{"as_of":"2026-08-15T23:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b39550788567a68f90c8f7540ca8f7052d31e0d12c3f9e434ee04bed27cd9ddf","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:24:21.848332Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:47:17.612675Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T14:47:18.305594Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"cited_work":{"arxiv_id":"2607.20253","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.20253","snapshot_observed_at":"2026-08-15T14:47:18.305594Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","venue":"cs.SD","work_id":"7cb91ad2-b36b-41ef-90ff-d575595d2d2a","year":2026},"citing_paper":{"arxiv_id":"2608.03999","last_updated":"2026-08-04T17:56:49Z","snapshot_observed_at":"2026-08-15T14:42:05.283821Z","submitted_at":"2026-08-04T17:56:49Z","title":"Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T14:47:17.612675Z"},"links":{"cited_paper":"/paper/2607.20253","citing_paper":"/paper/2608.03999"},"observation_digest":"sha256:598df0859b14ba695aa203854678bd4ee23383586a4a1ac5798ab5a4c0167ba8","observation_id":"653a5994-2993-48f7-b883-9e9841a1efaa","resolution":{"observed_at":"2026-08-15T14:47:18.310080Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2607.20253/citation-record","integrity":"/paper/2607.20253/integrity","json":"/paper/2607.20253/citation-record.json","paper":"/paper/2607.20253"},"outbound":[{"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":"2026-08-01T10:24:18.006109Z","title":"Musiclm: Generating music from text.arXiv preprint arXiv:2301.11325, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.006109Z"},"links":{"cited_paper":"/paper/2301.11325","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:70a72f1e4d55330cfaf6a0c0257d755c33cf3bc70115734332a9b82aba249ade","observation_id":"092c7024-82f6-4346-9df7-a3172c00e569","resolution":{"observed_at":"2026-08-01T10:24:18.006109Z","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":"2026-08-01T10:24:18.134417Z","title":"Music generation benchmarking methodology","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.134417Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:c879112a0c57f7733e972db6d3ac0b336b3321abfb168dbcc4ceb91a9481ccab","observation_id":"15b1b6f4-2bc9-4438-bf93-1ad6a421b749","resolution":{"observed_at":"2026-08-01T10:24:18.134417Z","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":"2026-08-01T10:24:18.298421Z","title":"V ocals music leaderboard","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.298421Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:8936d4bb8aa6027552565f92fc6a63e0563bc483facfbbcfb9f2e09109b4997a","observation_id":"a57b6254-ff95-4b33-b2d7-2a371b5ceec0","resolution":{"observed_at":"2026-08-01T10:24:18.298421Z","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":"2026-08-01T10:24:18.390559Z","title":"Yourmt3+: Multi- instrument music transcription with enhanced transformer architectures and cross-dataset stem augmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.390559Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:3e531cacddb63f4be173c35c6848ba18f67b8185384a3954546a23e64c4bb9e1","observation_id":"291d1b82-2267-412f-9b0c-c0907bf72536","resolution":{"observed_at":"2026-08-01T10:24:18.390559Z","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":"2026-08-01T10:24:18.493462Z","title":"Musicldm: Enhancing novelty in text-to-music generation using beat-synchronous mixup strategies","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.493462Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:0c92cc1d776e1495e5e7200f0a9a53257c42525ccbe8e9150b5045b5365f1624","observation_id":"bcbc03ff-7951-499d-9455-5e6acfde02ad","resolution":{"observed_at":"2026-08-01T10:24:18.493462Z","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":"2026-08-01T10:24:18.639038Z","title":"Visqol v3: An open source production ready objective speech and audio metric","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.639038Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:9d0ee8dc612848c426626941f45a651cbcfb94b2a5f91bba52d2ab0bf4df771a","observation_id":"63c8413b-bc8b-4e6e-8f89-03f77c6ee67f","resolution":{"observed_at":"2026-08-01T10:24:18.639038Z","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":"2026-08-01T10:24:18.749729Z","title":"Self-supervised learning with random-projection quantizer for speech recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.749729Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:bcc59f054327d52c676f4700cdf5294fcefa012abd80929b31b83ee4634ddfe3","observation_id":"243b5bf6-cb98-4bcf-aed2-4d70a9fcdb94","resolution":{"observed_at":"2026-08-01T10:24:18.749729Z","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":"2026-08-01T10:24:18.811849Z","title":"Simple and controllable music generation.Advances in neural information processing systems, 36:47704–47720, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.811849Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:b67cefa6c40808ae7a3a7b4a4d8eaea8d165d837ee50277dbdfc3576214e79b6","observation_id":"cf9f9a73-254c-4c92-80ce-6b7bb7d7ccea","resolution":{"observed_at":"2026-08-01T10:24:18.811849Z","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":"2026-08-01T10:24:18.929585Z","title":"Ace-step 