{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PQHXPO6NE6SATZT5FHXJPJON7I","short_pith_number":"pith:PQHXPO6N","schema_version":"1.0","canonical_sha256":"7c0f77bbcd27a409e67d29ee97a5cdfa3d882ed4927ddb2589609a0dd95cbc5f","source":{"kind":"arxiv","id":"2605.30195","version":1},"attestation_state":"computed","paper":{"title":"What drives performance in molecular MPNNs? An operator-level factorial benchmark","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Panyu Jiao, Runhai Ouyang, Shuizhou Chen, Wei Xie, Yiheng Shen, Yuyang Wang","submitted_at":"2026-05-28T16:34:53Z","abstract_excerpt":"Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how specific message-passing operators affect performance. We present an operator-level factorial benchmark that decomposes 2D molecular MPNNs into the three families of message-seed initialization, node-edge fusion, and node update operators. The resulting 84 configurations are benchmarked on ten MoleculeNet datasets under a shared experimental setup and statistical analysis protocol. Across this controlled design, performan"},"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":"2605.30195","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-28T16:34:53Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a6e9e29eae1a4192eedf593ceb0c91ec1c4f0d7cb8c060a3d00a38ee2ed7bf67","abstract_canon_sha256":"755eb603db49726ad97cd6568c1d47be482d4c91c04e6477bce45642f6cb6e3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:12.525180Z","signature_b64":"3plRc4SmqkalaP+uvUAKQfv9bgUuVyCyRtM73mgaZyJMlyqocX9lnrnqobEu7Zp/ZXjOX8nsUbQ7Xe38Oo2cCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c0f77bbcd27a409e67d29ee97a5cdfa3d882ed4927ddb2589609a0dd95cbc5f","last_reissued_at":"2026-05-29T02:06:12.524812Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:12.524812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What drives performance in molecular MPNNs? An operator-level factorial benchmark","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Panyu Jiao, Runhai Ouyang, Shuizhou Chen, Wei Xie, Yiheng Shen, Yuyang Wang","submitted_at":"2026-05-28T16:34:53Z","abstract_excerpt":"Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how specific message-passing operators affect performance. We present an operator-level factorial benchmark that decomposes 2D molecular MPNNs into the three families of message-seed initialization, node-edge fusion, and node update operators. The resulting 84 configurations are benchmarked on ten MoleculeNet datasets under a shared experimental setup and statistical analysis protocol. Across this controlled design, performan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30195","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/2605.30195/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":"2605.30195","created_at":"2026-05-29T02:06:12.524871+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30195v1","created_at":"2026-05-29T02:06:12.524871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30195","created_at":"2026-05-29T02:06:12.524871+00:00"},{"alias_kind":"pith_short_12","alias_value":"PQHXPO6NE6SA","created_at":"2026-05-29T02:06:12.524871+00:00"},{"alias_kind":"pith_short_16","alias_value":"PQHXPO6NE6SATZT5","created_at":"2026-05-29T02:06:12.524871+00:00"},{"alias_kind":"pith_short_8","alias_value":"PQHXPO6N","created_at":"2026-05-29T02:06:12.524871+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/PQHXPO6NE6SATZT5FHXJPJON7I","json":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I.json","graph_json":"https://pith.science/api/pith-number/PQHXPO6NE6SATZT5FHXJPJON7I/graph.json","events_json":"https://pith.science/api/pith-number/PQHXPO6NE6SATZT5FHXJPJON7I/events.json","paper":"https://pith.science/paper/PQHXPO6N"},"agent_actions":{"view_html":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I","download_json":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I.json","view_paper":"https://pith.science/paper/PQHXPO6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30195&json=true","fetch_graph":"https://pith.science/api/pith-number/PQHXPO6NE6SATZT5FHXJPJON7I/graph.json","fetch_events":"https://pith.science/api/pith-number/PQHXPO6NE6SATZT5FHXJPJON7I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I/action/storage_attestation","attest_author":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I/action/author_attestation","sign_citation":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I/action/citation_signature","submit_replication":"https://pith.science/pith/PQHXPO6NE6SATZT5FHXJPJON7I/action/replication_record"}},"created_at":"2026-05-29T02:06:12.524871+00:00","updated_at":"2026-05-29T02:06:12.524871+00:00"}