art and its impact on artists,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Ai art and its impact on artists,

Reference 32

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This paper cites Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act),.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act),

Reference 33

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This paper cites Article 53: Obligations for Providers of General-Purpose AI Models (Artificial Intelligence Act),.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Article 53: Obligations for Providers of General-Purpose AI Models (Artificial Intelligence Act),

Reference 34

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI General-Purpose AI Models in the AI Act - Questions & Answers,

Reference 35

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This paper cites Second Draft of the General- Purpose AI Code of Practice,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Second Draft of the General- Purpose AI Code of Practice,

Reference 36

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Opening up chatgpt: Tracking openness, trans- parency, and accountability in instruction-tuned text generators,

Reference 37

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Ai documentation: A path to accountability,

Reference 38

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This paper cites Patterns and anti-patterns, principles and pitfalls: accountability and transparency in ai,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Patterns and anti-patterns, principles and pitfalls: accountability and transparency in ai,

Reference 39

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Artificial intelligence and the value of transparency,

Reference 40

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This paper cites Accountability in artificial intelligence: what it is and how it works,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Accountability in artificial intelligence: what it is and how it works,

Reference 41

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The Foundation Model Transparency Index

Reference 42

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This paper cites The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence

Reference 43

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Risks and Opportunities of Open-Source Generative AI

Reference 44

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Near to Mid-term Risks and Opportunities of Open-Source Generative AI

Reference 45

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI GANSynth: Adversarial neural audio synthesis,

Reference 46

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This paper cites Jukebox: A Generative Model for Music.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Jukebox: A Generative Model for Music

Reference 47

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MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI RAVE: A variational autoencoder for fast and high-quality neural audio synthesis

Reference 48

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Observation 48baa3e2-fd46-472e-805e-bb84b123b386 · outbound

This paper cites Musika! fast infinite wave- form music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Musika! fast infinite wave- form music generation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.588088Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df3c97d4-6d6a-4031-b7aa-65e4edf87a47 · outbound

This paper cites Mo\^usai: Text-to-Music Generation with Long-Context Latent Diffusion.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Mo\^usai: Text-to-Music Generation with Long-Context Latent Diffusion

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.784073Z

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Observation 4608c60a-5c6d-4c31-8f5b-5f853d35b4f5 · outbound

This paper cites Diff-a-riff: Musical accompaniment co- creation via latent diffusion models,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Diff-a-riff: Musical accompaniment co- creation via latent diffusion models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.488075Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d26d516d-be5e-4c11-922e-166bc21d17a4 · outbound

This paper cites Simple and control- lable music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Simple and control- lable music generation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.559378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation caee7791-9627-4359-a49c-bea1403a072a · outbound

This paper cites MusicLM: Generating Music From Text.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI MusicLM: Generating Music From Text

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.798576Z

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Observation 3a8fa554-3234-4e8c-9374-2ce01fa9b7ef · outbound

This paper cites Vampnet: Music generation via masked acoustic to- ken modeling,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Vampnet: Music generation via masked acoustic to- ken modeling,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.544977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 324b0111-0b94-4dea-acab-06fa3266df6b · outbound

This paper cites Musicldm: Enhanc- ing novelty in text-to-music generation using beat- synchronous mixup strategies,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Musicldm: Enhanc- ing novelty in text-to-music generation using beat- synchronous mixup strategies,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.530875Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cb4bc677-474b-4f07-8f06-5ce1daffe3c8 · outbound

This paper cites Music controlnet: Multiple time-varying controls for music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Music controlnet: Multiple time-varying controls for music generation,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.811601Z

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Observation c0f3978c-ab5d-443c-bfe1-9135a99d1740 · outbound

This paper cites Noise2Music: Text-conditioned Music Generation with Diffusion Models.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Noise2Music: Text-conditioned Music Generation with Diffusion Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.815726Z

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Observation 78572164-0eb9-455d-89ca-376f35050135 · outbound

This paper cites Efficient neural music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Efficient neural music generation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.516881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8aafc5af-0051-41c1-abf3-352d17f09f76 · outbound

This paper cites DITTO-2: Distilled diffusion inference-time t- optimization for music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI DITTO-2: Distilled diffusion inference-time t- optimization for music generation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.502852Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b930e9a-d0dc-46dc-9a58-f7e633abf5b2 · outbound

This paper cites Joint audio and symbolic conditioning for temporally con- trolled text-to-music generation,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Joint audio and symbolic conditioning for temporally con- trolled text-to-music generation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.472797Z

Source-reported events for the cited work

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Observation 79271cda-6323-4b53-ae43-08a4a409bbc7 · outbound

This paper cites Stable Audio Open.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Stable Audio Open

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.838638Z

Source-reported events for the cited work

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Observation b36a9363-709c-478d-ae6a-af6bbd36d10b · outbound

This paper cites Auto-Encoding Variational Bayes.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Auto-Encoding Variational Bayes

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.842962Z

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Observation 6badf338-727e-4040-ba99-ee3440c7ba8d · outbound

This paper cites Generative adversarial networks,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Generative adversarial networks,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:33.847185Z

Source-reported events for the cited work

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Observation 7c581d91-9e5c-4e57-b429-31346e457ea0 · outbound

This paper cites Attention is all you need,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Attention is all you need,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.457137Z

Source-reported events for the cited work

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Observation 16829f2a-4055-44e5-9511-a8037f6f5be6 · outbound

This paper cites Denoising diffusion probabilistic models,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Denoising diffusion probabilistic models,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.442166Z

Source-reported events for the cited work

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Observation b85b28c4-2d7f-4111-a7fb-5e752b9d740e · outbound

This paper cites Flow matching for generative modeling,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Flow matching for generative modeling,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.425407Z

Source-reported events for the cited work

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Observation e85e1f65-a3b0-4a84-a3b3-0449495e5de3 · outbound

This paper cites A guide to conducting consensual qualitative research,.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI A guide to conducting consensual qualitative research,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.411090Z

Source-reported events for the cited work

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Observation e632f5db-8d8a-4353-8b19-ef7b873e9560 · outbound

This paper cites Available: https://arxiv.org/abs/2301.1 1757.

MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI Available: https://arxiv.org/abs/2301.1 1757

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:09:34.572436Z

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Pith citing papers

Observation 47cdee41-5988-4ed8-b088-dc24782edb17 · inbound