{"as_of":"2026-08-16T11:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:07314954f6de26ecf635ec184e509ef7f920efad6813e56cbcd93dea901f36ba","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:41:08.363814Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.19085/citation-record","integrity":"/paper/2506.19085/integrity","json":"/paper/2506.19085/citation-record.json","paper":"/paper/2506.19085"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1812.08466","last_updated":"2019-01-17T16:12:43Z","snapshot_observed_at":"2026-08-15T08:25:11.276003Z","submitted_at":"2018-12-20T10:28:00Z","title":"Fr\\'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08466","snapshot_observed_at":"2026-08-15T18:41:08.261080Z","title":"Fr\\’echet audio distance: A metric for evaluating music enhancement algorithms,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.261080Z"},"links":{"cited_paper":"/paper/1812.08466","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:ec1d5df585ca7be8129c9d92f02e314f2407b5d5164e955b329b50c6acd691dc","observation_id":"db3d388d-bab2-4c93-821d-d88ad442e6a3","resolution":{"observed_at":"2026-08-15T18:41:08.261080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.265876Z","title":"Cnn architectures for large-scale audio classification,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.265876Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:6146467f4addaae5256b2518faa79d8840c639d344bf80a604702245f5d0514d","observation_id":"0d9c23fc-6dd2-4aef-aca4-2798b3948ca4","resolution":{"observed_at":"2026-08-15T18:41:08.265876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11325","last_updated":"2023-01-26T18:58:53Z","snapshot_observed_at":"2026-08-14T02:16:19.922733Z","submitted_at":"2023-01-26T18:58:53Z","title":"MusicLM: Generating Music From Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.11325","snapshot_observed_at":"2026-08-15T18:41:08.269577Z","title":"Musiclm: Generating music from text,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.269577Z"},"links":{"cited_paper":"/paper/2301.11325","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:69f1f3957ba7ec88710d38055908b0aa514fcf5485d217a4b50ad16a3f7a2b41","observation_id":"439f87fa-8344-4674-9fc1-3a973dc37428","resolution":{"observed_at":"2026-08-15T18:41:08.269577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.273596Z","title":"Riffusion-stable diffusion for real-time music generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.273596Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:02274debac6b4a1187f8949b62435fdd7a52150e51cd8d002d95a8002fa01073","observation_id":"78c5a8e9-6536-4152-801c-99f0fb94d247","resolution":{"observed_at":"2026-08-15T18:41:08.273596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.277627Z","title":"Clap learning audio concepts from natural language supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.277627Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:e68925c25b62c055c287c0f472794b175c681fc7cac6698c6eeede32278fcc79","observation_id":"5916771d-dcef-41ff-a9ad-cb66b8d002e1","resolution":{"observed_at":"2026-08-15T18:41:08.277627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.646705Z","title":"Natural language supervision for general-purpose audio representations,","venue":null,"work_id":"9f10dad7-2067-4089-982c-d4bbdf242feb","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.281458Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:e548eba4740edb32a2410cd226ae2c86fc732fb21623adf8fd5c28f39262326d","observation_id":"8061b7cd-a01d-4092-8e4e-6988f14a1807","resolution":{"observed_at":"2026-08-15T18:41:08.650932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.285205Z","title":"Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.285205Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:ba88b75c678db375e3f61a8b5d5a8e3d84f4f79c0d00c6446e09d266f2c8fa4d","observation_id":"cdec7458-427f-4e02-8f82-5adf2794f12d","resolution":{"observed_at":"2026-08-15T18:41:08.285205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.00130","last_updated":"2022-08-31T21:48:34Z","snapshot_observed_at":"2026-08-13T14:34:57.673254Z","submitted_at":"2022-08-31T21:48:34Z","title":"Evaluating generative audio systems and their metrics","version":1},"cited_work":{"arxiv_id":"2209.00130","doi":null,"metadata_source":"pith","pith_arxiv_id":"2209.00130","snapshot_observed_at":"2026-08-15T18:41:08.474287Z","title":"Evaluating generative audio systems and their metrics","venue":"cs.SD","work_id":"9db4049c-61b5-427a-9ac2-1c38ce984d9e","year":2022},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.288845Z"},"links":{"cited_paper":"/paper/2209.00130","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:875ddd91d8d93ab727433a1407cd6fb356d68813a07bb513fdd54fd46e030edd","observation_id":"2aaa79df-2a5d-42d4-bc10-453bc07881fa","resolution":{"observed_at":"2026-08-15T18:41:08.480133Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.292864Z","title":"Adapting frechet audio distance for generative music evaluation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.292864Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:6a09f3884c69c06ebcd095ec4c3817e130afe43e2c408390547705b4868625b0","observation_id":"43c6233e-a0b0-4a8a-9867-a6117890bf41","resolution":{"observed_at":"2026-08-15T18:41:08.292864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03917","last_updated":"2023-03-06T18:09:56Z","snapshot_observed_at":"2026-08-14T07:31:45.753439Z","submitted_at":"2023-02-08T07:27:27Z","title":"Noise2Music: