{"as_of":"2026-08-10T08:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f2294bf59c9e795d47a0fe5b0763470c3638957236bfe255a44b06a73c3acda8","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T01:07:08.513230Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T18:42:37.123392Z","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":"2026-08-08T18:42:37.302502Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.10003","snapshot_observed_at":"2026-07-14T01:07:08.513230Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"cited_paper":"/paper/2607.10003","citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:e7f70983e765d76f06066de0ba897eb73ae9ba275be731991c4e0a7e350d31ca","observation_id":"bd62f60e-1d5c-4bf2-b2a4-11864be8eff5","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"cited_work":{"arxiv_id":"2607.10003","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.10003","snapshot_observed_at":"2026-08-08T18:42:37.302502Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","venue":"cs.SD","work_id":"3d3a7920-dc6d-4cd0-a938-060541d19317","year":2026},"citing_paper":{"arxiv_id":"2608.04378","last_updated":"2026-08-05T02:38:20Z","snapshot_observed_at":"2026-08-09T21:49:55.983462Z","submitted_at":"2026-08-05T02:38:20Z","title":"Helping Music Co-Creation Agents 'Listen' Well: Hierarchical Self-Supervised World Models for Understanding and Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T18:42:37.123392Z"},"links":{"cited_paper":"/paper/2607.10003","citing_paper":"/paper/2608.04378"},"observation_digest":"sha256:a097b96e5203de2d006835db12681949dd4b7d23ed2cf5773d1856ae0f51575d","observation_id":"b90d367e-3164-4d36-a123-753077ee38f0","resolution":{"observed_at":"2026-08-08T18:42:37.310363Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2607.10003/citation-record","integrity":"/paper/2607.10003/integrity","json":"/paper/2607.10003/citation-record.json","paper":"/paper/2607.10003"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.10003","snapshot_observed_at":"2026-07-14T01:07:08.513230Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"cited_paper":"/paper/2607.10003","citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:e7f70983e765d76f06066de0ba897eb73ae9ba275be731991c4e0a7e350d31ca","observation_id":"bd62f60e-1d5c-4bf2-b2a4-11864be8eff5","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"We therefore re- view each area","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:6bcf72ae16d80be4b5aadf3162928605006f1134a299e4d3ef915e60a624a961","observation_id":"997163c6-0e09-4186-9473-eacd6fb60513","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"We in- troduce the input representation, model components, and training objectives in the specification below","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:2df2e4abff8fd801cf88e543b135e61d02a0854cbba30138bd5b381ff96576b4","observation_id":"03169d3f-39dc-4c89-b739-fab33cf15d4f","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Both used= 512, 8 heads, and an FFN dimension of 2048","venue":null,"work_id":null,"year":2048},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:ed7c2b04ac8d57dc777f3399097dfd963144344bb9d03c7e9b6ee78530651586","observation_id":"9ee83284-407e-4464-b255-4f899af77c97","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"next-window","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:27bba31e90d55840fffbe8585a704d468bb38865a35f11f57daff4cafd918910","observation_id":"92cb8939-f080-4b48-a442-f3cd2657b26f","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:765d35ff9ec7f35e3a7db4dec2830583aab7c22549561dda3fef1a02e033d0d6","observation_id":"f888acf2-76bc-4a8c-99dc-45839974d26e","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"All scientific decisions, experimental de- signs, figure drawing, results analysis and discussion were completely made by humans","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:15c76da46ecbf33778055f26119ce6f6115e4f9840f66e863df718b1913d2dbc","observation_id":"2fdecc3c-fc52-475f-8577-39b7dce74a77","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"MidiBERT-Piano: Large-scale pre-training for symbolic music classification tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:f141c24f8aca1ee743b92a740c9c1ab7cc50a1a5041db1cdd2906f8b0839e034","observation_id":"002c5c51-68a8-4626-b96b-9a92f0f32e6f","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"MusicBERT: Symbolic music understanding with large-scale