{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ML3MUN5Q5GRU5GEGLEC5KRHBCW","short_pith_number":"pith:ML3MUN5Q","schema_version":"1.0","canonical_sha256":"62f6ca37b0e9a34e98865905d544e115adc5c9e921b5f749b8c7c15ce30312e2","source":{"kind":"arxiv","id":"2501.17167","version":2},"attestation_state":"computed","paper":{"title":"QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Amit Kachroo, Dejiao Zhang, Linbo Liu, Omer Tripp, Qiang Zhou, Qihong Chen, Talha Oz, Xiaopeng Li, Yaojie Hu","submitted_at":"2025-01-20T21:47:06Z","abstract_excerpt":"We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and passes the tests. QualityFlow includes large language model (LLM) agents resembling a software development team, including code generation, testing, and self-debugging. We propose the LLM Quality Checker, which explicitly \"imagines\" whether the synthesized programs' execution would conform to the unit tests. The Quality Checks dynamically control the workflow,"},"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":"2501.17167","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2025-01-20T21:47:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"28880ef629a9481245e5e88c999b93c3bdf4bb2438c44c5e43db45f2aab07b31","abstract_canon_sha256":"cd5261dfa9d375e46bf87c24db7624b94beed53312368a738403b46cedda8eef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:41.443342Z","signature_b64":"zXAY8ErGpLcH4LNO5LiDORmQzoOfUIJ0kEoGEstyg3OVCPP3iiaKqndRaDxOrPl6FRrlaMbDpMRn/PbrW+I8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62f6ca37b0e9a34e98865905d544e115adc5c9e921b5f749b8c7c15ce30312e2","last_reissued_at":"2026-07-05T10:38:41.442859Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:41.442859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SE","authors_text":"Amit Kachroo, Dejiao Zhang, Linbo Liu, Omer Tripp, Qiang Zhou, Qihong Chen, Talha Oz, Xiaopeng Li, Yaojie Hu","submitted_at":"2025-01-20T21:47:06Z","abstract_excerpt":"We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and passes the tests. QualityFlow includes large language model (LLM) agents resembling a software development team, including code generation, testing, and self-debugging. We propose the LLM Quality Checker, which explicitly \"imagines\" whether the synthesized programs' execution would conform to the unit tests. The Quality Checks dynamically control the workflow,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17167","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/2501.17167/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":"2501.17167","created_at":"2026-07-05T10:38:41.442913+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.17167v2","created_at":"2026-07-05T10:38:41.442913+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17167","created_at":"2026-07-05T10:38:41.442913+00:00"},{"alias_kind":"pith_short_12","alias_value":"ML3MUN5Q5GRU","created_at":"2026-07-05T10:38:41.442913+00:00"},{"alias_kind":"pith_short_16","alias_value":"ML3MUN5Q5GRU5GEG","created_at":"2026-07-05T10:38:41.442913+00:00"},{"alias_kind":"pith_short_8","alias_value":"ML3MUN5Q","created_at":"2026-07-05T10:38:41.442913+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18747","citing_title":"Code as Agent Harness","ref_index":257,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19750","citing_title":"Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13242","citing_title":"On the Creativity of AI Agents","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW","json":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW.json","graph_json":"https://pith.science/api/pith-number/ML3MUN5Q5GRU5GEGLEC5KRHBCW/graph.json","events_json":"https://pith.science/api/pith-number/ML3MUN5Q5GRU5GEGLEC5KRHBCW/events.json","paper":"https://pith.science/paper/ML3MUN5Q"},"agent_actions":{"view_html":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW","download_json":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW.json","view_paper":"https://pith.science/paper/ML3MUN5Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.17167&json=true","fetch_graph":"https://pith.science/api/pith-number/ML3MUN5Q5GRU5GEGLEC5KRHBCW/graph.json","fetch_events":"https://pith.science/api/pith-number/ML3MUN5Q5GRU5GEGLEC5KRHBCW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW/action/storage_attestation","attest_author":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW/action/author_attestation","sign_citation":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW/action/citation_signature","submit_replication":"https://pith.science/pith/ML3MUN5Q5GRU5GEGLEC5KRHBCW/action/replication_record"}},"created_at":"2026-07-05T10:38:41.442913+00:00","updated_at":"2026-07-05T10:38:41.442913+00:00"}