{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:EYSHON2YMCWBVSD5UBPNYRRUGV","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":"448fa62c8ace9adedcb2eaa2d3ba6e99c729d61020c2a980926054fe89a30c53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T15:24:36Z","title_canon_sha256":"02a1792f32ab913c7ae8553f5e4c238fe088763edd535dfedc76227bb0d1e7a2"},"schema_version":"1.0","source":{"id":"2606.07387","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07387","created_at":"2026-06-08T01:05:23Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07387v1","created_at":"2026-06-08T01:05:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07387","created_at":"2026-06-08T01:05:23Z"},{"alias_kind":"pith_short_12","alias_value":"EYSHON2YMCWB","created_at":"2026-06-08T01:05:23Z"},{"alias_kind":"pith_short_16","alias_value":"EYSHON2YMCWBVSD5","created_at":"2026-06-08T01:05:23Z"},{"alias_kind":"pith_short_8","alias_value":"EYSHON2Y","created_at":"2026-06-08T01:05:23Z"}],"graph_snapshots":[{"event_id":"sha256:489e8903e14d717b248f41340fe09584c7487ed3d80eb598f331bc3d8f16298b","target":"graph","created_at":"2026-06-08T01:05:23Z","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/2606.07387/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages. We propose \\textit{score-aware training}, which treats audio-caption alignment score as a direct supervision signal throughout the pipeline. Rather than discarding low-scoring segments, we repurpose them via a CLAP-conditioned Beta noise timestep schedule that routes them to high-noise training regimes, acting as an effective implicit regularizer. Complementarily, segment-level filtering re","authors_text":"Chih-Pin Tan, Tzu-Hung Huang, Yun-Chen Cheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T15:24:36Z","title":"Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07387","kind":"arxiv","version":1},"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":"sha256:49b5b63005abbbd0e59cb35577c4d242276fa5380619292fbc9c8f7a9dff4f67","target":"record","created_at":"2026-06-08T01:05:23Z","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":"448fa62c8ace9adedcb2eaa2d3ba6e99c729d61020c2a980926054fe89a30c53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T15:24:36Z","title_canon_sha256":"02a1792f32ab913c7ae8553f5e4c238fe088763edd535dfedc76227bb0d1e7a2"},"schema_version":"1.0","source":{"id":"2606.07387","kind":"arxiv","version":1}},"canonical_sha256":"262477375860ac1ac87da05edc4634354c9e85a272e18169dd38599140f7f853","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"262477375860ac1ac87da05edc4634354c9e85a272e18169dd38599140f7f853","first_computed_at":"2026-06-08T01:05:23.994229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:05:23.994229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0r9/4ZiKdMHVv61VMCUTymlvz022O7H+P/pKDNv+4dnpyxYBPe40Bd7ndJmkNMuXHfNxxw2NnnHOngcpH6yOCw==","signature_status":"signed_v1","signed_at":"2026-06-08T01:05:23.995088Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07387","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49b5b63005abbbd0e59cb35577c4d242276fa5380619292fbc9c8f7a9dff4f67","sha256:489e8903e14d717b248f41340fe09584c7487ed3d80eb598f331bc3d8f16298b"],"state_sha256":"f68078cff1dedc2f592515a6526c6bc1bdb3799cedc50901aba388cc241b03ac"}