{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6WK5E5IMFWTGKXPKGCXCPTZ7MR","short_pith_number":"pith:6WK5E5IM","schema_version":"1.0","canonical_sha256":"f595d2750c2da6655dea30ae27cf3f646f78fd8d7a9c499389ce8990fbf6cd72","source":{"kind":"arxiv","id":"2312.04684","version":4},"attestation_state":"computed","paper":{"title":"LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dmitriy Bespalov, Haozhu Wang, Peter Stone, Xian Wu, Yanjun Qi, Zifan Xu","submitted_at":"2023-12-07T20:36:10Z","abstract_excerpt":"Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL approaches construct prompts using examples that contain questions similar to the input question. However, CoT prompting, which includes crucial intermediate reasoning steps (rationales) within its examples, necessitates selecting examples based on these rationales rather than the questions themselves. Existing methods require human experts or pre-trained LLMs to describe the skill, a high-level abstraction of ratio"},"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":"2312.04684","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-07T20:36:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1258de24b5bfa4082fd74eeaf9e3669d365c69e51278e0b5dcb5b2e12f961f4f","abstract_canon_sha256":"f9805395b25809de56db6604083369bb4c2decef626eee096d34515c4c78d626"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:51.064378Z","signature_b64":"tfDEAy+X7bfip+2av1G82/biij8lV31qiXDaQ2JhLjw9ta0C15XOy5q9mNKdp702q0eu6sdqEbtH3zm9k2jeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f595d2750c2da6655dea30ae27cf3f646f78fd8d7a9c499389ce8990fbf6cd72","last_reissued_at":"2026-07-05T11:24:51.063848Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:51.063848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dmitriy Bespalov, Haozhu Wang, Peter Stone, Xian Wu, Yanjun Qi, Zifan Xu","submitted_at":"2023-12-07T20:36:10Z","abstract_excerpt":"Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks. Traditional ICL approaches construct prompts using examples that contain questions similar to the input question. However, CoT prompting, which includes crucial intermediate reasoning steps (rationales) within its examples, necessitates selecting examples based on these rationales rather than the questions themselves. Existing methods require human experts or pre-trained LLMs to describe the skill, a high-level abstraction of ratio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.04684","kind":"arxiv","version":4},"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/2312.04684/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":"2312.04684","created_at":"2026-07-05T11:24:51.063908+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.04684v4","created_at":"2026-07-05T11:24:51.063908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.04684","created_at":"2026-07-05T11:24:51.063908+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WK5E5IMFWTG","created_at":"2026-07-05T11:24:51.063908+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WK5E5IMFWTGKXPK","created_at":"2026-07-05T11:24:51.063908+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WK5E5IM","created_at":"2026-07-05T11:24:51.063908+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.05139","citing_title":"Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR","json":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR.json","graph_json":"https://pith.science/api/pith-number/6WK5E5IMFWTGKXPKGCXCPTZ7MR/graph.json","events_json":"https://pith.science/api/pith-number/6WK5E5IMFWTGKXPKGCXCPTZ7MR/events.json","paper":"https://pith.science/paper/6WK5E5IM"},"agent_actions":{"view_html":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR","download_json":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR.json","view_paper":"https://pith.science/paper/6WK5E5IM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.04684&json=true","fetch_graph":"https://pith.science/api/pith-number/6WK5E5IMFWTGKXPKGCXCPTZ7MR/graph.json","fetch_events":"https://pith.science/api/pith-number/6WK5E5IMFWTGKXPKGCXCPTZ7MR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR/action/storage_attestation","attest_author":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR/action/author_attestation","sign_citation":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR/action/citation_signature","submit_replication":"https://pith.science/pith/6WK5E5IMFWTGKXPKGCXCPTZ7MR/action/replication_record"}},"created_at":"2026-07-05T11:24:51.063908+00:00","updated_at":"2026-07-05T11:24:51.063908+00:00"}