{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QRWC2DCV5OHLJLQW6ENW2XGP5M","short_pith_number":"pith:QRWC2DCV","schema_version":"1.0","canonical_sha256":"846c2d0c55eb8eb4ae16f11b6d5ccfeb0fde8ac7fd0b4289bf9b461de5f3d17d","source":{"kind":"arxiv","id":"2305.04032","version":5},"attestation_state":"computed","paper":{"title":"ToolCoder: Teach Code Generation Models to use API search tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Ge Li, Huangzhao Zhang, Jia Li, Kechi Zhang, Zhi Jin, Zhuo Li","submitted_at":"2023-05-06T12:45:28Z","abstract_excerpt":"Automatically generating source code from natural language descriptions has been a growing field of research in recent years. However, current large-scale code generation models often encounter difficulties when selecting appropriate APIs for specific contexts. These models may generate APIs that do not meet requirements or refer to non-existent APIs in third-party libraries, especially for lesser-known or private libraries. Inspired by the process of human developers using tools to search APIs, we propose ToolCoder, a novel approach that integrates API search tools with existing models to ass"},"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":"2305.04032","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-05-06T12:45:28Z","cross_cats_sorted":[],"title_canon_sha256":"534520a6f9cdca635b20f034714a5b7360d3c3487d3579a2584e29c96bb84236","abstract_canon_sha256":"ba3a79605ba660e56b0d133d4d424e022c0fb006ce26d388199916ec774eb9eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:49:21.906764Z","signature_b64":"0Td3kEVt/+oHuOVXJY5hZ8kwNWwVXQmxygQBaAZLrngnSlrJLxAJX2AaPFEvP19X6ghByJgf3GsH6H1EtAefBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"846c2d0c55eb8eb4ae16f11b6d5ccfeb0fde8ac7fd0b4289bf9b461de5f3d17d","last_reissued_at":"2026-07-05T06:49:21.906289Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:49:21.906289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ToolCoder: Teach Code Generation Models to use API search tools","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Ge Li, Huangzhao Zhang, Jia Li, Kechi Zhang, Zhi Jin, Zhuo Li","submitted_at":"2023-05-06T12:45:28Z","abstract_excerpt":"Automatically generating source code from natural language descriptions has been a growing field of research in recent years. However, current large-scale code generation models often encounter difficulties when selecting appropriate APIs for specific contexts. These models may generate APIs that do not meet requirements or refer to non-existent APIs in third-party libraries, especially for lesser-known or private libraries. Inspired by the process of human developers using tools to search APIs, we propose ToolCoder, a novel approach that integrates API search tools with existing models to ass"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.04032","kind":"arxiv","version":5},"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/2305.04032/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":"2305.04032","created_at":"2026-07-05T06:49:21.906348+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.04032v5","created_at":"2026-07-05T06:49:21.906348+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04032","created_at":"2026-07-05T06:49:21.906348+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRWC2DCV5OHL","created_at":"2026-07-05T06:49:21.906348+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRWC2DCV5OHLJLQW","created_at":"2026-07-05T06:49:21.906348+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRWC2DCV","created_at":"2026-07-05T06:49:21.906348+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18747","citing_title":"Code as Agent Harness","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2409.02977","citing_title":"Large Language Model-Based Agents for Software Engineering: A Survey","ref_index":92,"is_internal_anchor":false},{"citing_arxiv_id":"2402.19473","citing_title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","ref_index":229,"is_internal_anchor":false},{"citing_arxiv_id":"2403.07974","citing_title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M","json":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M.json","graph_json":"https://pith.science/api/pith-number/QRWC2DCV5OHLJLQW6ENW2XGP5M/graph.json","events_json":"https://pith.science/api/pith-number/QRWC2DCV5OHLJLQW6ENW2XGP5M/events.json","paper":"https://pith.science/paper/QRWC2DCV"},"agent_actions":{"view_html":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M","download_json":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M.json","view_paper":"https://pith.science/paper/QRWC2DCV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.04032&json=true","fetch_graph":"https://pith.science/api/pith-number/QRWC2DCV5OHLJLQW6ENW2XGP5M/graph.json","fetch_events":"https://pith.science/api/pith-number/QRWC2DCV5OHLJLQW6ENW2XGP5M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M/action/storage_attestation","attest_author":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M/action/author_attestation","sign_citation":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M/action/citation_signature","submit_replication":"https://pith.science/pith/QRWC2DCV5OHLJLQW6ENW2XGP5M/action/replication_record"}},"created_at":"2026-07-05T06:49:21.906348+00:00","updated_at":"2026-07-05T06:49:21.906348+00:00"}