{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WPRFT3BBE7VJHPFY7BQJJBGSEW","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":"bca07aa5bf66b6c60db38a5614bf2938dfb0d2f4f6d88a6a43bb0473a76c7577","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-10-02T23:10:21Z","title_canon_sha256":"20fafaf52cc47df554013266657a3ccd335705f61dfebd0a6b6ebb50f9792af4"},"schema_version":"1.0","source":{"id":"2410.02084","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02084","created_at":"2026-07-05T11:22:16Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02084v3","created_at":"2026-07-05T11:22:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02084","created_at":"2026-07-05T11:22:16Z"},{"alias_kind":"pith_short_12","alias_value":"WPRFT3BBE7VJ","created_at":"2026-07-05T11:22:16Z"},{"alias_kind":"pith_short_16","alias_value":"WPRFT3BBE7VJHPFY","created_at":"2026-07-05T11:22:16Z"},{"alias_kind":"pith_short_8","alias_value":"WPRFT3BB","created_at":"2026-07-05T11:22:16Z"}],"graph_snapshots":[{"event_id":"sha256:e65b56a7120f97f20c24aa0537f92db7c02114ff5069f741003a0d571b341378","target":"graph","created_at":"2026-07-05T11:22:16Z","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/2410.02084/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent years have seen many audio-domain text-to-music generation models that rely on large amounts of text-audio pairs for training. However, symbolic-domain controllable music generation has lagged behind partly due to the lack of a large-scale symbolic music dataset with extensive metadata and captions. In this work, we present MetaScore, a new dataset consisting of 963K musical scores paired with rich metadata, including free-form user-annotated tags, collected from an online music forum. To approach text-to-music generation, We employ a pretrained large language model (LLM) to generate ps","authors_text":"Hao-Wen Dong, Julian McAuley, Shlomo Dubnov, Taylor Berg-Kirkpatrick, Weihan Xu","cross_cats":["eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-10-02T23:10:21Z","title":"Generating Symbolic Music from Natural Language Prompts using an LLM-Enhanced Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02084","kind":"arxiv","version":3},"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":"sha256:93094d0df0ffa566da61284155ad9a7671113ca719dcc95727494739f8daed5e","target":"record","created_at":"2026-07-05T11:22:16Z","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":"bca07aa5bf66b6c60db38a5614bf2938dfb0d2f4f6d88a6a43bb0473a76c7577","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-10-02T23:10:21Z","title_canon_sha256":"20fafaf52cc47df554013266657a3ccd335705f61dfebd0a6b6ebb50f9792af4"},"schema_version":"1.0","source":{"id":"2410.02084","kind":"arxiv","version":3}},"canonical_sha256":"b3e259ec2127ea93bcb8f8609484d225ac225157ba6a25358a5db02629767e25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3e259ec2127ea93bcb8f8609484d225ac225157ba6a25358a5db02629767e25","first_computed_at":"2026-07-05T11:22:16.693933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:16.693933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nXnhA4/fNslWuQcc79gSgn0U85V8Fp7eVNwYPdC2L6Daky9qD4ki9AlwJQ4q1PwhMRuWCui0+fZA+NBWTnRpBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:16.694442Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.02084","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93094d0df0ffa566da61284155ad9a7671113ca719dcc95727494739f8daed5e","sha256:e65b56a7120f97f20c24aa0537f92db7c02114ff5069f741003a0d571b341378"],"state_sha256":"ca4d7eb31dc87c31a61c4817db1b8ff90abd67276a33da0a88d2811252117771"}