{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PIQ2MQ4TNWPLBVSH7G4XU6INPP","short_pith_number":"pith:PIQ2MQ4T","schema_version":"1.0","canonical_sha256":"7a21a643936d9eb0d647f9b97a790d7bc7091cb06f9ab5bc8fa96b9ec5b0f00e","source":{"kind":"arxiv","id":"2402.19431","version":1},"attestation_state":"computed","paper":{"title":"Compositional API Recommendation for Library-Oriented Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Bing Xie, Shengnan An, Zeqi Lin, Zexiong Ma","submitted_at":"2024-02-29T18:27:27Z","abstract_excerpt":"Large language models (LLMs) have achieved exceptional performance in code generation. However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs. Previous work utilizes API recommendation technology to help LLMs use libraries: it retrieves APIs related to the user requirements, then leverages them as context to prompt LLMs. However, developmental requirements can be coarse-grained, requiring a combination of multiple fine-grained APIs. This granularity inconsistency makes API recommendation a chall"},"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":"2402.19431","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-29T18:27:27Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f113c8525f7a4e5bb74b85e65e51ac3736f2c3b243575fd9620d0d27ba3e5361","abstract_canon_sha256":"af8b1f15c576f4127d740533b27cc2683cc75dcf65363e62594d335177fff4ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:45.139211Z","signature_b64":"8eF1GVSSohqBsjGC9JhTFFSidFLb7oaGVydj+QwdtRzovXDzq+JHrW3iNuHvLWceZDJu49TSDoIVtCsiIiMlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a21a643936d9eb0d647f9b97a790d7bc7091cb06f9ab5bc8fa96b9ec5b0f00e","last_reissued_at":"2026-07-05T07:50:45.138699Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:45.138699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compositional API Recommendation for Library-Oriented Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.SE","authors_text":"Bing Xie, Shengnan An, Zeqi Lin, Zexiong Ma","submitted_at":"2024-02-29T18:27:27Z","abstract_excerpt":"Large language models (LLMs) have achieved exceptional performance in code generation. However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs. Previous work utilizes API recommendation technology to help LLMs use libraries: it retrieves APIs related to the user requirements, then leverages them as context to prompt LLMs. However, developmental requirements can be coarse-grained, requiring a combination of multiple fine-grained APIs. This granularity inconsistency makes API recommendation a chall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.19431","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/2402.19431/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":"2402.19431","created_at":"2026-07-05T07:50:45.138761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.19431v1","created_at":"2026-07-05T07:50:45.138761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.19431","created_at":"2026-07-05T07:50:45.138761+00:00"},{"alias_kind":"pith_short_12","alias_value":"PIQ2MQ4TNWPL","created_at":"2026-07-05T07:50:45.138761+00:00"},{"alias_kind":"pith_short_16","alias_value":"PIQ2MQ4TNWPLBVSH","created_at":"2026-07-05T07:50:45.138761+00:00"},{"alias_kind":"pith_short_8","alias_value":"PIQ2MQ4T","created_at":"2026-07-05T07:50:45.138761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14905","citing_title":"Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP","json":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP.json","graph_json":"https://pith.science/api/pith-number/PIQ2MQ4TNWPLBVSH7G4XU6INPP/graph.json","events_json":"https://pith.science/api/pith-number/PIQ2MQ4TNWPLBVSH7G4XU6INPP/events.json","paper":"https://pith.science/paper/PIQ2MQ4T"},"agent_actions":{"view_html":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP","download_json":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP.json","view_paper":"https://pith.science/paper/PIQ2MQ4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.19431&json=true","fetch_graph":"https://pith.science/api/pith-number/PIQ2MQ4TNWPLBVSH7G4XU6INPP/graph.json","fetch_events":"https://pith.science/api/pith-number/PIQ2MQ4TNWPLBVSH7G4XU6INPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP/action/storage_attestation","attest_author":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP/action/author_attestation","sign_citation":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP/action/citation_signature","submit_replication":"https://pith.science/pith/PIQ2MQ4TNWPLBVSH7G4XU6INPP/action/replication_record"}},"created_at":"2026-07-05T07:50:45.138761+00:00","updated_at":"2026-07-05T07:50:45.138761+00:00"}