{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QJKFUVHWX3MGWL6725STX4GXJL","short_pith_number":"pith:QJKFUVHW","canonical_record":{"source":{"id":"2309.10738","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-09-19T16:34:24Z","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"title_canon_sha256":"5998b3b5be4b9668e6f995aa9a3d7881e868ffa5c8effc7e0a23a207e6e3c050","abstract_canon_sha256":"62b18252930ec708ed8643b3cf45ab5e93c6a344ae0b0c014a02a0e456250467"},"schema_version":"1.0"},"canonical_sha256":"82545a54f6bed86b2fdfd7653bf0d74aed9a6bb65896f3d5eab4ebcea76628f6","source":{"kind":"arxiv","id":"2309.10738","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10738","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10738v2","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10738","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"QJKFUVHWX3MG","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"QJKFUVHWX3MGWL67","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"QJKFUVHW","created_at":"2026-07-05T06:52:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QJKFUVHWX3MGWL6725STX4GXJL","target":"record","payload":{"canonical_record":{"source":{"id":"2309.10738","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-09-19T16:34:24Z","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"title_canon_sha256":"5998b3b5be4b9668e6f995aa9a3d7881e868ffa5c8effc7e0a23a207e6e3c050","abstract_canon_sha256":"62b18252930ec708ed8643b3cf45ab5e93c6a344ae0b0c014a02a0e456250467"},"schema_version":"1.0"},"canonical_sha256":"82545a54f6bed86b2fdfd7653bf0d74aed9a6bb65896f3d5eab4ebcea76628f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:52:26.883523Z","signature_b64":"MPWUlJXByH4Cw10Auqi9UyRXGkv4rrmCijR7Tr5QmPuVKhZ59X6/LKnsf53yqOEvP1JFufoTnUhALdy+rB4xDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82545a54f6bed86b2fdfd7653bf0d74aed9a6bb65896f3d5eab4ebcea76628f6","last_reissued_at":"2026-07-05T06:52:26.883049Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:52:26.883049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.10738","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rqqasncpni6DDjUcf2xOalGCdn7wcInQM+tLJ1H6iv/hyrmtk/m/jRtZ4OVHmoiW1kbx/grNX3wq04iIFKIbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:46:12.667144Z"},"content_sha256":"46b1bd390cdca91b9688cb406187fe1e2d3dd5e4f01b03d3879d56d854b9c4fd","schema_version":"1.0","event_id":"sha256:46b1bd390cdca91b9688cb406187fe1e2d3dd5e4f01b03d3879d56d854b9c4fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QJKFUVHWX3MGWL6725STX4GXJL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Jiaxing Yu, Kejun Zhang, Lingyun Sun, Tieyao Zhang, Xinda Wu, Xu Tan, Zhijie Huang, Zihao Wang","submitted_at":"2023-09-19T16:34:24Z","abstract_excerpt":"Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10738","kind":"arxiv","version":2},"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/2309.10738/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:52:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oBwRUQBCjHWw3fvL71cfHOUZC6MLgk81NDyWTKMFvSXGio9trZuhkyy00GI1wa6h9+QUfjm0YKSeKeR8cdRSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:46:12.667775Z"},"content_sha256":"853a404fc7385926774ca7d01a0692132c1ea1755acbb7a927505e725669b28b","schema_version":"1.0","event_id":"sha256:853a404fc7385926774ca7d01a0692132c1ea1755acbb7a927505e725669b28b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QJKFUVHWX3MGWL6725STX4GXJL/bundle.json","state_url":"https://pith.science/pith/QJKFUVHWX3MGWL6725STX4GXJL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QJKFUVHWX3MGWL6725STX4GXJL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T02:46:12Z","links":{"resolver":"https://pith.science/pith/QJKFUVHWX3MGWL6725STX4GXJL","bundle":"https://pith.science/pith/QJKFUVHWX3MGWL6725STX4GXJL/bundle.json","state":"https://pith.science/pith/QJKFUVHWX3MGWL6725STX4GXJL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QJKFUVHWX3MGWL6725STX4GXJL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QJKFUVHWX3MGWL6725STX4GXJL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"62b18252930ec708ed8643b3cf45ab5e93c6a344ae0b0c014a02a0e456250467","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-09-19T16:34:24Z","title_canon_sha256":"5998b3b5be4b9668e6f995aa9a3d7881e868ffa5c8effc7e0a23a207e6e3c050"},"schema_version":"1.0","source":{"id":"2309.10738","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10738","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10738v2","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10738","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_12","alias_value":"QJKFUVHWX3MG","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_16","alias_value":"QJKFUVHWX3MGWL67","created_at":"2026-07-05T06:52:26Z"},{"alias_kind":"pith_short_8","alias_value":"QJKFUVHW","created_at":"2026-07-05T06:52:26Z"}],"graph_snapshots":[{"event_id":"sha256:853a404fc7385926774ca7d01a0692132c1ea1755acbb7a927505e725669b28b","target":"graph","created_at":"2026-07-05T06:52:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.10738/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram a","authors_text":"Jiaxing Yu, Kejun Zhang, Lingyun Sun, Tieyao Zhang, Xinda Wu, Xu Tan, Zhijie Huang, Zihao Wang","cross_cats":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-09-19T16:34:24Z","title":"MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10738","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:46b1bd390cdca91b9688cb406187fe1e2d3dd5e4f01b03d3879d56d854b9c4fd","target":"record","created_at":"2026-07-05T06:52:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"62b18252930ec708ed8643b3cf45ab5e93c6a344ae0b0c014a02a0e456250467","cross_cats_sorted":["cs.AI","cs.CL","cs.IR","cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-09-19T16:34:24Z","title_canon_sha256":"5998b3b5be4b9668e6f995aa9a3d7881e868ffa5c8effc7e0a23a207e6e3c050"},"schema_version":"1.0","source":{"id":"2309.10738","kind":"arxiv","version":2}},"canonical_sha256":"82545a54f6bed86b2fdfd7653bf0d74aed9a6bb65896f3d5eab4ebcea76628f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82545a54f6bed86b2fdfd7653bf0d74aed9a6bb65896f3d5eab4ebcea76628f6","first_computed_at":"2026-07-05T06:52:26.883049Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:52:26.883049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MPWUlJXByH4Cw10Auqi9UyRXGkv4rrmCijR7Tr5QmPuVKhZ59X6/LKnsf53yqOEvP1JFufoTnUhALdy+rB4xDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:52:26.883523Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.10738","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:46b1bd390cdca91b9688cb406187fe1e2d3dd5e4f01b03d3879d56d854b9c4fd","sha256:853a404fc7385926774ca7d01a0692132c1ea1755acbb7a927505e725669b28b"],"state_sha256":"4d26f02e13072537d2c9d08c88666976e0524157434981c104f03a3b2c536a67"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t+wJ1VBhrnJjscnDUYbXphtfeU26UYgpjdOP7fhk79cCI/GCQatgtVqFXDDe5HWPPwS20Q8VMRMJWuTn5y4DBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T02:46:12.673966Z","bundle_sha256":"2ecfb1768a789a3a3e2f349480942948ff406160d31a1465566f0e57c1c04e34"}}