{"as_of":"2026-08-14T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4b7756aa7fecb097db47ea5784fdfa631e4ae5995591f00f8e4461652c06cd67","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T12:38:27.978216Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2509.26007/citation-record","integrity":"/paper/2509.26007/integrity","json":"/paper/2509.26007/citation-record.json","paper":"/paper/2509.26007"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Non-autoregressive neural text-to-speech","venue":null,"work_id":"8d7f7558-f20c-4666-9a77-44bb3813f539","year":2020},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:6b06acd3b0634f9cd67c7b12f906183f5b1ab609e58d915f7fe6f9cfd7d3df1c","observation_id":"83cdff75-4210-4bf5-bf6d-7b3415d51825","resolution":{"observed_at":"2026-05-18T12:42:38.057478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi- resolution spectrogram","venue":null,"work_id":"edf1a74c-5a1c-4031-9474-5158204c4344","year":2020},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:b923c2838d185fba253dee4a86505cf05cee04b337c16c284ef6a6c0c4b49f58","observation_id":"d098c0e9-e5fe-49fc-8e91-c54a8e701b39","resolution":{"observed_at":"2026-05-18T12:42:38.060404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"High fidelity speech syn- thesis with adversarial networks","venue":null,"work_id":"8f3a7b11-534b-4e87-9155-b4d4852fbbc5","year":2020},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:aaf2ecdfeeb44d6f752c67e367317e435e6e325cc0de22dee4fa015b404ec244","observation_id":"3d79dbeb-3b48-4f38-abcd-cd875583c352","resolution":{"observed_at":"2026-05-18T12:42:38.054470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Diffwave: A versatile diffusion model for audio synthesis","venue":null,"work_id":"c55ade38-95d8-410a-a490-9201280cf829","year":2021},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:a2cacb4060a3efd125bb26268b8c1b9adc2456c43baa0c73936eee01a8aeafdf","observation_id":"cd9fa6aa-95a7-4973-8b25-06d60de61705","resolution":{"observed_at":"2026-05-18T12:42:38.063637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.01083","last_updated":"2019-06-04T04:58:19Z","snapshot_observed_at":"2026-08-10T16:14:28.914911Z","submitted_at":"2019-06-04T04:58:19Z","title":"MelNet: A Generative Model for Audio in the Frequency Domain","version":1},"cited_work":{"arxiv_id":"1906.01083","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.01083","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"MelNet: A Generative Model for Audio in the Frequency Domain","venue":"eess.AS","work_id":"e575b2ba-3e84-47ec-bf94-2c374cd9245a","year":2019},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"cited_paper":"/paper/1906.01083","citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:0ed862472d608a331098a0c3ba384a5e43b86c650037d19ad107c991357f1d94","observation_id":"72bd4f13-6e81-46fc-a026-bc118c13a70d","resolution":{"observed_at":"2026-05-18T12:41:22.890885Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Gansynth: Adversarial neural audio synthesis","venue":null,"work_id":"f2eb528b-eb58-4dd8-aff0-657b23faea88","year":2019},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:9c3adf87dbb95639a5e07b22c01b9e945dedf46f136a72c8d45b993dffb58dd2","observation_id":"3dc91e90-9d1f-4abc-92d1-791a36552d38","resolution":{"observed_at":"2026-05-18T12:42:38.067574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08667","last_updated":"2023-11-18T15:16:16Z","snapshot_observed_at":"2026-08-14T10:10:40.451425Z","submitted_at":"2023-11-15T03:27:35Z","title":"EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis","version":2},"cited_work":{"arxiv_id":"2311.08667","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.08667","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Edmsound: Spectrogram based diffu- sion models for efficient and high-quality audio synthe- sis","venue":null,"work_id":"aa36bded-1e5f-4441-85d5-f855e5968b22","year":2023},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"cited_paper":"/paper/2311.08667","citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:dc0bb6634d17ada8099a40a41229e45d7b89ec7a8761f8310b10005108fcf79b","observation_id":"53f1043c-1522-481f-a8e8-5928e1b37d74","resolution":{"observed_at":"2026-05-18T12:41:22.884762Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Visual autoregressive modeling: Scalable image generation via next-scale prediction","venue":null,"work_id":"ab540771-4e13-4962-9b54-7ff37750ed87","year":2024},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:256507dd64d1e0797ddd3f24412403c70c3e190e386930ec5107b9f285510b83","observation_id":"7b974308-6f21-489d-9cfa-ec5b7a9f6b71","resolution":{"observed_at":"2026-05-18T12:42:38.083269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Imagefolder: