{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AP3WMRZEQP5N6HPTQMSX3CJWY7","short_pith_number":"pith:AP3WMRZE","schema_version":"1.0","canonical_sha256":"03f766472483fadf1df383257d8936c7e3ed283e8d2c4fe6904310b7e259800d","source":{"kind":"arxiv","id":"2412.16964","version":2},"attestation_state":"computed","paper":{"title":"System-2 Mathematical Reasoning via Enriched Instruction Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Huanqia Cai, Yijun Yang, ZhiFeng Li","submitted_at":"2024-12-22T10:49:27Z","abstract_excerpt":"Solving complex mathematical problems via system-2 reasoning is a natural human skill, yet it remains a significant challenge for current large language models (LLMs). We identify the scarcity of deliberate multi-step reasoning data as a primary limiting factor. To this end, we introduce Enriched Instruction Tuning (EIT), a method that enriches existing human-annotated mathematical datasets by synergizing human and AI feedback to create fine-grained reasoning trajectories. These datasets are then used to fine-tune open-source LLMs, enhancing their mathematical reasoning abilities without relia"},"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":"2412.16964","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-12-22T10:49:27Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"48cf5d39e764aa3c7a4de80fe3fb7c1a63d28a9c7c0f5df38728a2f0e9b6e57d","abstract_canon_sha256":"cee1cc01f3f3a8ac14bb6bc0b29c25c1f60724c160b8da3c09db5444ec2aa8c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:47.823086Z","signature_b64":"+eMoKGZanLF/al9OQ/6uelabtpl4YNZWLi1D4GY+SCca1GwXJuY69f56rtwjpyWMgL+aK+zqpQuQUWskWr8TDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03f766472483fadf1df383257d8936c7e3ed283e8d2c4fe6904310b7e259800d","last_reissued_at":"2026-07-05T09:53:47.822601Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:47.822601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"System-2 Mathematical Reasoning via Enriched Instruction Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Huanqia Cai, Yijun Yang, ZhiFeng Li","submitted_at":"2024-12-22T10:49:27Z","abstract_excerpt":"Solving complex mathematical problems via system-2 reasoning is a natural human skill, yet it remains a significant challenge for current large language models (LLMs). We identify the scarcity of deliberate multi-step reasoning data as a primary limiting factor. To this end, we introduce Enriched Instruction Tuning (EIT), a method that enriches existing human-annotated mathematical datasets by synergizing human and AI feedback to create fine-grained reasoning trajectories. These datasets are then used to fine-tune open-source LLMs, enhancing their mathematical reasoning abilities without relia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16964","kind":"arxiv","version":2},"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/2412.16964/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":"2412.16964","created_at":"2026-07-05T09:53:47.822660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.16964v2","created_at":"2026-07-05T09:53:47.822660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16964","created_at":"2026-07-05T09:53:47.822660+00:00"},{"alias_kind":"pith_short_12","alias_value":"AP3WMRZEQP5N","created_at":"2026-07-05T09:53:47.822660+00:00"},{"alias_kind":"pith_short_16","alias_value":"AP3WMRZEQP5N6HPT","created_at":"2026-07-05T09:53:47.822660+00:00"},{"alias_kind":"pith_short_8","alias_value":"AP3WMRZE","created_at":"2026-07-05T09:53:47.822660+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01168","citing_title":"Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2503.09567","citing_title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","ref_index":63,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7","json":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7.json","graph_json":"https://pith.science/api/pith-number/AP3WMRZEQP5N6HPTQMSX3CJWY7/graph.json","events_json":"https://pith.science/api/pith-number/AP3WMRZEQP5N6HPTQMSX3CJWY7/events.json","paper":"https://pith.science/paper/AP3WMRZE"},"agent_actions":{"view_html":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7","download_json":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7.json","view_paper":"https://pith.science/paper/AP3WMRZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.16964&json=true","fetch_graph":"https://pith.science/api/pith-number/AP3WMRZEQP5N6HPTQMSX3CJWY7/graph.json","fetch_events":"https://pith.science/api/pith-number/AP3WMRZEQP5N6HPTQMSX3CJWY7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7/action/storage_attestation","attest_author":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7/action/author_attestation","sign_citation":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7/action/citation_signature","submit_replication":"https://pith.science/pith/AP3WMRZEQP5N6HPTQMSX3CJWY7/action/replication_record"}},"created_at":"2026-07-05T09:53:47.822660+00:00","updated_at":"2026-07-05T09:53:47.822660+00:00"}