{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4C2QRWCJ3JBAQYCDPBECRNIOS6","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":"41356b60a4505507d5b2dfb11a44aceb748676a79e0074c597ddab23d3b1a4f7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T17:41:44Z","title_canon_sha256":"57519c94b94341e80f3cd950d34f9355462947765b442624d07434a6689d9672"},"schema_version":"1.0","source":{"id":"2411.17629","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.17629","created_at":"2026-07-05T09:56:37Z"},{"alias_kind":"arxiv_version","alias_value":"2411.17629v2","created_at":"2026-07-05T09:56:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17629","created_at":"2026-07-05T09:56:37Z"},{"alias_kind":"pith_short_12","alias_value":"4C2QRWCJ3JBA","created_at":"2026-07-05T09:56:37Z"},{"alias_kind":"pith_short_16","alias_value":"4C2QRWCJ3JBAQYCD","created_at":"2026-07-05T09:56:37Z"},{"alias_kind":"pith_short_8","alias_value":"4C2QRWCJ","created_at":"2026-07-05T09:56:37Z"}],"graph_snapshots":[{"event_id":"sha256:347504df58bf6e91655bb547960247a9f16946fbc00d1abb34f603b1cef858da","target":"graph","created_at":"2026-07-05T09:56:37Z","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/2411.17629/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Organic synthesis stands as a cornerstone of the chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or direct adaptations of model architectures from other domains, which lack feasibility as data scales increase or ignore the rich chemical information inherent in reactions. To address these issues, this paper introduces RAlign, a novel chemical reaction representation learning model for various organic reaction-related tasks. By integrating ato","authors_text":"Kaipeng Zeng, Xianbin Liu, Xiaokang Yang, Yanyan Xu, Yaohui Jin, Yu Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T17:41:44Z","title":"Learning Chemical Reaction Representation with Reactant-Product Alignment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17629","kind":"arxiv","version":2},"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":"sha256:943511273e97fdf0956975be22814cf255bbb8c2a1d5d672a123ff4de08cad34","target":"record","created_at":"2026-07-05T09:56:37Z","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":"41356b60a4505507d5b2dfb11a44aceb748676a79e0074c597ddab23d3b1a4f7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-26T17:41:44Z","title_canon_sha256":"57519c94b94341e80f3cd950d34f9355462947765b442624d07434a6689d9672"},"schema_version":"1.0","source":{"id":"2411.17629","kind":"arxiv","version":2}},"canonical_sha256":"e0b508d849da42086043784828b50e97aca7a2e0a5eb9298ba49d73936a23832","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0b508d849da42086043784828b50e97aca7a2e0a5eb9298ba49d73936a23832","first_computed_at":"2026-07-05T09:56:37.844417Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:37.844417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ep2IlBLtuw6vCEmHRfiMQ4gXkOG57RGfnAO0tLjXvUT39v7gM2ZT7iebzws/urhxaZODblRL6IUNfGb4GtoaAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:37.844961Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.17629","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:943511273e97fdf0956975be22814cf255bbb8c2a1d5d672a123ff4de08cad34","sha256:347504df58bf6e91655bb547960247a9f16946fbc00d1abb34f603b1cef858da"],"state_sha256":"479ad010e1bfe9b0ab22cf513555e3d6a8910ecdf202f6c3c734bc9428a82ac6"}