pre-training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:1bc5304d7b77be1a940eedfca18fb4996ef3437ab677686ccae3646cd9f14d88","observation_id":"869a5a54-dd3b-4695-9dc8-5fc57af0974a","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"PianoBART: Symbolic piano music genera- tion and understanding with large-scale pre-training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:441424c77c301356d2aad08802f00c906f9315645d3cd9787e5a68716c1c478e","observation_id":"72002ffe-16b1-4e5d-8905-9afd70f91a54","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Scaling self-supervised representation learning for symbolic piano performance,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:6ae810171d0fa1bc90347a54fda67edc6cd8fae49be6110020104fbd357ebe28","observation_id":"a3952141-2f62-4abf-a961-4b3349c6c31b","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Natural language processing methods for symbolic music generation and information retrieval: A survey,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:bb05187a62ef39dddde6d91637043954b277ef8d28e6ae5076eb25453355146a","observation_id":"6e1018a0-180e-4686-ad4f-4b06eaae35d0","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:b6bb5c52a04a5b31a59e41694630da16107319570d3f0af8f7cd89e2ac618d37","observation_id":"c6a72841-3c33-408b-a4a8-0bc79cfb5c9a","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:d5d016f6b500b56f3257d87d8e2e37caf0dce0e9178c6eb7073fe0927f246d7e","observation_id":"99bea12f-a643-489e-8a28-6211e36479bc","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"10.5281/zenodo.10265247","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Clamp: Contrastive language-music pre-training for cross- modal symbolic music information retrieval,","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"0ce60fdb-1d08-4eca-8b74-e8369a9b889c","year":2023},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:23830814cc1960ffaf62cced922aac1707e910f187dbfb23dabd635056d1c91f","observation_id":"537f7179-3b0b-4971-93ce-f5c6665cb521","resolution":{"observed_at":"2026-07-14T01:10:07.324768Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-07-16T19:51:45.718341+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-16T19:51:45.718341+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-07-14T01:07:08.513230Z","title":"Self-supervised learning from images with a joint- embedding predictive architecture,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:2418cdeb66c3aeaab17c30b04db35490d70f480e049ea3e643b6e29b12cc66b6","observation_id":"0eb11ace-4128-4970-be30-dad4c2e0d3cd","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08471","last_updated":"2024-02-15T18:59:11Z","snapshot_observed_at":"2026-08-07T02:30:11.447693Z","submitted_at":"2024-02-15T18:59:11Z","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08471","snapshot_observed_at":"2026-07-14T01:07:08.513230Z","title":"Revisiting fea- ture prediction for learning visual representations from video,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"cited_paper":"/paper/2404.08471","citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:e9309830b1c8b1b45f070604e0b1eef948f82c3e1f16c0c3df2bb980d84df1e2","observation_id":"daad2de4-2f19-4360-b1cd-307e857762d5","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.19312","last_updated":"2026-06-03T18:50:40Z","snapshot_observed_at":"2026-08-06T05:42:53.129146Z","submitted_at":"2026-03-13T19:48:14Z","title":"LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.19312","snapshot_observed_at":"2026-07-14T01:07:08.513230Z","title":"Leworldmodel: Stable end-to-end joint-embedding predictive architecture from pixels,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"cited_paper":"/paper/2603.19312","citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:6e641d138edbe076c6f2e38e86424a0b971fdd3ceb9f6b1905da64e899ff0d3c","observation_id":"610a817f-c4c9-43b4-ac82-94e4b4b4af1c","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-07-14T01:07:08.513230Z","title":"Representa- tion learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:fda92bcdbf72b3211847c7651dd91d9110da57d593bea4385613ade5bdce0de3","observation_id":"da2f0a1c-cfc7-437d-85f3-371657b2f5fa","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Improving BERT for symbolic music understanding using token denoising and pi- anoroll prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:773edf75f25621906648f3f6c372153fc40b3b6ce600cce5c9fd8ae30de288d2","observation_id":"0160171b-2191-4ec7-9848-0223962fa8ac","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:ea652ca5df8b512dc30df7114b91b21a8e6a83b9135d57638509ff5cec27105e","observation_id":"bfd433a0-e245-4c0a-870e-0319a7f843a2","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Bootstrap