{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TBVLVXJ6LHWLPWJRJ6T5RBPJDV","short_pith_number":"pith:TBVLVXJ6","canonical_record":{"source":{"id":"2605.23241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T05:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"cb0b9c518a474115a180ad9475a04fb91aad8bd7f97d601e80f32ac4fadc132f","abstract_canon_sha256":"8e804b02681d397298a0a6888180456e1d56a07a6ada5d9cae58c75754024983"},"schema_version":"1.0"},"canonical_sha256":"986abadd3e59ecb7d9314fa7d885e91d6a8248bcecb1f282a5676f17023bd5f7","source":{"kind":"arxiv","id":"2605.23241","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23241","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23241v1","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23241","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"TBVLVXJ6LHWL","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"TBVLVXJ6LHWLPWJR","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"TBVLVXJ6","created_at":"2026-05-25T02:01:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TBVLVXJ6LHWLPWJRJ6T5RBPJDV","target":"record","payload":{"canonical_record":{"source":{"id":"2605.23241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T05:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"cb0b9c518a474115a180ad9475a04fb91aad8bd7f97d601e80f32ac4fadc132f","abstract_canon_sha256":"8e804b02681d397298a0a6888180456e1d56a07a6ada5d9cae58c75754024983"},"schema_version":"1.0"},"canonical_sha256":"986abadd3e59ecb7d9314fa7d885e91d6a8248bcecb1f282a5676f17023bd5f7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:45.290958Z","signature_b64":"Z56xRycTOOjcdSLv8+Nj09haNZPuRSepX26LnyxXN8NQDb+uRvY64txZ6OpeHhpXpiS6n9abLf91VRR3qnkZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"986abadd3e59ecb7d9314fa7d885e91d6a8248bcecb1f282a5676f17023bd5f7","last_reissued_at":"2026-05-25T02:01:45.290384Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:45.290384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.23241","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3d/dhfuV789V9WDFUVqC9L7KqS6KuCqDPwBg4WLwFSr6sTV500mYJsAqiSHcNUFg6CfwCniK/edRyo1tKAAZCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T23:49:40.127262Z"},"content_sha256":"36aed26a12bc15be434e33d8d046f0d1341f0277f7e90f0e4c4374b62ef458d3","schema_version":"1.0","event_id":"sha256:36aed26a12bc15be434e33d8d046f0d1341f0277f7e90f0e4c4374b62ef458d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TBVLVXJ6LHWLPWJRJ6T5RBPJDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Cheng Yang, Chuan Shi, Hanyang Peng, Jinyu Yang, Junze Chen, Muhan Zhang, Zedi Liu","submitted_at":"2026-05-22T05:19:58Z","abstract_excerpt":"Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end prediction by converting RDBs into graphs, where rows are represented as nodes and inter-table interactions are represented as edges, and then applying graph-based models for representation learning. Despite the strong capability of RDL, effective self-supervised pre-training for RDBs remains non-trivial. RDB tasks often require multi-faceted information across different perspectives and granularities. For example, user "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23241","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/2605.23241/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":"2026-05-25T02:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ANU37tNZechCpgWivPWzsi9Rlq87Fr5q1lWSkFP37oGIg8j0POFlAfzmlAZHDV88u16o83NF5473Rdwi3JIPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T23:49:40.128060Z"},"content_sha256":"6603bcbb106bc89ed92e8baccdeceb44802f8b0f42d3c3a3f7bf645640e37592","schema_version":"1.0","event_id":"sha256:6603bcbb106bc89ed92e8baccdeceb44802f8b0f42d3c3a3f7bf645640e37592"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/bundle.json","state_url":"https://pith.science/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/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":"2026-05-26T23:49:40Z","links":{"resolver":"https://pith.science/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV","bundle":"https://pith.science/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/bundle.json","state":"https://pith.science/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TBVLVXJ6LHWLPWJRJ6T5RBPJDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TBVLVXJ6LHWLPWJRJ6T5RBPJDV","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":"8e804b02681d397298a0a6888180456e1d56a07a6ada5d9cae58c75754024983","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T05:19:58Z","title_canon_sha256":"cb0b9c518a474115a180ad9475a04fb91aad8bd7f97d601e80f32ac4fadc132f"},"schema_version":"1.0","source":{"id":"2605.23241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23241","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23241v1","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23241","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"TBVLVXJ6LHWL","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"TBVLVXJ6LHWLPWJR","created_at":"2026-05-25T02:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"TBVLVXJ6","created_at":"2026-05-25T02:01:45Z"}],"graph_snapshots":[{"event_id":"sha256:6603bcbb106bc89ed92e8baccdeceb44802f8b0f42d3c3a3f7bf645640e37592","target":"graph","created_at":"2026-05-25T02:01:45Z","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/2605.23241/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) methods enable end-to-end prediction by converting RDBs into graphs, where rows are represented as nodes and inter-table interactions are represented as edges, and then applying graph-based models for representation learning. Despite the strong capability of RDL, effective self-supervised pre-training for RDBs remains non-trivial. RDB tasks often require multi-faceted information across different perspectives and granularities. For example, user ","authors_text":"Cheng Yang, Chuan Shi, Hanyang Peng, Jinyu Yang, Junze Chen, Muhan Zhang, Zedi Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T05:19:58Z","title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23241","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":"sha256:36aed26a12bc15be434e33d8d046f0d1341f0277f7e90f0e4c4374b62ef458d3","target":"record","created_at":"2026-05-25T02:01:45Z","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":"8e804b02681d397298a0a6888180456e1d56a07a6ada5d9cae58c75754024983","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T05:19:58Z","title_canon_sha256":"cb0b9c518a474115a180ad9475a04fb91aad8bd7f97d601e80f32ac4fadc132f"},"schema_version":"1.0","source":{"id":"2605.23241","kind":"arxiv","version":1}},"canonical_sha256":"986abadd3e59ecb7d9314fa7d885e91d6a8248bcecb1f282a5676f17023bd5f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"986abadd3e59ecb7d9314fa7d885e91d6a8248bcecb1f282a5676f17023bd5f7","first_computed_at":"2026-05-25T02:01:45.290384Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:45.290384Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z56xRycTOOjcdSLv8+Nj09haNZPuRSepX26LnyxXN8NQDb+uRvY64txZ6OpeHhpXpiS6n9abLf91VRR3qnkZAg==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:45.290958Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.23241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36aed26a12bc15be434e33d8d046f0d1341f0277f7e90f0e4c4374b62ef458d3","sha256:6603bcbb106bc89ed92e8baccdeceb44802f8b0f42d3c3a3f7bf645640e37592"],"state_sha256":"03081096b22d8d3607c52d771665bcd086fb3b4fcb4de251472f750f047e67ed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"anKLFIhUPsJaM7XyZdC7t67eFGf9zCKDSMszg8spAFhbAHF5x976cW6IEpnQADt/XVJPCEIeSJkr50BaGmY/Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T23:49:40.131998Z","bundle_sha256":"6b9f7103f206c4847d0766488857674ef64ed5c282554304c9ee00de1442ca2e"}}