1.5: Pushing the boundaries of open-source music generation.arXiv preprint arXiv:2602.00744, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:18.929585Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:7d10ff08e431ad78279f0308b0621f4c5253357d46f12677d84b8f932dd17d94","observation_id":"fc40c4c3-9c81-4c21-ad1a-eaebd1d559a0","resolution":{"observed_at":"2026-08-01T10:24:18.929585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.00045","last_updated":"2025-05-28T12:23:09Z","snapshot_observed_at":"2026-08-09T08:18:10.025134Z","submitted_at":"2025-05-28T12:23:09Z","title":"ACE-Step: A Step Towards Music Generation Foundation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.00045","snapshot_observed_at":"2026-08-01T10:24:19.082295Z","title":"Ace-step: A step towards music generation foundation model.arXiv preprint arXiv:2506.00045, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.082295Z"},"links":{"cited_paper":"/paper/2506.00045","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:f8936dd9e03d107e2393c6916c0019563b3cc0ef42124df26ef6c7c738cbba13","observation_id":"c2a48299-6730-44a6-baaf-3ad500282caf","resolution":{"observed_at":"2026-08-01T10:24:19.082295Z","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":"2026-08-01T10:24:19.205919Z","title":"Visqol: an objective speech quality model.EURASIP Journal on Audio, Speech, and Music Processing, 2015(1):13, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.205919Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:3956b4e9b710a22b08931a79160d98967c3e2c416de198828c55495803a111e5","observation_id":"c71f853a-4002-407f-ae28-1d97362973d7","resolution":{"observed_at":"2026-08-01T10:24:19.205919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-01T10:24:19.344153Z","title":"Classifier-free diffusion guidance.CoRR, abs/2207.12598, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.344153Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:d6afc05d6ac0485a29cdcf119fd93efdb433dfc5e189cadb51ef2eb7c2ad1baa","observation_id":"22bc1965-513f-4a7d-b197-0867ecd454a3","resolution":{"observed_at":"2026-08-01T10:24:19.344153Z","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":"2026-08-01T10:24:19.468121Z","title":"Levo: High-quality song generation with multi- preference alignment.Advances in Neural Information Processing Systems, 38:102448–102479, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.468121Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:8f12b3a5be8580a8c4f771f7c2feee0f17904eba3523c092f92381cd045ccab2","observation_id":"90c79656-f78b-4b0e-a716-6d4069323389","resolution":{"observed_at":"2026-08-01T10:24:19.468121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.30642","last_updated":"2026-06-29T17:59:20Z","snapshot_observed_at":"2026-08-13T14:26:47.969747Z","submitted_at":"2026-06-29T17:59:20Z","title":"LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.30642","snapshot_observed_at":"2026-08-01T10:24:19.629619Z","title":"Levo 2: Stable and melodious song gener- ation via hierarchical representation modeling and progressive post-training.arXiv preprint arXiv:2606.30642, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.629619Z"},"links":{"cited_paper":"/paper/2606.30642","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:f877569152a420104a1dc94b1bb22bcc239440749ec8a8ad99169a06048ff045","observation_id":"5251279e-a494-4357-b986-416e5e204f89","resolution":{"observed_at":"2026-08-01T10:24:19.629619Z","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":"2026-08-01T10:24:19.768908Z","title":"Songecho: Towards cover song generation via instance-adaptive element-wise linear modulation.arXiv preprint arXiv:2602.19976, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.768908Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:f3eb566865f0484c0a530f7016b867be20f5ae539f9f02bfb1a0df5f9dcb7018","observation_id":"c3c06397-82d8-41be-b830-95d0becb9592","resolution":{"observed_at":"2026-08-01T10:24:19.768908Z","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":"2026-08-01T10:24:19.935018Z","title":"Duo-tok: Dual-track semantic music tokenizer for vocal-accompaniment generation.arXiv preprint arXiv:2511.20224, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:19.935018Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:e805a00a48283239ed270b859e4c63df62d34cdd3c6386225a1aa3fa6a5cbf8d","observation_id":"8a76ba1c-6b22-469b-8255-23c6d38a7d2f","resolution":{"observed_at":"2026-08-01T10:24:19.935018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.01790","last_updated":"2026-07-06T10:05:57Z","snapshot_observed_at":"2026-08-11T13:59:40.244112Z","submitted_at":"2026-05-03T09:13:20Z","title":"Shao: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.01790","snapshot_observed_at":"2026-08-01T10:24:20.063956Z","title":"Khala: Scaling acoustic token language models toward high-fidelity music generation.arXiv preprint