{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HH5CIB5UV2DPPTGWNCUPS7CPZN","short_pith_number":"pith:HH5CIB5U","schema_version":"1.0","canonical_sha256":"39fa2407b4ae86f7ccd668a8f97c4fcb57382cadc84ed954248bfdbfd4215bdb","source":{"kind":"arxiv","id":"2508.00013","version":1},"attestation_state":"computed","paper":{"title":"From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.PL","authors_text":"Anna Arnania, Tamar Sanikidze, Zurabi Kobaladze","submitted_at":"2025-07-21T11:33:57Z","abstract_excerpt":"Program synthesis--the automated generation of executable code from high-level specifications--has been a central goal of computer science for over fifty years. This thesis provides a comparative literature review of the main paradigms that have shaped the field, tracing its evolution from formal logic based methods to recent advances using large scale neural models. We examine five key approaches: logic based (deductive) synthesis, inductive (example based) synthesis, sketch/schema based synthesis, large language model based synthesis, and neuro-symbolic hybrids. For each, we analyze foundati"},"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":"2508.00013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PL","submitted_at":"2025-07-21T11:33:57Z","cross_cats_sorted":[],"title_canon_sha256":"b2854567c622bcf484c4bd096ecd1a9653bee0007a023c2b595ba35aafac84c1","abstract_canon_sha256":"ac27348a4726dbf4384ac99ebb9a8ba14a963d34d2f0b7a26f44f5d34d3c4263"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:47.599609Z","signature_b64":"lm+HCRoesmw46YDxLkjxtIm3I4gViBuh5sGMtiUjYM1FpTNgy5cwR9c21bZ1q1/fA2A6QO65Be4ls7c48vHNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39fa2407b4ae86f7ccd668a8f97c4fcb57382cadc84ed954248bfdbfd4215bdb","last_reissued_at":"2026-07-05T11:46:47.599142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:47.599142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.PL","authors_text":"Anna Arnania, Tamar Sanikidze, Zurabi Kobaladze","submitted_at":"2025-07-21T11:33:57Z","abstract_excerpt":"Program synthesis--the automated generation of executable code from high-level specifications--has been a central goal of computer science for over fifty years. This thesis provides a comparative literature review of the main paradigms that have shaped the field, tracing its evolution from formal logic based methods to recent advances using large scale neural models. We examine five key approaches: logic based (deductive) synthesis, inductive (example based) synthesis, sketch/schema based synthesis, large language model based synthesis, and neuro-symbolic hybrids. For each, we analyze foundati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.00013","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/2508.00013/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":"2508.00013","created_at":"2026-07-05T11:46:47.599198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.00013v1","created_at":"2026-07-05T11:46:47.599198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.00013","created_at":"2026-07-05T11:46:47.599198+00:00"},{"alias_kind":"pith_short_12","alias_value":"HH5CIB5UV2DP","created_at":"2026-07-05T11:46:47.599198+00:00"},{"alias_kind":"pith_short_16","alias_value":"HH5CIB5UV2DPPTGW","created_at":"2026-07-05T11:46:47.599198+00:00"},{"alias_kind":"pith_short_8","alias_value":"HH5CIB5U","created_at":"2026-07-05T11:46:47.599198+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/HH5CIB5UV2DPPTGWNCUPS7CPZN","json":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN.json","graph_json":"https://pith.science/api/pith-number/HH5CIB5UV2DPPTGWNCUPS7CPZN/graph.json","events_json":"https://pith.science/api/pith-number/HH5CIB5UV2DPPTGWNCUPS7CPZN/events.json","paper":"https://pith.science/paper/HH5CIB5U"},"agent_actions":{"view_html":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN","download_json":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN.json","view_paper":"https://pith.science/paper/HH5CIB5U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.00013&json=true","fetch_graph":"https://pith.science/api/pith-number/HH5CIB5UV2DPPTGWNCUPS7CPZN/graph.json","fetch_events":"https://pith.science/api/pith-number/HH5CIB5UV2DPPTGWNCUPS7CPZN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN/action/storage_attestation","attest_author":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN/action/author_attestation","sign_citation":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN/action/citation_signature","submit_replication":"https://pith.science/pith/HH5CIB5UV2DPPTGWNCUPS7CPZN/action/replication_record"}},"created_at":"2026-07-05T11:46:47.599198+00:00","updated_at":"2026-07-05T11:46:47.599198+00:00"}