{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EJQGOG56YBSFIVSNH4E64WPKQT","short_pith_number":"pith:EJQGOG56","schema_version":"1.0","canonical_sha256":"2260671bbec06454564d3f09ee59ea84f954c50e465b22d9ba15ffb9c069f509","source":{"kind":"arxiv","id":"2409.13122","version":2},"attestation_state":"computed","paper":{"title":"RepoGenReflex: Enhancing Repository-Level Code Completion with Verbal Reinforcement and Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Hao Chen, Jicheng Wang, Yifeng He","submitted_at":"2024-09-19T23:38:59Z","abstract_excerpt":"In real-world software engineering tasks, solving a problem often requires understanding and modifying multiple functions, classes, and files across a large codebase. Therefore, on the repository level, it is crucial to extract the relevant information to achieve accurate code completion effectively. Existing code completion tools have achieved some success, but they struggle to optimize the retrieval and generation process dynamically. In this paper, we propose RepoGenReflex, a generic, dynamic, effective framework to address this challenge. By leveraging the Retrieval-Augmented Generation (R"},"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":"2409.13122","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-09-19T23:38:59Z","cross_cats_sorted":[],"title_canon_sha256":"58f2f89d3dc54d7404c734edc2272955d946d036a7fd8e366f18587b56316cee","abstract_canon_sha256":"29af1557fc63eeb8352ded6ae7e99972fb1207698d1df42dd4890c0480bd75cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:51.100903Z","signature_b64":"a4UuYwElw8JjOve90wMVAeExkOEtBpqzi/MHjtP9fDfDDMCJsA2uwV9Znk1Iy2KR9rP46zrLCGKh8w0+3A5JCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2260671bbec06454564d3f09ee59ea84f954c50e465b22d9ba15ffb9c069f509","last_reissued_at":"2026-07-05T09:10:51.100397Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:51.100397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RepoGenReflex: Enhancing Repository-Level Code Completion with Verbal Reinforcement and Retrieval-Augmented Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Hao Chen, Jicheng Wang, Yifeng He","submitted_at":"2024-09-19T23:38:59Z","abstract_excerpt":"In real-world software engineering tasks, solving a problem often requires understanding and modifying multiple functions, classes, and files across a large codebase. Therefore, on the repository level, it is crucial to extract the relevant information to achieve accurate code completion effectively. Existing code completion tools have achieved some success, but they struggle to optimize the retrieval and generation process dynamically. In this paper, we propose RepoGenReflex, a generic, dynamic, effective framework to address this challenge. By leveraging the Retrieval-Augmented Generation (R"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13122","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/2409.13122/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":"2409.13122","created_at":"2026-07-05T09:10:51.100463+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13122v2","created_at":"2026-07-05T09:10:51.100463+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13122","created_at":"2026-07-05T09:10:51.100463+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJQGOG56YBSF","created_at":"2026-07-05T09:10:51.100463+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJQGOG56YBSFIVSN","created_at":"2026-07-05T09:10:51.100463+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJQGOG56","created_at":"2026-07-05T09:10:51.100463+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20473","citing_title":"Code Generation by Differential Test Time Scaling","ref_index":94,"is_internal_anchor":false},{"citing_arxiv_id":"2601.00376","citing_title":"In Line with Context: Repository-Level Code Generation via Context Inlining","ref_index":61,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT","json":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT.json","graph_json":"https://pith.science/api/pith-number/EJQGOG56YBSFIVSNH4E64WPKQT/graph.json","events_json":"https://pith.science/api/pith-number/EJQGOG56YBSFIVSNH4E64WPKQT/events.json","paper":"https://pith.science/paper/EJQGOG56"},"agent_actions":{"view_html":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT","download_json":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT.json","view_paper":"https://pith.science/paper/EJQGOG56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13122&json=true","fetch_graph":"https://pith.science/api/pith-number/EJQGOG56YBSFIVSNH4E64WPKQT/graph.json","fetch_events":"https://pith.science/api/pith-number/EJQGOG56YBSFIVSNH4E64WPKQT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT/action/storage_attestation","attest_author":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT/action/author_attestation","sign_citation":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT/action/citation_signature","submit_replication":"https://pith.science/pith/EJQGOG56YBSFIVSNH4E64WPKQT/action/replication_record"}},"created_at":"2026-07-05T09:10:51.100463+00:00","updated_at":"2026-07-05T09:10:51.100463+00:00"}