arXiv:2605.01790, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.063956Z"},"links":{"cited_paper":"/paper/2605.01790","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:b385910ef342f25ae0d2053f1cdeafe4b8096080f534d816d04b692a8dce33b3","observation_id":"f799bc6a-d8ba-4fea-9d08-17484026fdeb","resolution":{"observed_at":"2026-08-01T10:24:20.063956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05470","last_updated":"2025-10-27T09:57:02Z","snapshot_observed_at":"2026-08-13T10:15:00.293616Z","submitted_at":"2025-05-08T17:58:45Z","title":"Flow-GRPO: Training Flow Matching Models via Online RL","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.05470","snapshot_observed_at":"2026-08-01T10:24:20.269589Z","title":"Flow-grpo: Training flow matching models via online rl.arXiv preprint arXiv:2505.05470, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.269589Z"},"links":{"cited_paper":"/paper/2505.05470","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:0d4f39f08d9c21aa66e1e8a8cb265e5a95d1049665fb77f9a6bf0be258367774","observation_id":"d93b9a5e-1c78-4bbd-addc-9c7b237946a1","resolution":{"observed_at":"2026-08-01T10:24:20.269589Z","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":"2026-08-01T10:24:20.398877Z","title":"Music source separation with band-split rope transformer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.398877Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:d4543edb6a1820d93cea71442c6ab32b6b12e2cb77ee6ea0cf337cfd64018347","observation_id":"8885ab35-2372-4fcd-ac94-3bc440302153","resolution":{"observed_at":"2026-08-01T10:24:20.398877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.00610","last_updated":"2026-06-11T15:20:43Z","snapshot_observed_at":"2026-08-14T18:43:03.691653Z","submitted_at":"2026-02-28T12:10:58Z","title":"CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.00610","snapshot_observed_at":"2026-08-01T10:24:20.489543Z","title":"CMI- RewardBench: Evaluating music reward models with compositional multimodal instruction","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.489543Z"},"links":{"cited_paper":"/paper/2603.00610","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:ee637c2db954dd21f66ffae249dec91d9b8c95717f54519bc42bb4290211594c","observation_id":"c3ce65f0-1775-4e9d-b6d2-4995916b2e60","resolution":{"observed_at":"2026-08-01T10:24:20.489543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01183","last_updated":"2025-03-03T05:15:34Z","snapshot_observed_at":"2026-08-13T12:51:33.315849Z","submitted_at":"2025-03-03T05:15:34Z","title":"DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01183","snapshot_observed_at":"2026-08-01T10:24:20.586685Z","title":"Diffrhythm: Blazingly fast and embarrassingly simple end-to-end full-length song generation with latent diffusion.arXiv preprint arXiv:2503.01183, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.586685Z"},"links":{"cited_paper":"/paper/2503.01183","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:3e642756d043ee7cdaea304f08114c87df07754028291be340ba71539ec19ec8","observation_id":"d9736da5-4768-4c28-aacb-e671f45f624a","resolution":{"observed_at":"2026-08-01T10:24:20.586685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05139","last_updated":"2025-02-07T18:15:57Z","snapshot_observed_at":"2026-08-14T10:00:02.244477Z","submitted_at":"2025-02-07T18:15:57Z","title":"Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05139","snapshot_observed_at":"2026-08-01T10:24:20.680861Z","title":"Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.680861Z"},"links":{"cited_paper":"/paper/2502.05139","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:b21ee59ab8e50c1b80f0269941542253f9f415ef4693fd2ad1a2c5c80f0a29d7","observation_id":"f8aaec9d-b444-43f3-b35a-2f01365c39bd","resolution":{"observed_at":"2026-08-01T10:24:20.680861Z","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":"2026-08-01T10:24:20.803917Z","title":"Flowse-grpo: Training flow matching speech enhancement via online reinforcement learning","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.803917Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:fc4d4dae2382d9aea1fad51e726cfd02cac9988cc834e8d4757b4418d7863790","observation_id":"6a479f5c-1c89-4c33-9901-6dcdd626be35","resolution":{"observed_at":"2026-08-01T10:24:20.803917Z","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":"2026-08-01T10:24:20.947217Z","title":"Flowtts-grpo: Online reinforcement learning with multi-objective reward optimization for flow-matching based text-to-speech, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:20.947217Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:df062d750b25a0fbb274202fe08185c3204cefa8d06d85e4768bea173a9588b8","observation_id":"47fae71c-f098-495a-aa25-a922088cebd3","resolution":{"observed_at":"2026-08-01T10:24:20.947217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15412","last_updated":"2023-06-28T01:53:37Z","snapshot_observed_at":"2026-08-13T11:08:28.119782Z","submitted_at":"2023-06-27T12:11:55Z","title":"RMVPE: A Robust Model for Vocal Pitch Estimation in Polyphonic Music","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15412","snapshot_observed_at":"2026-08-01T10:24:21.023292Z","title":"Rmvpe: