{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2RYBRCTMCGFDFXSMLMLHRTXE7U","short_pith_number":"pith:2RYBRCTM","schema_version":"1.0","canonical_sha256":"d470188a6c118a32de4c5b1678cee4fd132522a1ebd81b6af4e8fdc3d245627e","source":{"kind":"arxiv","id":"2303.09564","version":1},"attestation_state":"computed","paper":{"title":"TypeT5: Seq2seq Type Inference using Static Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Greg Durrett, Isil Dillig, Jiayi Wei","submitted_at":"2023-03-16T23:48:00Z","abstract_excerpt":"There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as a code infilling task by leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for code. Our method uses static analysis to construct dynamic contexts for each code element whose type signature is to be predicted by the "},"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":"2303.09564","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2023-03-16T23:48:00Z","cross_cats_sorted":["cs.LG","cs.PL"],"title_canon_sha256":"fcc47a0cee2c154aea1b378a1fca79266075dd3c78bcab5086d0e702701d5d1a","abstract_canon_sha256":"5d9b379562adac9dcd7b30e3d69b7b246796fdaf9fb504fdf7df5dd416689e59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:58.425823Z","signature_b64":"rGOE3+rRRsGUqtSOdc3ejOaKvNwls+9bRmF64lOkxMRqyC3n6+m40iP4OGbVgM7PphnCYRz9K+Zq7J47K/fuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d470188a6c118a32de4c5b1678cee4fd132522a1ebd81b6af4e8fdc3d245627e","last_reissued_at":"2026-07-05T05:51:58.425262Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:58.425262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TypeT5: Seq2seq Type Inference using Static Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Greg Durrett, Isil Dillig, Jiayi Wei","submitted_at":"2023-03-16T23:48:00Z","abstract_excerpt":"There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as a code infilling task by leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for code. Our method uses static analysis to construct dynamic contexts for each code element whose type signature is to be predicted by the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.09564","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/2303.09564/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":"2303.09564","created_at":"2026-07-05T05:51:58.425348+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.09564v1","created_at":"2026-07-05T05:51:58.425348+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.09564","created_at":"2026-07-05T05:51:58.425348+00:00"},{"alias_kind":"pith_short_12","alias_value":"2RYBRCTMCGFD","created_at":"2026-07-05T05:51:58.425348+00:00"},{"alias_kind":"pith_short_16","alias_value":"2RYBRCTMCGFDFXSM","created_at":"2026-07-05T05:51:58.425348+00:00"},{"alias_kind":"pith_short_8","alias_value":"2RYBRCTM","created_at":"2026-07-05T05:51:58.425348+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2403.07974","citing_title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U","json":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U.json","graph_json":"https://pith.science/api/pith-number/2RYBRCTMCGFDFXSMLMLHRTXE7U/graph.json","events_json":"https://pith.science/api/pith-number/2RYBRCTMCGFDFXSMLMLHRTXE7U/events.json","paper":"https://pith.science/paper/2RYBRCTM"},"agent_actions":{"view_html":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U","download_json":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U.json","view_paper":"https://pith.science/paper/2RYBRCTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.09564&json=true","fetch_graph":"https://pith.science/api/pith-number/2RYBRCTMCGFDFXSMLMLHRTXE7U/graph.json","fetch_events":"https://pith.science/api/pith-number/2RYBRCTMCGFDFXSMLMLHRTXE7U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U/action/storage_attestation","attest_author":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U/action/author_attestation","sign_citation":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U/action/citation_signature","submit_replication":"https://pith.science/pith/2RYBRCTMCGFDFXSMLMLHRTXE7U/action/replication_record"}},"created_at":"2026-07-05T05:51:58.425348+00:00","updated_at":"2026-07-05T05:51:58.425348+00:00"}