{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KBD7MA45QT3GTSKCMDFFDU3SZK","short_pith_number":"pith:KBD7MA45","schema_version":"1.0","canonical_sha256":"5047f6039d84f669c94260ca51d372cabdc36b8c1e0bcfd0fb42b3131eeb4154","source":{"kind":"arxiv","id":"2506.23527","version":1},"attestation_state":"computed","paper":{"title":"On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jan Kvapil, Martin Fajcik","submitted_at":"2025-06-30T05:27:11Z","abstract_excerpt":"This work-in-progress investigates the memorization, creativity, and nonsense found in cooking recipes generated from Large Language Models (LLMs). Precisely, we aim (i) to analyze memorization, creativity, and non-sense in LLMs using a small, high-quality set of human judgments and (ii) to evaluate potential approaches to automate such a human annotation in order to scale our study to hundreds of recipes. To achieve (i), we conduct a detailed human annotation on 20 preselected recipes generated by LLM (Mixtral), extracting each recipe's ingredients and step-by-step actions to assess which ele"},"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":"2506.23527","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-30T05:27:11Z","cross_cats_sorted":[],"title_canon_sha256":"1cb049b3a31806a20982d7174bbba28939b5bd2f9ffca5ce8599bea8a304deac","abstract_canon_sha256":"013baf9faa7c3dd701b61f8a9fcb89e1c278b015a40bee0f6538da3f0d9a1123"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:23.430675Z","signature_b64":"cjH6G7OAp39VAqpDhVDqnEKTjv6Rv6u09rAgqFQ/hAcpLA3iC5P+R3DZ4EoVcDV+8n/S/EjUMOLm0BXFpirQCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5047f6039d84f669c94260ca51d372cabdc36b8c1e0bcfd0fb42b3131eeb4154","last_reissued_at":"2026-07-05T11:29:23.430243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:23.430243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jan Kvapil, Martin Fajcik","submitted_at":"2025-06-30T05:27:11Z","abstract_excerpt":"This work-in-progress investigates the memorization, creativity, and nonsense found in cooking recipes generated from Large Language Models (LLMs). Precisely, we aim (i) to analyze memorization, creativity, and non-sense in LLMs using a small, high-quality set of human judgments and (ii) to evaluate potential approaches to automate such a human annotation in order to scale our study to hundreds of recipes. To achieve (i), we conduct a detailed human annotation on 20 preselected recipes generated by LLM (Mixtral), extracting each recipe's ingredients and step-by-step actions to assess which ele"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23527","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/2506.23527/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":"2506.23527","created_at":"2026-07-05T11:29:23.430309+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.23527v1","created_at":"2026-07-05T11:29:23.430309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23527","created_at":"2026-07-05T11:29:23.430309+00:00"},{"alias_kind":"pith_short_12","alias_value":"KBD7MA45QT3G","created_at":"2026-07-05T11:29:23.430309+00:00"},{"alias_kind":"pith_short_16","alias_value":"KBD7MA45QT3GTSKC","created_at":"2026-07-05T11:29:23.430309+00:00"},{"alias_kind":"pith_short_8","alias_value":"KBD7MA45","created_at":"2026-07-05T11:29:23.430309+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/KBD7MA45QT3GTSKCMDFFDU3SZK","json":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK.json","graph_json":"https://pith.science/api/pith-number/KBD7MA45QT3GTSKCMDFFDU3SZK/graph.json","events_json":"https://pith.science/api/pith-number/KBD7MA45QT3GTSKCMDFFDU3SZK/events.json","paper":"https://pith.science/paper/KBD7MA45"},"agent_actions":{"view_html":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK","download_json":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK.json","view_paper":"https://pith.science/paper/KBD7MA45","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.23527&json=true","fetch_graph":"https://pith.science/api/pith-number/KBD7MA45QT3GTSKCMDFFDU3SZK/graph.json","fetch_events":"https://pith.science/api/pith-number/KBD7MA45QT3GTSKCMDFFDU3SZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK/action/storage_attestation","attest_author":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK/action/author_attestation","sign_citation":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK/action/citation_signature","submit_replication":"https://pith.science/pith/KBD7MA45QT3GTSKCMDFFDU3SZK/action/replication_record"}},"created_at":"2026-07-05T11:29:23.430309+00:00","updated_at":"2026-07-05T11:29:23.430309+00:00"}