{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MD2A4KYCCXV4R7LQBL5OKLDAJF","short_pith_number":"pith:MD2A4KYC","schema_version":"1.0","canonical_sha256":"60f40e2b0215ebc8fd700afae52c6049778007508a61abbc6018200a28dd7114","source":{"kind":"arxiv","id":"2608.05222","version":1},"attestation_state":"computed","paper":{"title":"Diff-Symbo: Text-Controlled Long-Duration Symbolic Music Generation Using Autoregressive Latent Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Boshi Tang, Fan Fan, Jun Chen, Weihao Wu, Yang Jing, Yaolong Ju, Zhiwei Lin, Zhiyong Wu","submitted_at":"2026-08-05T11:03:33Z","abstract_excerpt":"Text-controlled symbolic music generation has recently gained research attention due to its versatile, flexible and straightforward approach to music composition. However, previous approaches tend to generate symbolic music with compromising quality, diversity, controllability and limited duration. In this paper, we present Diff-Symbo, an innovative method that uses latent diffusion model (LDM) to generate high-quality, diverse and long-duration symbolic music. To address the lack of text-symbolic music dataset, we develop a comprehensive dataset with 19,345 text templates by employing large l"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2608.05222","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-08-05T11:03:33Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"c811e16a2ff87ecffc7b4aa0816fd74d84be15f8b333e823290081eb35ffea37","abstract_canon_sha256":"6046563c927431bee2b988b4256915bff098b2a5aa1e3014b7496670aa75cfcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:46:33.316838Z","signature_b64":"lkSxRjMPGxNwJ6vkbla/nhRcSr766ngUjBYtFNY1eslarVVG6gRxeiWgrvZ9mTVuYlkq5gjEA5Nlx55/cWgwBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60f40e2b0215ebc8fd700afae52c6049778007508a61abbc6018200a28dd7114","last_reissued_at":"2026-08-07T00:46:33.315301Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:46:33.315301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diff-Symbo: Text-Controlled Long-Duration Symbolic Music Generation Using Autoregressive Latent Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Boshi Tang, Fan Fan, Jun Chen, Weihao Wu, Yang Jing, Yaolong Ju, Zhiwei Lin, Zhiyong Wu","submitted_at":"2026-08-05T11:03:33Z","abstract_excerpt":"Text-controlled symbolic music generation has recently gained research attention due to its versatile, flexible and straightforward approach to music composition. However, previous approaches tend to generate symbolic music with compromising quality, diversity, controllability and limited duration. In this paper, we present Diff-Symbo, an innovative method that uses latent diffusion model (LDM) to generate high-quality, diverse and long-duration symbolic music. To address the lack of text-symbolic music dataset, we develop a comprehensive dataset with 19,345 text templates by employing large l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05222","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2608.05222/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2608.05222","created_at":"2026-08-07T00:46:33.316664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.05222v1","created_at":"2026-08-07T00:46:33.316664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05222","created_at":"2026-08-07T00:46:33.316664+00:00"},{"alias_kind":"pith_short_12","alias_value":"MD2A4KYCCXV4","created_at":"2026-08-07T00:46:33.316664+00:00"},{"alias_kind":"pith_short_16","alias_value":"MD2A4KYCCXV4R7LQ","created_at":"2026-08-07T00:46:33.316664+00:00"},{"alias_kind":"pith_short_8","alias_value":"MD2A4KYC","created_at":"2026-08-07T00:46:33.316664+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF","json":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF.json","graph_json":"https://pith.science/api/pith-number/MD2A4KYCCXV4R7LQBL5OKLDAJF/graph.json","events_json":"https://pith.science/api/pith-number/MD2A4KYCCXV4R7LQBL5OKLDAJF/events.json","paper":"https://pith.science/paper/MD2A4KYC"},"agent_actions":{"view_html":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF","download_json":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF.json","view_paper":"https://pith.science/paper/MD2A4KYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.05222&json=true","fetch_graph":"https://pith.science/api/pith-number/MD2A4KYCCXV4R7LQBL5OKLDAJF/graph.json","fetch_events":"https://pith.science/api/pith-number/MD2A4KYCCXV4R7LQBL5OKLDAJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF/action/storage_attestation","attest_author":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF/action/author_attestation","sign_citation":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF/action/citation_signature","submit_replication":"https://pith.science/pith/MD2A4KYCCXV4R7LQBL5OKLDAJF/action/replication_record"}},"created_at":"2026-08-07T00:46:33.316664+00:00","updated_at":"2026-08-07T00:46:33.316664+00:00"}