{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:J6EXYNCLG3JKKJLVH3GEI3F6OM","short_pith_number":"pith:J6EXYNCL","schema_version":"1.0","canonical_sha256":"4f897c344b36d2a525753ecc446cbe730995af0549c52fc1e3d3ab94f9f28a5f","source":{"kind":"arxiv","id":"2310.01831","version":2},"attestation_state":"computed","paper":{"title":"Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.PL"],"primary_cat":"cs.SE","authors_text":"Madeline Endres, Saikat Chakraborty, Sarah Fakhoury, Shuvendu K. Lahiri","submitted_at":"2023-10-03T06:55:45Z","abstract_excerpt":"Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However, there is typically no guarantee that a programs implementation and natural language documentation are aligned. In the case of a conflict, leveraging information in code-adjacent natural language has the potential to enhance fault localization, debugging, and code trustworthiness. In practice, however, this information is often underutilized due to the inherent ambiguity of natural language which makes natural languag"},"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":"2310.01831","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-10-03T06:55:45Z","cross_cats_sorted":["cs.AI","cs.PL"],"title_canon_sha256":"6603dbe04fb9532ecf03b20547b3df7b8b50190e7b2fbaf7eaf6a30607236d69","abstract_canon_sha256":"674763ab8ff02d9ae0e340876cd97d962eede5b21128cf77045f8c5d50ae853d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:18.256579Z","signature_b64":"xccfvpcCFqSoUTfhTXFx/iJ73yDbQn/NbqxyL//01N2F+uiYUNeyk9yYv8srTRyzC7hC4lgK1dKmqgHuzKfICA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f897c344b36d2a525753ecc446cbe730995af0549c52fc1e3d3ab94f9f28a5f","last_reissued_at":"2026-07-05T08:08:18.256136Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:18.256136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.PL"],"primary_cat":"cs.SE","authors_text":"Madeline Endres, Saikat Chakraborty, Sarah Fakhoury, Shuvendu K. Lahiri","submitted_at":"2023-10-03T06:55:45Z","abstract_excerpt":"Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However, there is typically no guarantee that a programs implementation and natural language documentation are aligned. In the case of a conflict, leveraging information in code-adjacent natural language has the potential to enhance fault localization, debugging, and code trustworthiness. In practice, however, this information is often underutilized due to the inherent ambiguity of natural language which makes natural languag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01831","kind":"arxiv","version":2},"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/2310.01831/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":"2310.01831","created_at":"2026-07-05T08:08:18.256193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.01831v2","created_at":"2026-07-05T08:08:18.256193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01831","created_at":"2026-07-05T08:08:18.256193+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6EXYNCLG3JK","created_at":"2026-07-05T08:08:18.256193+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6EXYNCLG3JKKJLV","created_at":"2026-07-05T08:08:18.256193+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6EXYNCL","created_at":"2026-07-05T08:08:18.256193+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23109","citing_title":"Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM","json":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM.json","graph_json":"https://pith.science/api/pith-number/J6EXYNCLG3JKKJLVH3GEI3F6OM/graph.json","events_json":"https://pith.science/api/pith-number/J6EXYNCLG3JKKJLVH3GEI3F6OM/events.json","paper":"https://pith.science/paper/J6EXYNCL"},"agent_actions":{"view_html":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM","download_json":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM.json","view_paper":"https://pith.science/paper/J6EXYNCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.01831&json=true","fetch_graph":"https://pith.science/api/pith-number/J6EXYNCLG3JKKJLVH3GEI3F6OM/graph.json","fetch_events":"https://pith.science/api/pith-number/J6EXYNCLG3JKKJLVH3GEI3F6OM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM/action/storage_attestation","attest_author":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM/action/author_attestation","sign_citation":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM/action/citation_signature","submit_replication":"https://pith.science/pith/J6EXYNCLG3JKKJLVH3GEI3F6OM/action/replication_record"}},"created_at":"2026-07-05T08:08:18.256193+00:00","updated_at":"2026-07-05T08:08:18.256193+00:00"}