Autoregres- sive image generation with folded tokens","venue":null,"work_id":"bc428b70-2a84-46ba-b6ad-6c4c33d73f74","year":2025},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:ba466303fe4e33afdf24e538593580ab4f28ba39c84dc731e8b8b660757e7343","observation_id":"c4e887dd-f867-4952-9af9-26c744b6e9ca","resolution":{"observed_at":"2026-05-18T12:42:38.088235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Neural audio synthesis of musical notes with wavenet autoencoders","venue":null,"work_id":"2d6ac02a-3c38-4aa3-acd7-fcbf8aa75795","year":2017},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:85894680b8bd5678efc537c7a087cbf93dacd6e9b6bb46390615ea73fec3a6fb","observation_id":"81aa1604-05e3-436b-a034-65c76d082ef1","resolution":{"observed_at":"2026-05-18T12:42:38.080522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Evaluating gen- erative audio systems and their metrics","venue":null,"work_id":"398726cd-3a6d-458d-836b-93a0406f571f","year":2022},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:7d97c514c53456f5e206c0127ba658397aa306c5450cc304ca6b6c7129bb0cfe","observation_id":"ee707657-182d-4a61-8901-79c29218b51a","resolution":{"observed_at":"2026-05-18T12:42:38.085974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"DDSP: differentiable digital signal pro- cessing","venue":null,"work_id":"24c51ff4-2823-4744-a7fe-ec5689603881","year":2020},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:38d2cd1c935e50502ebf0e4c26ba73281f054a980d43d87484f969aab9440cf6","observation_id":"d9d658cc-394b-4bba-b8e4-09c2b4085d7f","resolution":{"observed_at":"2026-05-18T12:42:38.071183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Signal estimation from mod- ified short-time fourier transform","venue":null,"work_id":"8eaaddcd-586f-4c1d-a05c-7a1874492db7","year":1984},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:b6ace307856661704cb4e4086e2ac99be04baf1be97198087130ea2bf7830c48","observation_id":"2b89528e-07d0-4ec3-8a9b-b0376a0adcc2","resolution":{"observed_at":"2026-05-18T12:42:38.077078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Image-to-image translation with conditional ad- versarial networks","venue":null,"work_id":"095b4ebc-55fb-4195-a05b-a8fefc72939c","year":2017},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:3f6b5cc541459b4ef2f51b6b9f6f9224698150ef3509fbaad29f1c102cd9d64e","observation_id":"3f869f34-3f93-4e11-9ad5-0d32e29417a7","resolution":{"observed_at":"2026-05-18T12:42:38.051621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"On gans and gmms","venue":null,"work_id":"69f19fc2-ff0c-4784-bb86-dc0eec56e094","year":2018},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:a363ab174851313e0393a041955be7d103c75986b7f4095811abba5701b98519","observation_id":"1dd7580a-31cd-42fd-a90c-b4085828bf57","resolution":{"observed_at":"2026-05-18T12:42:38.074345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Com- paring representations for audio synthesis using gener- ative adversarial networks","venue":null,"work_id":"a9612d07-ebe0-45fa-9881-d60b0d1eb915","year":2021},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:f14b452b2ddcdcefe0d83b4c45299541b0bdd4b29efe10785c5378dbf15603a6","observation_id":"58992367-69fa-49ae-8f5f-745b0660273e","resolution":{"observed_at":"2026-05-18T12:42:38.046259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Demystifying MMD gans","venue":null,"work_id":"148522af-ca03-46c6-a530-88e81963ea48","year":2018},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:6182a07df296c545de5ee28ef79c41b86fb05e97473643635a7c56a7dd563c90","observation_id":"8f4acb44-15cf-475b-b199-776a9dfbec36","resolution":{"observed_at":"2026-05-18T12:42:38.043328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Fr´echet audio distance: A reference- free metric for evaluating music enhancement algo- rithms","venue":null,"work_id":"2515d9e2-a5bd-4ac8-a3e4-c9d204d90790","year":2019},"citing_paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T12:38:27.978216Z"},"links":{"citing_paper":"/paper/2509.26007"},"observation_digest":"sha256:51b7f0c0cd67e75b54d85b8cfdb612fd5a220d965d44a8ea25b022e836cf4020","observation_id":"8ffe8b70-7013-40db-b8dd-e7aa9b3cefb6","resolution":{"observed_at":"2026-05-18T12:42:38.048804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.26007","last_updated":"2026-04-16T15:01:23Z","latest_version":2,"primary_category":"cs.SD","snapshot_observed_at":"2026-07-31T15:15:50.184449Z","submitted_at":"2025-09-30T09:38:02Z","title":"MARS: Sound Generation via Multi-Channel Autoregression on Spectrograms"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":2,"verified_fuzzy":16},"total_outbound_references":18},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.26007."}