{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6S3TMU5TIOIPDOANXFKIZC3MAZ","short_pith_number":"pith:6S3TMU5T","schema_version":"1.0","canonical_sha256":"f4b73653b34390f1b80db9548c8b6c066fa01d4c9b23d8aa0e669d01f51bd306","source":{"kind":"arxiv","id":"2607.28086","version":1},"attestation_state":"computed","paper":{"title":"Distilling Answer Set Programming Theories from Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Claudiu Leoveanu-Condrei, Markus Hofmarcher, Nelson Higuera Ruiz","submitted_at":"2026-07-30T11:53:43Z","abstract_excerpt":"Writing Answer Set Programming (ASP) theories from scratch is a difficult and time-consuming task. We take a neurosymbolic approach to study whether a model can distill complete and correct theories, given a fixed agent harness with the solver in the loop. The protocol is dataset-agnostic: with a single prompt and an empty file as the starting point the model is given a 1-hour time limit to derive a complete theory. We chose VQA as the application domain, three benchmarks (CLEVR, GQA, CLEVRER), as these are publicly available and non-trivial. In order to study the model scale required for solv"},"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":"2607.28086","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-30T11:53:43Z","cross_cats_sorted":[],"title_canon_sha256":"21401f3d1e11524afcb012550a2c5013823c2a600497be7df6fc919fc53c3ac8","abstract_canon_sha256":"30a0cb93797d3fd3f971b184e176c4f26b17cb3629a0767f7072effe48417d2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4b73653b34390f1b80db9548c8b6c066fa01d4c9b23d8aa0e669d01f51bd306","last_reissued_at":"2026-07-31T01:35:38.914956Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:35:38.914956Z"},"graph_snapshot":{"paper":{"title":"Distilling Answer Set Programming Theories from Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Claudiu Leoveanu-Condrei, Markus Hofmarcher, Nelson Higuera Ruiz","submitted_at":"2026-07-30T11:53:43Z","abstract_excerpt":"Writing Answer Set Programming (ASP) theories from scratch is a difficult and time-consuming task. We take a neurosymbolic approach to study whether a model can distill complete and correct theories, given a fixed agent harness with the solver in the loop. The protocol is dataset-agnostic: with a single prompt and an empty file as the starting point the model is given a 1-hour time limit to derive a complete theory. We chose VQA as the application domain, three benchmarks (CLEVR, GQA, CLEVRER), as these are publicly available and non-trivial. In order to study the model scale required for solv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28086","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/2607.28086/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":"2607.28086","created_at":"2026-07-31T01:35:38.918137+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28086v1","created_at":"2026-07-31T01:35:38.918137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28086","created_at":"2026-07-31T01:35:38.918137+00:00"},{"alias_kind":"pith_short_12","alias_value":"6S3TMU5TIOIP","created_at":"2026-07-31T01:35:38.918137+00:00"},{"alias_kind":"pith_short_16","alias_value":"6S3TMU5TIOIPDOAN","created_at":"2026-07-31T01:35:38.918137+00:00"},{"alias_kind":"pith_short_8","alias_value":"6S3TMU5T","created_at":"2026-07-31T01:35:38.918137+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/6S3TMU5TIOIPDOANXFKIZC3MAZ","json":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ.json","graph_json":"https://pith.science/api/pith-number/6S3TMU5TIOIPDOANXFKIZC3MAZ/graph.json","events_json":"https://pith.science/api/pith-number/6S3TMU5TIOIPDOANXFKIZC3MAZ/events.json","paper":"https://pith.science/paper/6S3TMU5T"},"agent_actions":{"view_html":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ","download_json":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ.json","view_paper":"https://pith.science/paper/6S3TMU5T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28086&json=true","fetch_graph":"https://pith.science/api/pith-number/6S3TMU5TIOIPDOANXFKIZC3MAZ/graph.json","fetch_events":"https://pith.science/api/pith-number/6S3TMU5TIOIPDOANXFKIZC3MAZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ/action/storage_attestation","attest_author":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ/action/author_attestation","sign_citation":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ/action/citation_signature","submit_replication":"https://pith.science/pith/6S3TMU5TIOIPDOANXFKIZC3MAZ/action/replication_record"}},"created_at":"2026-07-31T01:35:38.918137+00:00","updated_at":"2026-07-31T01:35:38.918137+00:00"}