{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OJYHZET2ARSOK3O3JTK7HNOGKP","short_pith_number":"pith:OJYHZET2","schema_version":"1.0","canonical_sha256":"72707c927a0464e56ddb4cd5f3b5c653f31ae4b96bde2df66be87cd2698fe471","source":{"kind":"arxiv","id":"2401.05199","version":1},"attestation_state":"computed","paper":{"title":"Monte Carlo Tree Search for Recipe Generation using GPT-2","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Karan Taneja, Richard Goodwin, Richard Segal","submitted_at":"2024-01-10T14:50:46Z","abstract_excerpt":"Automatic food recipe generation methods provide a creative tool for chefs to explore and to create new, and interesting culinary delights. Given the recent success of large language models (LLMs), they have the potential to create new recipes that can meet individual preferences, dietary constraints, and adapt to what is in your refrigerator. Existing research on using LLMs to generate recipes has shown that LLMs can be finetuned to generate realistic-sounding recipes. However, on close examination, these generated recipes often fail to meet basic requirements like including chicken as an ing"},"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":"2401.05199","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-10T14:50:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"967cd74464251c00dd81ce7f9872736a4cd8167a131b053fa4ddc888268aa835","abstract_canon_sha256":"b7cf0c8335c544f9290639d9b67fa028ec7b6cadbfaacaf3128b615b012e3981"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:32:12.059832Z","signature_b64":"ihp0QhydvrJaouh1t5jTM8+lvIT01aO/5Mid97xtHLQ38DHfXUnZvofBi7Jpq6BJhvBcsdKLCiH1hDAhuaKMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72707c927a0464e56ddb4cd5f3b5c653f31ae4b96bde2df66be87cd2698fe471","last_reissued_at":"2026-07-05T07:32:12.059493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:32:12.059493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Monte Carlo Tree Search for Recipe Generation using GPT-2","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Karan Taneja, Richard Goodwin, Richard Segal","submitted_at":"2024-01-10T14:50:46Z","abstract_excerpt":"Automatic food recipe generation methods provide a creative tool for chefs to explore and to create new, and interesting culinary delights. Given the recent success of large language models (LLMs), they have the potential to create new recipes that can meet individual preferences, dietary constraints, and adapt to what is in your refrigerator. Existing research on using LLMs to generate recipes has shown that LLMs can be finetuned to generate realistic-sounding recipes. However, on close examination, these generated recipes often fail to meet basic requirements like including chicken as an ing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05199","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/2401.05199/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":"2401.05199","created_at":"2026-07-05T07:32:12.059548+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.05199v1","created_at":"2026-07-05T07:32:12.059548+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05199","created_at":"2026-07-05T07:32:12.059548+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJYHZET2ARSO","created_at":"2026-07-05T07:32:12.059548+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJYHZET2ARSOK3O3","created_at":"2026-07-05T07:32:12.059548+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJYHZET2","created_at":"2026-07-05T07:32:12.059548+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23527","citing_title":"On Recipe Memorization and Creativity in Large Language Models: Is Your Model a Creative Cook, a Bad Cook, or Merely a Plagiator?","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP","json":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP.json","graph_json":"https://pith.science/api/pith-number/OJYHZET2ARSOK3O3JTK7HNOGKP/graph.json","events_json":"https://pith.science/api/pith-number/OJYHZET2ARSOK3O3JTK7HNOGKP/events.json","paper":"https://pith.science/paper/OJYHZET2"},"agent_actions":{"view_html":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP","download_json":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP.json","view_paper":"https://pith.science/paper/OJYHZET2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.05199&json=true","fetch_graph":"https://pith.science/api/pith-number/OJYHZET2ARSOK3O3JTK7HNOGKP/graph.json","fetch_events":"https://pith.science/api/pith-number/OJYHZET2ARSOK3O3JTK7HNOGKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP/action/storage_attestation","attest_author":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP/action/author_attestation","sign_citation":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP/action/citation_signature","submit_replication":"https://pith.science/pith/OJYHZET2ARSOK3O3JTK7HNOGKP/action/replication_record"}},"created_at":"2026-07-05T07:32:12.059548+00:00","updated_at":"2026-07-05T07:32:12.059548+00:00"}