{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ONDWJS2Q3VY7JJR3CKOLNYZQAS","short_pith_number":"pith:ONDWJS2Q","schema_version":"1.0","canonical_sha256":"734764cb50dd71f4a63b129cb6e330048f711170ae5bc230c1080057d0370810","source":{"kind":"arxiv","id":"2501.12226","version":1},"attestation_state":"computed","paper":{"title":"CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dianhui Chu, Guoqing Chao, Shaobo Li, Wenqiang Lei, Yuanheng Fang","submitted_at":"2025-01-21T15:51:07Z","abstract_excerpt":"Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely on using the same prompts, whether manually designed or automatically generated, to handle the entire dataset. This one-size-fits-all approach may fail to meet the specific needs arising from the diversities within a single dataset. To solve this problem, we propose the Clustered Distance-Weighted Chain of Thought (CDW-CoT) method, which dynamically constructs prompts tailored to the characteristics of each data inst"},"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":"2501.12226","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-21T15:51:07Z","cross_cats_sorted":[],"title_canon_sha256":"40cb099de60a473cc21aebec40909dbd2bb246b5cc1199b22c2e9faab5fcf3d0","abstract_canon_sha256":"baa9674e09d80f926e39f98b5d36b9b14eb0f907847dd9e0f254054cdf6e8688"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:33.266560Z","signature_b64":"dTcyhF4ere81qM30HcEcZ9yE/4Gb2Xm7MHbpO0qC+zzGPvUdk62eUJYFP/g08C2hlUrzzszU3ZDw86pfxdUeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"734764cb50dd71f4a63b129cb6e330048f711170ae5bc230c1080057d0370810","last_reissued_at":"2026-07-05T10:03:33.266138Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:33.266138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dianhui Chu, Guoqing Chao, Shaobo Li, Wenqiang Lei, Yuanheng Fang","submitted_at":"2025-01-21T15:51:07Z","abstract_excerpt":"Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely on using the same prompts, whether manually designed or automatically generated, to handle the entire dataset. This one-size-fits-all approach may fail to meet the specific needs arising from the diversities within a single dataset. To solve this problem, we propose the Clustered Distance-Weighted Chain of Thought (CDW-CoT) method, which dynamically constructs prompts tailored to the characteristics of each data inst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12226","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/2501.12226/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":"2501.12226","created_at":"2026-07-05T10:03:33.266195+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12226v1","created_at":"2026-07-05T10:03:33.266195+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12226","created_at":"2026-07-05T10:03:33.266195+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONDWJS2Q3VY7","created_at":"2026-07-05T10:03:33.266195+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONDWJS2Q3VY7JJR3","created_at":"2026-07-05T10:03:33.266195+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONDWJS2Q","created_at":"2026-07-05T10:03:33.266195+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS","json":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS.json","graph_json":"https://pith.science/api/pith-number/ONDWJS2Q3VY7JJR3CKOLNYZQAS/graph.json","events_json":"https://pith.science/api/pith-number/ONDWJS2Q3VY7JJR3CKOLNYZQAS/events.json","paper":"https://pith.science/paper/ONDWJS2Q"},"agent_actions":{"view_html":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS","download_json":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS.json","view_paper":"https://pith.science/paper/ONDWJS2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12226&json=true","fetch_graph":"https://pith.science/api/pith-number/ONDWJS2Q3VY7JJR3CKOLNYZQAS/graph.json","fetch_events":"https://pith.science/api/pith-number/ONDWJS2Q3VY7JJR3CKOLNYZQAS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS/action/storage_attestation","attest_author":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS/action/author_attestation","sign_citation":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS/action/citation_signature","submit_replication":"https://pith.science/pith/ONDWJS2Q3VY7JJR3CKOLNYZQAS/action/replication_record"}},"created_at":"2026-07-05T10:03:33.266195+00:00","updated_at":"2026-07-05T10:03:33.266195+00:00"}