{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:J54ITGKTIZ5C2Y2DLMQCJPWVN2","short_pith_number":"pith:J54ITGKT","canonical_record":{"source":{"id":"2310.05128","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T11:36:45Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f942f650fbb3de3b4cf08d095376964a4d2483183ffccf74e970dc9efe8321f3","abstract_canon_sha256":"a41265650f65c88d6da93f9160986e06d11360d7771703b772cd144fb515bb66"},"schema_version":"1.0"},"canonical_sha256":"4f78899953467a2d63435b2024bed56e8a6420b20a774ebc3ea42b368009a082","source":{"kind":"arxiv","id":"2310.05128","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.05128","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"arxiv_version","alias_value":"2310.05128v3","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.05128","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_12","alias_value":"J54ITGKTIZ5C","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_16","alias_value":"J54ITGKTIZ5C2Y2D","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_8","alias_value":"J54ITGKT","created_at":"2026-07-05T08:34:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:J54ITGKTIZ5C2Y2DLMQCJPWVN2","target":"record","payload":{"canonical_record":{"source":{"id":"2310.05128","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T11:36:45Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f942f650fbb3de3b4cf08d095376964a4d2483183ffccf74e970dc9efe8321f3","abstract_canon_sha256":"a41265650f65c88d6da93f9160986e06d11360d7771703b772cd144fb515bb66"},"schema_version":"1.0"},"canonical_sha256":"4f78899953467a2d63435b2024bed56e8a6420b20a774ebc3ea42b368009a082","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:34:06.686462Z","signature_b64":"oBpKFcEsabVlUd6eLC+6c1io8u2y+4vQG6We2DURoY0h3jgnT7zRrq/1XjxJwn1ZBR5g5KRFuYbI6m61fngcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f78899953467a2d63435b2024bed56e8a6420b20a774ebc3ea42b368009a082","last_reissued_at":"2026-07-05T08:34:06.685972Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:34:06.685972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.05128","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:34:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ied3mFIwF4psZ/qheIRycaCFQSHg7XAbckJlDwx6ZR6nx2Luk4nQGNTWLAqJ+haYM6g7DM7ZJJjo/T8Oui6DDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T15:21:06.776068Z"},"content_sha256":"2f8c3ccd3d93005edb08d9a5ebd7fb62aa8d749fb41169af95480b74fabc3069","schema_version":"1.0","event_id":"sha256:2f8c3ccd3d93005edb08d9a5ebd7fb62aa8d749fb41169af95480b74fabc3069"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:J54ITGKTIZ5C2Y2DLMQCJPWVN2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Jeff Z. Pan, Jie He, Simon Yu, V\\'ictor Guti\\'errez-Basulto","submitted_at":"2023-10-08T11:36:45Z","abstract_excerpt":"Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an over-constrained premise on the output space by using contrastive learning on generated samples in a semi-supervised manner to bring text and label embeddings closer. However, the generation of samples tends to introduce noise as it ignores the correlation between similar samples in the same batch. One solution to this issue is supervised contrastive learning, but it remains an underexplored topic in HMTC due to it"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.05128","kind":"arxiv","version":3},"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/2310.05128/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":"2026-07-05T08:34:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o9JLNiv9/HNiIL0B6BMr4qcJpX1/d32b0HCILKczO8iUc7wq844W7+qm3BxcGi1ggQcXsi37YZrajO58bNnHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T15:21:06.776593Z"},"content_sha256":"2b8ecde5317cc23414c5c111c9eaa4b6dcb3c5ac1e323d2f8cd804f5dd596880","schema_version":"1.0","event_id":"sha256:2b8ecde5317cc23414c5c111c9eaa4b6dcb3c5ac1e323d2f8cd804f5dd596880"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/bundle.json","state_url":"https://pith.science/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/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":"2026-08-12T15:21:06Z","links":{"resolver":"https://pith.science/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2","bundle":"https://pith.science/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/bundle.json","state":"https://pith.science/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J54ITGKTIZ5C2Y2DLMQCJPWVN2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:J54ITGKTIZ5C2Y2DLMQCJPWVN2","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":"a41265650f65c88d6da93f9160986e06d11360d7771703b772cd144fb515bb66","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T11:36:45Z","title_canon_sha256":"f942f650fbb3de3b4cf08d095376964a4d2483183ffccf74e970dc9efe8321f3"},"schema_version":"1.0","source":{"id":"2310.05128","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.05128","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"arxiv_version","alias_value":"2310.05128v3","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.05128","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_12","alias_value":"J54ITGKTIZ5C","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_16","alias_value":"J54ITGKTIZ5C2Y2D","created_at":"2026-07-05T08:34:06Z"},{"alias_kind":"pith_short_8","alias_value":"J54ITGKT","created_at":"2026-07-05T08:34:06Z"}],"graph_snapshots":[{"event_id":"sha256:2b8ecde5317cc23414c5c111c9eaa4b6dcb3c5ac1e323d2f8cd804f5dd596880","target":"graph","created_at":"2026-07-05T08:34:06Z","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/2310.05128/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an over-constrained premise on the output space by using contrastive learning on generated samples in a semi-supervised manner to bring text and label embeddings closer. However, the generation of samples tends to introduce noise as it ignores the correlation between similar samples in the same batch. One solution to this issue is supervised contrastive learning, but it remains an underexplored topic in HMTC due to it","authors_text":"Jeff Z. Pan, Jie He, Simon Yu, V\\'ictor Guti\\'errez-Basulto","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T11:36:45Z","title":"Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.05128","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":"sha256:2f8c3ccd3d93005edb08d9a5ebd7fb62aa8d749fb41169af95480b74fabc3069","target":"record","created_at":"2026-07-05T08:34:06Z","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":"a41265650f65c88d6da93f9160986e06d11360d7771703b772cd144fb515bb66","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-08T11:36:45Z","title_canon_sha256":"f942f650fbb3de3b4cf08d095376964a4d2483183ffccf74e970dc9efe8321f3"},"schema_version":"1.0","source":{"id":"2310.05128","kind":"arxiv","version":3}},"canonical_sha256":"4f78899953467a2d63435b2024bed56e8a6420b20a774ebc3ea42b368009a082","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f78899953467a2d63435b2024bed56e8a6420b20a774ebc3ea42b368009a082","first_computed_at":"2026-07-05T08:34:06.685972Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:34:06.685972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oBpKFcEsabVlUd6eLC+6c1io8u2y+4vQG6We2DURoY0h3jgnT7zRrq/1XjxJwn1ZBR5g5KRFuYbI6m61fngcAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:34:06.686462Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.05128","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f8c3ccd3d93005edb08d9a5ebd7fb62aa8d749fb41169af95480b74fabc3069","sha256:2b8ecde5317cc23414c5c111c9eaa4b6dcb3c5ac1e323d2f8cd804f5dd596880"],"state_sha256":"f9053ab5c0fa6de6d6e3a3e20f10f1ce78c99310e543d8a7bd12b1468d1ea00d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"72JFslFBR4IWZaFrzNg4R8hlfjF43K95NLOrlaMNdtL74Z4pGnYC926bkOHUEz3HD5f1JNY8BW66xehE/G9nAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T15:21:06.780705Z","bundle_sha256":"6df9befa4f2c8b83bdf3ce10c3908810195f998339ea5cf4e3ffdf6984c07403"}}