Text-conditioned Music Generation with Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03917","snapshot_observed_at":"2026-08-15T18:41:08.296944Z","title":"Noise2music: Text-conditioned music generation with diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.296944Z"},"links":{"cited_paper":"/paper/2302.03917","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:c857a34282b6d2904e1976f1f5227f15565bc1bb44cd189b59cb907a34ddada4","observation_id":"948da723-3b9f-4bab-9a97-4ebdfff51363","resolution":{"observed_at":"2026-08-15T18:41:08.296944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.621064Z","title":"The mtg-jamendo dataset for automatic music tagging,","venue":null,"work_id":"9dc3109b-1c3a-44f1-a5b5-a25e0895a011","year":2019},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.300878Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:210b00236944f2d094bd446cee3352d28dff6266098f9405633183c8c89d1491","observation_id":"12ce9133-b659-4017-b101-f947b6045844","resolution":{"observed_at":"2026-08-15T18:41:08.625185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.01840","last_updated":"2017-09-05T18:38:33Z","snapshot_observed_at":"2026-08-14T21:26:53.042056Z","submitted_at":"2016-12-06T14:58:59Z","title":"FMA: A Dataset For Music Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.01840","snapshot_observed_at":"2026-08-15T18:41:08.308210Z","title":"Fma: A dataset for music analysis,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.308210Z"},"links":{"cited_paper":"/paper/1612.01840","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:452c85c60da7d9f0a0af6360e9512312e28a22aeb816425a00657304041c1286","observation_id":"9aae6ff8-95b4-4c42-a807-87e05aeef59f","resolution":{"observed_at":"2026-08-15T18:41:08.308210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.598366Z","title":"Evaluation of algorithms using games: The case of music tagging","venue":null,"work_id":"dafb4fce-07b7-4e0d-8e90-d2ee945653e9","year":2009},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.311373Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:ebd13d2c30cfba128f7345cec1f87a2545a630094b2537541e4d0dd97e540219","observation_id":"72f2aaae-d030-468c-b1f7-64090cb076ff","resolution":{"observed_at":"2026-08-15T18:41:08.602080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.587019Z","title":"Simple and controllable music generation,","venue":null,"work_id":"3db48866-d185-4160-8569-5be5a926640b","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.314540Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:91ad3f648cf314310ac90e4ef6ba0e66213db9fa7490a8ad057e6b2255df675f","observation_id":"ad36a96c-4b1e-48a7-8815-61b35ce5ad62","resolution":{"observed_at":"2026-08-15T18:41:08.590731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05734","last_updated":"2024-05-11T11:24:51Z","snapshot_observed_at":"2026-08-16T06:07:29.063480Z","submitted_at":"2023-08-10T17:55:13Z","title":"AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.05734","snapshot_observed_at":"2026-08-15T18:41:08.318232Z","title":"”audioldm 2: Learning holis- tic audio generation with self-supervised pretraining","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.318232Z"},"links":{"cited_paper":"/paper/2308.05734","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:da4ead182ef648eb30f11c57f026a8402e0159fc4fe597c609cb5a2e9be8abc4","observation_id":"3cb2cbae-4970-43b0-a9ec-6a46b0d4bd85","resolution":{"observed_at":"2026-08-15T18:41:08.318232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08355","last_updated":"2024-06-03T07:56:23Z","snapshot_observed_at":"2026-08-15T09:27:03.424255Z","submitted_at":"2023-11-14T17:54:38Z","title":"Mustango: Toward Controllable Text-to-Music Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.08355","snapshot_observed_at":"2026-08-15T18:41:08.322086Z","title":"Mustango: Toward controllable text-to-music generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.322086Z"},"links":{"cited_paper":"/paper/2311.08355","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:1f0770ace5834a54095f536f8dee942420ff44a88daaf5fc088834680d079ea3","observation_id":"8b43321c-ee0f-4bd9-95e1-1eab3c52c66b","resolution":{"observed_at":"2026-08-15T18:41:08.322086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04825","last_updated":"2024-05-13T14:05:00Z","snapshot_observed_at":"2026-08-14T12:24:12.895232Z","submitted_at":"2024-02-07T13:23:25Z","title":"Fast Timing-Conditioned Latent Audio Diffusion","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04825","snapshot_observed_at":"2026-08-15T18:41:08.326154Z","title":"Fast timing- conditioned latent audio diffusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.326154Z"},"links":{"cited_paper":"/paper/2402.04825","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:fa91c9c51ff8bef6008a0654f0ac7694ed5a65ee4e25d752c167d5b42fa99056","observation_id":"7e059e21-6226-4434-ba3a-444a187a3b82","resolution":{"observed_at":"2026-08-15T18:41:08.326154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.575943Z","title":null,"venue":null,"work_id":"3bcef5e4-519b-440f-8426-8ea88d282b5c","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.329837Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:2da0e9769165821f7d5d487d72968c095bdd92250cbb1158fcae9ece60e01de7","observation_id":"7fface9f-52f2-4c41-b8f9-8571222e54fa","resolution":{"observed_at":"2026-08-15T18:41:08.579608Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.565006Z","title":null,"venue":null,"work_id":"54554631-c7d9-4a5f-b0f3-5bac0284fb95","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.333773Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:b82bdeb8d0786eb476de65b91b544f87bd2923f19654f2520a73534d52ca523f","observation_id":"2b958d07-60c8-4f05-9ac4-dc7a2cdfefd9","resolution":{"observed_at":"2026-08-15T18:41:08.568780Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04132","last_updated":"2024-03-07T01:22:38Z","snapshot_observed_at":"2026-08-02T17:55:33.750637Z","submitted_at":"2024-03-07T01:22:38Z","title":"Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04132","snapshot_observed_at":"2026-08-15T18:41:08.337618Z","title":"Chatbot