{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VTJONDUYP5LN44AYI4PY7A5U5U","short_pith_number":"pith:VTJONDUY","schema_version":"1.0","canonical_sha256":"acd2e68e987f56de7018471f8f83b4ed31733fbece9ed61441fa3a47c75b8f54","source":{"kind":"arxiv","id":"2502.12492","version":1},"attestation_state":"computed","paper":{"title":"Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hanjing Wang, Jianxing Liu, Jizheng Chen, Jun Wang, Kounianhua Du, Ruiming Tang, Weinan Zhang, Xinyi Dai, Yasheng Wang, Yong Yu","submitted_at":"2025-02-18T03:20:50Z","abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks, yet the intricacies of their problem-solving mechanisms in system 2 tasks are not sufficiently explored. Recent research on System2-to-System1 methods surge, exploring the System 2 reasoning knowledge via inference-time computation and compressing the explored knowledge into System 1 process. In this paper, we focus on code generation, which is a representative System 2 task, and identify two primary challenges: (1) the complex hidden reasoning processes and (2) the hetero"},"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":"2502.12492","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-18T03:20:50Z","cross_cats_sorted":[],"title_canon_sha256":"ec7fc2b397423ae88ebf67afb2fcafb0e16ca185102c4bfc5ed0c5ed06cd3788","abstract_canon_sha256":"308eb6e3fe5d34c1b8354626a1098e3265c5348a750dd8adeab413ad4b3f17d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:56.452504Z","signature_b64":"mty2ybM0DclYuI1zUzXiG0TD/UqhBWrTcKkBWN8NWXbwVDn8kFxUO3z1pfMh2sNItXatvvKNzCRW9T6aooNqCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acd2e68e987f56de7018471f8f83b4ed31733fbece9ed61441fa3a47c75b8f54","last_reissued_at":"2026-07-05T10:15:56.452012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:56.452012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hanjing Wang, Jianxing Liu, Jizheng Chen, Jun Wang, Kounianhua Du, Ruiming Tang, Weinan Zhang, Xinyi Dai, Yasheng Wang, Yong Yu","submitted_at":"2025-02-18T03:20:50Z","abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks, yet the intricacies of their problem-solving mechanisms in system 2 tasks are not sufficiently explored. Recent research on System2-to-System1 methods surge, exploring the System 2 reasoning knowledge via inference-time computation and compressing the explored knowledge into System 1 process. In this paper, we focus on code generation, which is a representative System 2 task, and identify two primary challenges: (1) the complex hidden reasoning processes and (2) the hetero"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12492","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/2502.12492/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":"2502.12492","created_at":"2026-07-05T10:15:56.452071+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.12492v1","created_at":"2026-07-05T10:15:56.452071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12492","created_at":"2026-07-05T10:15:56.452071+00:00"},{"alias_kind":"pith_short_12","alias_value":"VTJONDUYP5LN","created_at":"2026-07-05T10:15:56.452071+00:00"},{"alias_kind":"pith_short_16","alias_value":"VTJONDUYP5LN44AY","created_at":"2026-07-05T10:15:56.452071+00:00"},{"alias_kind":"pith_short_8","alias_value":"VTJONDUY","created_at":"2026-07-05T10:15:56.452071+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.09567","citing_title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","ref_index":166,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U","json":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U.json","graph_json":"https://pith.science/api/pith-number/VTJONDUYP5LN44AYI4PY7A5U5U/graph.json","events_json":"https://pith.science/api/pith-number/VTJONDUYP5LN44AYI4PY7A5U5U/events.json","paper":"https://pith.science/paper/VTJONDUY"},"agent_actions":{"view_html":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U","download_json":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U.json","view_paper":"https://pith.science/paper/VTJONDUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.12492&json=true","fetch_graph":"https://pith.science/api/pith-number/VTJONDUYP5LN44AYI4PY7A5U5U/graph.json","fetch_events":"https://pith.science/api/pith-number/VTJONDUYP5LN44AYI4PY7A5U5U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U/action/storage_attestation","attest_author":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U/action/author_attestation","sign_citation":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U/action/citation_signature","submit_replication":"https://pith.science/pith/VTJONDUYP5LN44AYI4PY7A5U5U/action/replication_record"}},"created_at":"2026-07-05T10:15:56.452071+00:00","updated_at":"2026-07-05T10:15:56.452071+00:00"}