{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:75FTMG6VJZG3OCRFPCANL747WZ","short_pith_number":"pith:75FTMG6V","schema_version":"1.0","canonical_sha256":"ff4b361bd54e4db70a257880d5ff9fb67fdcc07284ee92bd349e5d2dfacf9433","source":{"kind":"arxiv","id":"2605.28487","version":1},"attestation_state":"computed","paper":{"title":"ProvMind: Provenance-grounded reasoning for materials synthesis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Koji Tsuda, Ryo Tamura, Yiming Zhang","submitted_at":"2026-05-27T13:44:24Z","abstract_excerpt":"Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text or ordered steps. We introduce MatProcBench, a provenance-grounded benchmark constructed from literature-mined MatPROV graphs, to evaluate seven process-reasoning tasks spanning route continuity, step-level variable inference and global causal consistency under both same-split and shift-aware evaluation, including a strict dual-OOD split that combines temporal and material-class shift. We further introduce ProvMind,"},"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":"2605.28487","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-27T13:44:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0037c5be9fa7eeeb1d0922c72eb6623dfce8948e42b99dbcdd6b627394ef83a6","abstract_canon_sha256":"4ccb24da1c1a374ef904de139a416f4e5a7839b63e402e7c9dacc97e94613f8b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:54.339213Z","signature_b64":"gdmbOl8zBNAhg812DCTzQgMPkzF9PtWEAZ4so9i/XtDmhZAvdcILdJWvENuMci6/w8PSTR/iKDY2j0Ajzfi3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff4b361bd54e4db70a257880d5ff9fb67fdcc07284ee92bd349e5d2dfacf9433","last_reissued_at":"2026-05-28T02:04:54.338810Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:54.338810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ProvMind: Provenance-grounded reasoning for materials synthesis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Koji Tsuda, Ryo Tamura, Yiming Zhang","submitted_at":"2026-05-27T13:44:24Z","abstract_excerpt":"Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text or ordered steps. We introduce MatProcBench, a provenance-grounded benchmark constructed from literature-mined MatPROV graphs, to evaluate seven process-reasoning tasks spanning route continuity, step-level variable inference and global causal consistency under both same-split and shift-aware evaluation, including a strict dual-OOD split that combines temporal and material-class shift. We further introduce ProvMind,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28487","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/2605.28487/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":"2605.28487","created_at":"2026-05-28T02:04:54.338872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28487v1","created_at":"2026-05-28T02:04:54.338872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28487","created_at":"2026-05-28T02:04:54.338872+00:00"},{"alias_kind":"pith_short_12","alias_value":"75FTMG6VJZG3","created_at":"2026-05-28T02:04:54.338872+00:00"},{"alias_kind":"pith_short_16","alias_value":"75FTMG6VJZG3OCRF","created_at":"2026-05-28T02:04:54.338872+00:00"},{"alias_kind":"pith_short_8","alias_value":"75FTMG6V","created_at":"2026-05-28T02:04:54.338872+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/75FTMG6VJZG3OCRFPCANL747WZ","json":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ.json","graph_json":"https://pith.science/api/pith-number/75FTMG6VJZG3OCRFPCANL747WZ/graph.json","events_json":"https://pith.science/api/pith-number/75FTMG6VJZG3OCRFPCANL747WZ/events.json","paper":"https://pith.science/paper/75FTMG6V"},"agent_actions":{"view_html":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ","download_json":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ.json","view_paper":"https://pith.science/paper/75FTMG6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28487&json=true","fetch_graph":"https://pith.science/api/pith-number/75FTMG6VJZG3OCRFPCANL747WZ/graph.json","fetch_events":"https://pith.science/api/pith-number/75FTMG6VJZG3OCRFPCANL747WZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ/action/storage_attestation","attest_author":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ/action/author_attestation","sign_citation":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ/action/citation_signature","submit_replication":"https://pith.science/pith/75FTMG6VJZG3OCRFPCANL747WZ/action/replication_record"}},"created_at":"2026-05-28T02:04:54.338872+00:00","updated_at":"2026-05-28T02:04:54.338872+00:00"}