arena: An open platform for evaluating llms by human preference,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.337618Z"},"links":{"cited_paper":"/paper/2403.04132","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:c2752253ebee7c66f03efdde275707a0fbade610ba411fbb6c6ff6cad8a04f66","observation_id":"6819d7d1-2d2e-4750-9187-52b164ec8ce3","resolution":{"observed_at":"2026-08-15T18:41:08.337618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.554546Z","title":"Prolific,","venue":null,"work_id":"64eaf824-3a8c-4512-8ca0-b423b2a2cd7d","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.341977Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:f8bb999f97537e0f171facd0dafaf1581061ce22fd532b14b22efe70c0e8e12e","observation_id":"7849a48d-893f-4621-bff4-45d1f5dabc84","resolution":{"observed_at":"2026-08-15T18:41:08.558208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.543758Z","title":"Amazon mechanical turk,","venue":null,"work_id":"ebfedae0-5799-4874-840e-12f58473c88d","year":2024},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.345741Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:1e5c0de18a8dfaf2fd6c80259cb557775a3a732b1eb940b8a4bd492961ab491a","observation_id":"80cab03b-9481-4a36-a4ce-843db3560a59","resolution":{"observed_at":"2026-08-15T18:41:08.547407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.349138Z","title":"Data quality in on- line human-subjects research: Comparisons between mturk, prolific, cloudresearch, qualtrics, and sona,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.349138Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:29ecf782c82c03570af78eee26fbda075fdd95f3df7f034c200a9536f54c80b5","observation_id":"76add4fe-7dba-4300-8701-84063971fb94","resolution":{"observed_at":"2026-08-15T18:41:08.349138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.524127Z","title":"Fast and accurate inference of plackett–luce models,","venue":null,"work_id":"7bd627bd-bf7e-4799-8244-cb530598b3f8","year":2015},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.352644Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:5607071a6c92dbd692d4e10d67d9490ff7bc4258ab3d3c3827db76047fa73ec0","observation_id":"c422fb56-ed73-4940-9b27-45fb0bb8ba1b","resolution":{"observed_at":"2026-08-15T18:41:08.528263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.356407Z","title":"Rank analysis of incomplete block designs: I. the method of paired comparisons,","venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.356407Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:2a6ab1efd8d95b9b3acdf968a3d1d900e99296704cc0f14a297ad9af3fc35ad8","observation_id":"c0bef770-b242-4e22-a1fd-0af386203c05","resolution":{"observed_at":"2026-08-15T18:41:08.356407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.360214Z","title":"Panns: Large-scale pretrained audio neural networks for audio pattern recognition,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.360214Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:227d9c81075b09b6badc297cd25811b1332dba190ef65a3949de6d29bf03266a","observation_id":"190a0d05-9adb-4bf5-94f3-2a2709f7b1b2","resolution":{"observed_at":"2026-08-15T18:41:08.360214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13438","last_updated":"2022-10-24T17:52:02Z","snapshot_observed_at":"2026-08-15T04:43:16.361751Z","submitted_at":"2022-10-24T17:52:02Z","title":"High Fidelity Neural Audio Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13438","snapshot_observed_at":"2026-08-15T18:41:08.363814Z","title":"High fidelity neural audio compression,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.363814Z"},"links":{"cited_paper":"/paper/2210.13438","citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:05b741e18e2e1e8bb4b261cd3620279ca2fb7a57fe5a6cff8d0aa2594402b6d3","observation_id":"fb271fd9-322c-4cc7-8e81-a65c6589c74d","resolution":{"observed_at":"2026-08-15T18:41:08.363814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:41:08.610401Z","title":"Available: http://hdl.handle.net/10230/42015","venue":null,"work_id":"57a358e5-8d3d-427e-aeba-9d9160611f05","year":null},"citing_paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T18:41:08.304481Z"},"links":{"citing_paper":"/paper/2506.19085"},"observation_digest":"sha256:6cb6055d2d54adcddd44c5966f912be08fb7e802f01c0427239a7285c079a95b","observation_id":"618987df-e798-44c1-9067-83f2a49f7f3e","resolution":{"observed_at":"2026-08-15T18:41:08.613714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.19085","last_updated":"2025-06-23T20:01:29Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T18:35:01.773892Z","submitted_at":"2025-06-23T20:01:29Z","title":"Benchmarking Music Generation Models and Metrics via Human Preference Studies"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":8},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.19085."}