{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:35FYCWZ5LGKSPJCV63KMJZOBFG","short_pith_number":"pith:35FYCWZ5","schema_version":"1.0","canonical_sha256":"df4b815b3d599527a455f6d4c4e5c129bbef08abb5cb7511eeada29383d81f4c","source":{"kind":"arxiv","id":"2310.08992","version":3},"attestation_state":"computed","paper":{"title":"CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.PL"],"primary_cat":"cs.AI","authors_text":"Akash Gokul, Amrita Saha, Doyen Sahoo, Hailin Chen, Hung Le, Shafiq Joty","submitted_at":"2023-10-13T10:17:48Z","abstract_excerpt":"Large Language Models (LLMs) have already become quite proficient at solving simpler programming tasks like those in HumanEval or MBPP benchmarks. However, solving more complex and competitive programming tasks is still quite challenging for these models - possibly due to their tendency to generate solutions as monolithic code blocks instead of decomposing them into logical sub-tasks and sub-modules. On the other hand, experienced programmers instinctively write modularized code with abstraction for solving complex tasks, often reusing previously developed modules. To address this gap, we prop"},"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":"2310.08992","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-10-13T10:17:48Z","cross_cats_sorted":["cs.CL","cs.PL"],"title_canon_sha256":"cb4f0e207df220e0b6f6bd157a85febd0badf7068428cdaeb9ca924c128944de","abstract_canon_sha256":"fbd4fceacaf97e3a5cb0b3a2875868494b10d8f6b52b6495daf0169c26099967"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:00.262476Z","signature_b64":"VDkEi1o+mga/aQjteuiIt9HqToul8hwPTKdRieaVJoS/hNri/siV3zq+vCA5XIrIoqtLr4qIKBr2IjasSx+bBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df4b815b3d599527a455f6d4c4e5c129bbef08abb5cb7511eeada29383d81f4c","last_reissued_at":"2026-07-05T07:56:00.262000Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:00.262000Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.PL"],"primary_cat":"cs.AI","authors_text":"Akash Gokul, Amrita Saha, Doyen Sahoo, Hailin Chen, Hung Le, Shafiq Joty","submitted_at":"2023-10-13T10:17:48Z","abstract_excerpt":"Large Language Models (LLMs) have already become quite proficient at solving simpler programming tasks like those in HumanEval or MBPP benchmarks. However, solving more complex and competitive programming tasks is still quite challenging for these models - possibly due to their tendency to generate solutions as monolithic code blocks instead of decomposing them into logical sub-tasks and sub-modules. On the other hand, experienced programmers instinctively write modularized code with abstraction for solving complex tasks, often reusing previously developed modules. To address this gap, we prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08992","kind":"arxiv","version":3},"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/2310.08992/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":"2310.08992","created_at":"2026-07-05T07:56:00.262059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.08992v3","created_at":"2026-07-05T07:56:00.262059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08992","created_at":"2026-07-05T07:56:00.262059+00:00"},{"alias_kind":"pith_short_12","alias_value":"35FYCWZ5LGKS","created_at":"2026-07-05T07:56:00.262059+00:00"},{"alias_kind":"pith_short_16","alias_value":"35FYCWZ5LGKSPJCV","created_at":"2026-07-05T07:56:00.262059+00:00"},{"alias_kind":"pith_short_8","alias_value":"35FYCWZ5","created_at":"2026-07-05T07:56:00.262059+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.02390","citing_title":"DecompRL: Solving Harder Problems by Learning Modular Code Generation","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2312.13010","citing_title":"AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23580","citing_title":"PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG","json":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG.json","graph_json":"https://pith.science/api/pith-number/35FYCWZ5LGKSPJCV63KMJZOBFG/graph.json","events_json":"https://pith.science/api/pith-number/35FYCWZ5LGKSPJCV63KMJZOBFG/events.json","paper":"https://pith.science/paper/35FYCWZ5"},"agent_actions":{"view_html":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG","download_json":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG.json","view_paper":"https://pith.science/paper/35FYCWZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.08992&json=true","fetch_graph":"https://pith.science/api/pith-number/35FYCWZ5LGKSPJCV63KMJZOBFG/graph.json","fetch_events":"https://pith.science/api/pith-number/35FYCWZ5LGKSPJCV63KMJZOBFG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG/action/storage_attestation","attest_author":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG/action/author_attestation","sign_citation":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG/action/citation_signature","submit_replication":"https://pith.science/pith/35FYCWZ5LGKSPJCV63KMJZOBFG/action/replication_record"}},"created_at":"2026-07-05T07:56:00.262059+00:00","updated_at":"2026-07-05T07:56:00.262059+00:00"}