{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OMVHZY4ASZ564TAFLZ6VZGGQT5","short_pith_number":"pith:OMVHZY4A","schema_version":"1.0","canonical_sha256":"732a7ce380967bee4c055e7d5c98d09f413255fcacfb578ff7af137eacd403ae","source":{"kind":"arxiv","id":"2608.06917","version":1},"attestation_state":"computed","paper":{"title":"ReGraph: Learning to Generate Recipe Graphs from Food Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bin Zhu, Chong-Wah Ngo, Guoshan Liu, Jingjing Chen, Pengkun Jiao, Yu-Gang Jiang","submitted_at":"2026-08-07T07:51:08Z","abstract_excerpt":"Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.However, cooking is a structured transformation process in which ingredients undergo state changes through ordered actions,while free-form recipe language leaves the corresponding entities, intermediate states, and dependencies largely implicit and entangled.A graph representation makes this procedural knowledge explicit and compositional, providing a structured basis for assessing whether model outputs encode process-level knowledge rather than merely presenting plausible textual d"},"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":"2608.06917","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-07T07:51:08Z","cross_cats_sorted":[],"title_canon_sha256":"f793eac02bf72d727dd9b3d3675d5d29309ec7761b72ba5a643b99757285d248","abstract_canon_sha256":"5686ca0fa1265390b2d255f60c98d5ff7e3990ee65ef30d2aad97d9efca060ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-10T01:12:30.425053Z","signature_b64":"i0hSqaWv8IITqTl6SKcx3ioVwrPJOt6zW1x0AdfKJP72aZ7hKzOPkLKUMA1abb1X+FM3kbsoXBRYQJGvtBhbDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"732a7ce380967bee4c055e7d5c98d09f413255fcacfb578ff7af137eacd403ae","last_reissued_at":"2026-08-10T01:12:30.422577Z","signature_status":"signed_v1","first_computed_at":"2026-08-10T01:12:30.422577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReGraph: Learning to Generate Recipe Graphs from Food Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bin Zhu, Chong-Wah Ngo, Guoshan Liu, Jingjing Chen, Pengkun Jiao, Yu-Gang Jiang","submitted_at":"2026-08-07T07:51:08Z","abstract_excerpt":"Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.However, cooking is a structured transformation process in which ingredients undergo state changes through ordered actions,while free-form recipe language leaves the corresponding entities, intermediate states, and dependencies largely implicit and entangled.A graph representation makes this procedural knowledge explicit and compositional, providing a structured basis for assessing whether model outputs encode process-level knowledge rather than merely presenting plausible textual d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.06917","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/2608.06917/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":"2608.06917","created_at":"2026-08-10T01:12:30.423573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.06917v1","created_at":"2026-08-10T01:12:30.423573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.06917","created_at":"2026-08-10T01:12:30.423573+00:00"},{"alias_kind":"pith_short_12","alias_value":"OMVHZY4ASZ56","created_at":"2026-08-10T01:12:30.423573+00:00"},{"alias_kind":"pith_short_16","alias_value":"OMVHZY4ASZ564TAF","created_at":"2026-08-10T01:12:30.423573+00:00"},{"alias_kind":"pith_short_8","alias_value":"OMVHZY4A","created_at":"2026-08-10T01:12:30.423573+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/OMVHZY4ASZ564TAFLZ6VZGGQT5","json":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5.json","graph_json":"https://pith.science/api/pith-number/OMVHZY4ASZ564TAFLZ6VZGGQT5/graph.json","events_json":"https://pith.science/api/pith-number/OMVHZY4ASZ564TAFLZ6VZGGQT5/events.json","paper":"https://pith.science/paper/OMVHZY4A"},"agent_actions":{"view_html":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5","download_json":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5.json","view_paper":"https://pith.science/paper/OMVHZY4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.06917&json=true","fetch_graph":"https://pith.science/api/pith-number/OMVHZY4ASZ564TAFLZ6VZGGQT5/graph.json","fetch_events":"https://pith.science/api/pith-number/OMVHZY4ASZ564TAFLZ6VZGGQT5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5/action/storage_attestation","attest_author":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5/action/author_attestation","sign_citation":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5/action/citation_signature","submit_replication":"https://pith.science/pith/OMVHZY4ASZ564TAFLZ6VZGGQT5/action/replication_record"}},"created_at":"2026-08-10T01:12:30.423573+00:00","updated_at":"2026-08-10T01:12:30.423573+00:00"}