{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YB4KZCA3SJMCLNLMUHZ3MXIHQ7","short_pith_number":"pith:YB4KZCA3","schema_version":"1.0","canonical_sha256":"c078ac881b925825b56ca1f3b65d0787ca2128506b1b4fb2982c2349261f243e","source":{"kind":"arxiv","id":"2606.21434","version":1},"attestation_state":"computed","paper":{"title":"Universal Encoders for Modular Relational Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LG","authors_text":"Gustav \\v{S}\\'ir, Jakub Pele\\v{s}ka","submitted_at":"2026-06-19T13:49:47Z","abstract_excerpt":"Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs for end-to-end representation learning. While RDL is evolving rapidly, existing approaches face significant generalization obstacles. They are either schema-specific, requiring training from scratch for every new database, or they rely on monolithic architectures that entangle feature encoding with graph message-passing. Analyzing these limitations, we establish four core pillars for building foundational relational models: semantic granularity, structural topology, temporal causality, and unified op"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.21434","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T13:49:47Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"e54be16bf713e11c7e0fdcb3419783475da57f3143f92e3047daf3742005ee77","abstract_canon_sha256":"b196b7df0c8af41f2c8c3a385440bb7cd3a0b9a71f089a25e8f5278dafaa5e1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:13:10.039467Z","signature_b64":"oLS5TErmhm2pSHk+akXSq1hXWcHoDlpPZZIIIPTJCYD9px0oQLtvvRVkyFJ9++QNiSqYfbKYzVuo3UvSHoj0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c078ac881b925825b56ca1f3b65d0787ca2128506b1b4fb2982c2349261f243e","last_reissued_at":"2026-06-23T01:13:10.039018Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:13:10.039018Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Universal Encoders for Modular Relational Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.LG","authors_text":"Gustav \\v{S}\\'ir, Jakub Pele\\v{s}ka","submitted_at":"2026-06-19T13:49:47Z","abstract_excerpt":"Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs for end-to-end representation learning. While RDL is evolving rapidly, existing approaches face significant generalization obstacles. They are either schema-specific, requiring training from scratch for every new database, or they rely on monolithic architectures that entangle feature encoding with graph message-passing. Analyzing these limitations, we establish four core pillars for building foundational relational models: semantic granularity, structural topology, temporal causality, and unified op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21434","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/2606.21434/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.21434","created_at":"2026-06-23T01:13:10.039092+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21434v1","created_at":"2026-06-23T01:13:10.039092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21434","created_at":"2026-06-23T01:13:10.039092+00:00"},{"alias_kind":"pith_short_12","alias_value":"YB4KZCA3SJMC","created_at":"2026-06-23T01:13:10.039092+00:00"},{"alias_kind":"pith_short_16","alias_value":"YB4KZCA3SJMCLNLM","created_at":"2026-06-23T01:13:10.039092+00:00"},{"alias_kind":"pith_short_8","alias_value":"YB4KZCA3","created_at":"2026-06-23T01:13:10.039092+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7","json":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7.json","graph_json":"https://pith.science/api/pith-number/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/graph.json","events_json":"https://pith.science/api/pith-number/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/events.json","paper":"https://pith.science/paper/YB4KZCA3"},"agent_actions":{"view_html":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7","download_json":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7.json","view_paper":"https://pith.science/paper/YB4KZCA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21434&json=true","fetch_graph":"https://pith.science/api/pith-number/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/graph.json","fetch_events":"https://pith.science/api/pith-number/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/action/storage_attestation","attest_author":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/action/author_attestation","sign_citation":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/action/citation_signature","submit_replication":"https://pith.science/pith/YB4KZCA3SJMCLNLMUHZ3MXIHQ7/action/replication_record"}},"created_at":"2026-06-23T01:13:10.039092+00:00","updated_at":"2026-06-23T01:13:10.039092+00:00"}