A robust model for vocal pitch estimation in polyphonic music.arXiv preprint arXiv:2306.15412, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.023292Z"},"links":{"cited_paper":"/paper/2306.15412","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:7822b0e9655cf064f64be0062ca5e378ba7c09d23a26b02572d09b222bc78d41","observation_id":"fd24275a-1d06-41b5-b55f-731b81c0b864","resolution":{"observed_at":"2026-08-01T10:24:21.023292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.25937","last_updated":"2026-04-16T04:26:34Z","snapshot_observed_at":"2026-08-15T16:06:56.859743Z","submitted_at":"2026-04-16T04:26:34Z","title":"SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.25937","snapshot_observed_at":"2026-08-01T10:24:21.142293Z","title":"SongBench: A fine-grained multi-aspect benchmark for song quality assessment.arXiv preprint arXiv:2604.25937, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.142293Z"},"links":{"cited_paper":"/paper/2604.25937","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:e66401c3cf9682e8c4d5ea8d00c58589e50da2a5a29dfacf6b3ef838dbb8b711","observation_id":"6bd7bbfe-c7be-4c12-958e-9c1fd44cbe4a","resolution":{"observed_at":"2026-08-01T10:24:21.142293Z","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":"2026-08-01T10:24:21.220014Z","title":"Mucodec: Ultra low-bitrate music codec for music generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.220014Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:970cb3c9316b24326694c344af558244fb999b67c14f35350dccf7ac95972721","observation_id":"e20899c9-182f-4ab3-a1c9-3040479f2f34","resolution":{"observed_at":"2026-08-01T10:24:21.220014Z","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":"2026-08-01T10:24:21.329645Z","title":"Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.329645Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:ce8f0a0c672a1defc69e83b783e451822348af5cb273c9e0d771c34c1bbc1a7f","observation_id":"2d6c1eaa-769f-46a2-b220-4d9e21c9c48f","resolution":{"observed_at":"2026-08-01T10:24:21.329645Z","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":"2026-08-01T10:24:21.422952Z","title":"Songbloom: Coherent song generation via interleaved autoregressive sketching and diffusion refinement","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.422952Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:393d0eb1c28d09f6b9db810e76aca480c88517f98633429fee15839ce40dce50","observation_id":"7c09a726-b2b2-45c2-9880-1d469f991d87","resolution":{"observed_at":"2026-08-01T10:24:21.422952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.10547","last_updated":"2026-07-07T07:53:42Z","snapshot_observed_at":"2026-08-13T18:10:53.815074Z","submitted_at":"2026-01-15T16:14:25Z","title":"HeartMuLa: A Family of Open Sourced Music Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.10547","snapshot_observed_at":"2026-08-01T10:24:21.505367Z","title":"Heartmula: A family of open sourced music foundation models.arXiv preprint arXiv:2601.10547, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.505367Z"},"links":{"cited_paper":"/paper/2601.10547","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:b2853198ba88ff3466c7f992b52032e3f1e481602e643bc95342395d76a41a67","observation_id":"8038b852-6a47-4c4c-b062-81309a52f6c6","resolution":{"observed_at":"2026-08-01T10:24:21.505367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10793","last_updated":"2025-05-16T02:06:25Z","snapshot_observed_at":"2026-08-15T21:01:03.472765Z","submitted_at":"2025-05-16T02:06:25Z","title":"SongEval: A Benchmark Dataset for Song Aesthetics Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.10793","snapshot_observed_at":"2026-08-01T10:24:21.592941Z","title":"SongEval: A benchmark dataset for song aesthetics evaluation.arXiv preprint arXiv:2505.10793, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.592941Z"},"links":{"cited_paper":"/paper/2505.10793","citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:681c667421ab521ca45546c6ff6288517f64ac00715d6ee7498badac6cb73d6c","observation_id":"0becebc9-d2e8-4ce0-8b4f-e6aa50428043","resolution":{"observed_at":"2026-08-01T10:24:21.592941Z","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":"2026-08-01T10:24:21.848332Z","title":"Yue: Scaling open foundation models for long-form music generation.arXiv preprint arXiv:2503.08638, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T10:24:21.848332Z"},"links":{"citing_paper":"/paper/2607.20253"},"observation_digest":"sha256:0d9513487347b8bbaad7f4648a8874e15827d6be19bba3d0034d653dfd8b8fed","observation_id":"b334e699-ae91-4e7c-a3a3-5f6203f96858","resolution":{"observed_at":"2026-08-01T10:24:21.848332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20253","last_updated":"2026-07-29T04:40:54Z","latest_version":3,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-13T19:21:38.414761Z","submitted_at":"2026-07-22T15:11:46Z","title":"Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":32,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":32},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2607.20253."}