{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:O43FIFKNEQLU6ELJY7LFN2M7CU","short_pith_number":"pith:O43FIFKN","canonical_record":{"source":{"id":"2310.01714","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T00:57:26Z","cross_cats_sorted":[],"title_canon_sha256":"dc1cb5d969bd2fe305112d43b374aa73417a14b8f5fb5b492d308ea714f2aad0","abstract_canon_sha256":"45dd538a2ef07eca23b634c501115f109a471cea1eb7317c71db1ca76c0db800"},"schema_version":"1.0"},"canonical_sha256":"773654154d24174f1169c7d656e99f15101984f47b522831d51f5dfdeaa72e7f","source":{"kind":"arxiv","id":"2310.01714","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.01714","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"arxiv_version","alias_value":"2310.01714v3","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01714","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_12","alias_value":"O43FIFKNEQLU","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_16","alias_value":"O43FIFKNEQLU6ELJ","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_8","alias_value":"O43FIFKN","created_at":"2026-07-05T07:54:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:O43FIFKNEQLU6ELJY7LFN2M7CU","target":"record","payload":{"canonical_record":{"source":{"id":"2310.01714","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T00:57:26Z","cross_cats_sorted":[],"title_canon_sha256":"dc1cb5d969bd2fe305112d43b374aa73417a14b8f5fb5b492d308ea714f2aad0","abstract_canon_sha256":"45dd538a2ef07eca23b634c501115f109a471cea1eb7317c71db1ca76c0db800"},"schema_version":"1.0"},"canonical_sha256":"773654154d24174f1169c7d656e99f15101984f47b522831d51f5dfdeaa72e7f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:04.291150Z","signature_b64":"FeB8g/ZaPgNITz8IRp4RPUJAWC7YKZBJ1cCtF+oyhw3an4LC5mC2TA58kMoAD/2zmaEVHBTyxV8bFKcuhAAeCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"773654154d24174f1169c7d656e99f15101984f47b522831d51f5dfdeaa72e7f","last_reissued_at":"2026-07-05T07:54:04.290667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:04.290667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.01714","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:54:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B6z0FEVYecKSF3ytyzw8ZhNAGCz1jgVYytsGqfPdtfygeMj2paCBtLqB9c/DUJQ1+fai81Op5aA4ZCWJBDvJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T04:38:48.376386Z"},"content_sha256":"3ac92039e6b9a92038678d913dccaf381165a0d50347d618aa2bc63ca3a16e81","schema_version":"1.0","event_id":"sha256:3ac92039e6b9a92038678d913dccaf381165a0d50347d618aa2bc63ca3a16e81"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:O43FIFKNEQLU6ELJY7LFN2M7CU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models as Analogical Reasoners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Denny Zhou, Ed H. Chi, Jure Leskovec, Michihiro Yasunaga, Panupong Pasupat, Percy Liang, Xinyun Chen, Yujia Li","submitted_at":"2023-10-03T00:57:26Z","abstract_excerpt":"Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this work, we introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models. Inspired by analogical reasoning, a cognitive process in which humans draw from relevant past experiences to tackle new problems, our approach prompts language models to self-generate relevant exemplars or knowledge in the context, before proceeding to solve the given"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01714","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/2310.01714/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:54:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KKCnPb8JR6LIOaT3Gfu5AjlbeP/Gh0vCr0u5oCFCQ4/t2v3xXKqNcPRLQs8VkgFWYwDHFmAsDPR/Z8gq5rzlCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T04:38:48.377285Z"},"content_sha256":"de0e5499387abc5ffff5ced78e0b3993d10a7141201c55ede40a2de8024dd3bd","schema_version":"1.0","event_id":"sha256:de0e5499387abc5ffff5ced78e0b3993d10a7141201c55ede40a2de8024dd3bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/bundle.json","state_url":"https://pith.science/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T04:38:48Z","links":{"resolver":"https://pith.science/pith/O43FIFKNEQLU6ELJY7LFN2M7CU","bundle":"https://pith.science/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/bundle.json","state":"https://pith.science/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O43FIFKNEQLU6ELJY7LFN2M7CU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:O43FIFKNEQLU6ELJY7LFN2M7CU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"45dd538a2ef07eca23b634c501115f109a471cea1eb7317c71db1ca76c0db800","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T00:57:26Z","title_canon_sha256":"dc1cb5d969bd2fe305112d43b374aa73417a14b8f5fb5b492d308ea714f2aad0"},"schema_version":"1.0","source":{"id":"2310.01714","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.01714","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"arxiv_version","alias_value":"2310.01714v3","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01714","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_12","alias_value":"O43FIFKNEQLU","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_16","alias_value":"O43FIFKNEQLU6ELJ","created_at":"2026-07-05T07:54:04Z"},{"alias_kind":"pith_short_8","alias_value":"O43FIFKN","created_at":"2026-07-05T07:54:04Z"}],"graph_snapshots":[{"event_id":"sha256:de0e5499387abc5ffff5ced78e0b3993d10a7141201c55ede40a2de8024dd3bd","target":"graph","created_at":"2026-07-05T07:54:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.01714/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this work, we introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models. Inspired by analogical reasoning, a cognitive process in which humans draw from relevant past experiences to tackle new problems, our approach prompts language models to self-generate relevant exemplars or knowledge in the context, before proceeding to solve the given","authors_text":"Denny Zhou, Ed H. Chi, Jure Leskovec, Michihiro Yasunaga, Panupong Pasupat, Percy Liang, Xinyun Chen, Yujia Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T00:57:26Z","title":"Large Language Models as Analogical Reasoners"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01714","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3ac92039e6b9a92038678d913dccaf381165a0d50347d618aa2bc63ca3a16e81","target":"record","created_at":"2026-07-05T07:54:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"45dd538a2ef07eca23b634c501115f109a471cea1eb7317c71db1ca76c0db800","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T00:57:26Z","title_canon_sha256":"dc1cb5d969bd2fe305112d43b374aa73417a14b8f5fb5b492d308ea714f2aad0"},"schema_version":"1.0","source":{"id":"2310.01714","kind":"arxiv","version":3}},"canonical_sha256":"773654154d24174f1169c7d656e99f15101984f47b522831d51f5dfdeaa72e7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"773654154d24174f1169c7d656e99f15101984f47b522831d51f5dfdeaa72e7f","first_computed_at":"2026-07-05T07:54:04.290667Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:54:04.290667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FeB8g/ZaPgNITz8IRp4RPUJAWC7YKZBJ1cCtF+oyhw3an4LC5mC2TA58kMoAD/2zmaEVHBTyxV8bFKcuhAAeCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:54:04.291150Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.01714","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ac92039e6b9a92038678d913dccaf381165a0d50347d618aa2bc63ca3a16e81","sha256:de0e5499387abc5ffff5ced78e0b3993d10a7141201c55ede40a2de8024dd3bd"],"state_sha256":"de6e0075d7f1203faeef1779e1f07a5177423fc2d3d85fd5223cf9372787bb5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0QCYoQ2XpstSbJjRzrSfzPP2rKvqDemXqq5mMoT9l8NeRFdKdPffblSCxRdnf7l7QL7B59iTDey9yPgipJUuDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T04:38:48.382446Z","bundle_sha256":"9f28c7b139a00db7a4f22a0041d3ad1d06426eff4ad736a5ce007b8e7de9a55d"}}