{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R3EDN5WG4PVBS33UAULFFZNSN7","short_pith_number":"pith:R3EDN5WG","schema_version":"1.0","canonical_sha256":"8ec836f6c6e3ea196f74051652e5b26ff8c2e64ddb0a3d181574efbaa9eec9a4","source":{"kind":"arxiv","id":"2606.05116","version":1},"attestation_state":"computed","paper":{"title":"Graph Set Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Baoquan Chen, Daniel Probst, Jose E. Escrig Molina","submitted_at":"2026-06-03T17:20:48Z","abstract_excerpt":"We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide context as well as local structure. Existing architectures, including DeepSets and SetTransformer, require pre-encoded graph embeddings from a separate GNN, creating a bottleneck between feature extraction and set-level contextualisation. In contrast, GST interleaves node-level feature propagation and cross-graph contextual modelling at every layer, fusing the two levels of information through a gating mechanism. We e"},"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":"2606.05116","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T17:20:48Z","cross_cats_sorted":[],"title_canon_sha256":"9f358387fbb2e6f74c96bb08b4c1084022b0fb46dc36ab3235fefc8afef7a348","abstract_canon_sha256":"25bfc167a06dc2f918796a8b18c3a944045f2a4bbb7fddcc63c16fbec9c7b69f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:10:06.913916Z","signature_b64":"ap8VFgEJODSp+TGgeWn+eQVQYaj6w6qqN6b6NOmG5t55tSGyu7nySCTZG1fh/AVhBbcIr66WeAjWgzDKqIUkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ec836f6c6e3ea196f74051652e5b26ff8c2e64ddb0a3d181574efbaa9eec9a4","last_reissued_at":"2026-06-04T01:10:06.913282Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:10:06.913282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Set Transformer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Baoquan Chen, Daniel Probst, Jose E. Escrig Molina","submitted_at":"2026-06-03T17:20:48Z","abstract_excerpt":"We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide context as well as local structure. Existing architectures, including DeepSets and SetTransformer, require pre-encoded graph embeddings from a separate GNN, creating a bottleneck between feature extraction and set-level contextualisation. In contrast, GST interleaves node-level feature propagation and cross-graph contextual modelling at every layer, fusing the two levels of information through a gating mechanism. We e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05116","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/2606.05116/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":"2606.05116","created_at":"2026-06-04T01:10:06.913390+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05116v1","created_at":"2026-06-04T01:10:06.913390+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05116","created_at":"2026-06-04T01:10:06.913390+00:00"},{"alias_kind":"pith_short_12","alias_value":"R3EDN5WG4PVB","created_at":"2026-06-04T01:10:06.913390+00:00"},{"alias_kind":"pith_short_16","alias_value":"R3EDN5WG4PVBS33U","created_at":"2026-06-04T01:10:06.913390+00:00"},{"alias_kind":"pith_short_8","alias_value":"R3EDN5WG","created_at":"2026-06-04T01:10:06.913390+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/R3EDN5WG4PVBS33UAULFFZNSN7","json":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7.json","graph_json":"https://pith.science/api/pith-number/R3EDN5WG4PVBS33UAULFFZNSN7/graph.json","events_json":"https://pith.science/api/pith-number/R3EDN5WG4PVBS33UAULFFZNSN7/events.json","paper":"https://pith.science/paper/R3EDN5WG"},"agent_actions":{"view_html":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7","download_json":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7.json","view_paper":"https://pith.science/paper/R3EDN5WG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05116&json=true","fetch_graph":"https://pith.science/api/pith-number/R3EDN5WG4PVBS33UAULFFZNSN7/graph.json","fetch_events":"https://pith.science/api/pith-number/R3EDN5WG4PVBS33UAULFFZNSN7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7/action/storage_attestation","attest_author":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7/action/author_attestation","sign_citation":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7/action/citation_signature","submit_replication":"https://pith.science/pith/R3EDN5WG4PVBS33UAULFFZNSN7/action/replication_record"}},"created_at":"2026-06-04T01:10:06.913390+00:00","updated_at":"2026-06-04T01:10:06.913390+00:00"}