your own la- tent – a new approach to self-supervised learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:3c639163fcb68b731cec2d4b7d767de059202a7ca6acc41fbf8025dd27811700","observation_id":"454124a5-1911-4e5d-985b-3eb7f7d282b8","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"High- resolution piano transcription with pedals by regress- ing onset and offset times,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:b3bf0683a225f276820875b7d4ec010de8aed67f59cfcef7420620cdc1cef5ab","observation_id":"459e1e01-8ee8-4e67-a7f6-ee6d822d3b9a","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Deep networks with stochastic depth,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:7a7beb05bd8a659a9a840fe2fcee1deefc701b64a449303f27a6c272f3e2d66f","observation_id":"80a06ca2-9a22-4a6b-a44e-dea432f35661","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"ATEPP: A dataset of automatically tran- scribed expressive piano performance,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:6bea9dbeb438f5cbd07a6901c47f3aa3d9a43b4bd5dda239bb6c54a27113ffb9","observation_id":"94ff1960-d9f6-4a0f-9ac7-f8d913ec43a6","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"POP909: A pop-song dataset for music arrangement generation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:ca601dc6786cb504587cf1e925c2f2f20e2af75d099459c01e060650c3bc6477","observation_id":"898c1c9e-4049-4734-8ff3-589297a731c4","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"EMOPIA: A multi-modal pop piano dataset for emotion recognition and emotion-based music gen- eration,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:8a7da9b7cae52624bb7e96ae6379956810da9f2c6966013b8a56c4d8bdf78158","observation_id":"8e694ea8-7282-4ce0-b219-d1069cc2f3e4","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:fa8c828d09e8971cf110859ca61aab2f3dc551b362f4520440cc06b8a87692da","observation_id":"6ec27226-f024-4792-a299-fbf48c5b23a2","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Pianist8 dataset,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:3eccb9c1efbe8e7e737443a6889aa1691374eec3988f8c67d109141e5f60d7d9","observation_id":"87427eef-93e6-4765-911d-7a7c6ba58f8f","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Bachi: Boundary-aware symbolic chord recognition through masked iterative decoding on pop and classi- cal music,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:17ae70ae39b25f0426402ff70447d16b04b3abceac5ecc10f3cc8b52cc3c0257","observation_id":"6cc304ea-5d01-4af3-b51a-38622151636b","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Combining piano perfor- mance dimensions for score difficulty classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:ea6b84f7c6219cb75a131e24a5733ab87695a5e8aadcf276e5eab13da0efe1b5","observation_id":"7cd2e891-63df-45d0-8a86-86154d48ec39","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"ASAP: A dataset of aligned scores and performances for piano transcription,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:978cf75ca39a890c0076c6a00c32bbf6fb9646c11aaeb4fc491a25c8ab9d27eb","observation_id":"bd64dd76-50f2-49c3-957a-025361ca4c29","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"VICReg: Variance-invariance-covariance regularization for self- supervised learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:de78ebd2d5426e3cf10adc5270a197132df5a3667fd20a95e407b8195747ad15","observation_id":"2ca21946-69f1-424b-b8af-a96f6a04d7b2","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","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":"2026-07-14T01:07:08.513230Z","title":"Learning from reward-free offline data: A case for planning with latent dynamics mod- els,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T01:07:08.513230Z"},"links":{"citing_paper":"/paper/2607.10003"},"observation_digest":"sha256:63b2c1329beb87f04f4baf6f6897f6d7526d72d0a1ff6760ac9910ded59436c1","observation_id":"73e8eb01-b4cc-46b8-9501-d867f8e3f320","resolution":{"observed_at":"2026-07-14T01:07:08.513230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.10003","last_updated":"2026-07-10T22:02:00Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-09T17:04:53.257364Z","submitted_at":"2026-07-10T22:02:00Z","title":"ARIMA: Reconstruction-Grounded Predictive Representation Learning for Symbolic Music"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":34},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2607.10003."}