{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VS2YLKN6PMZPBJIQYBFW6P4U24","short_pith_number":"pith:VS2YLKN6","canonical_record":{"source":{"id":"2311.08505","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T19:53:53Z","cross_cats_sorted":[],"title_canon_sha256":"0c4257bf4f161f65528cb8eb918e0bf0dfab66e30ab944ad4b75504376360c58","abstract_canon_sha256":"2bddafc9ae702b1959371915940dfa0abccfe515c2a83159c9a392718fd2f405"},"schema_version":"1.0"},"canonical_sha256":"acb585a9be7b32f0a510c04b6f3f94d72f6dcaaf8029c0ecee699cf8ef79faa7","source":{"kind":"arxiv","id":"2311.08505","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.08505","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"arxiv_version","alias_value":"2311.08505v2","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08505","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_12","alias_value":"VS2YLKN6PMZP","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_16","alias_value":"VS2YLKN6PMZPBJIQ","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_8","alias_value":"VS2YLKN6","created_at":"2026-07-05T08:03:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VS2YLKN6PMZPBJIQYBFW6P4U24","target":"record","payload":{"canonical_record":{"source":{"id":"2311.08505","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T19:53:53Z","cross_cats_sorted":[],"title_canon_sha256":"0c4257bf4f161f65528cb8eb918e0bf0dfab66e30ab944ad4b75504376360c58","abstract_canon_sha256":"2bddafc9ae702b1959371915940dfa0abccfe515c2a83159c9a392718fd2f405"},"schema_version":"1.0"},"canonical_sha256":"acb585a9be7b32f0a510c04b6f3f94d72f6dcaaf8029c0ecee699cf8ef79faa7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:03:08.358858Z","signature_b64":"d2QZcYc1SdvuyI3G0bvyBIPvInWwIYaA+ErZCPextWS7LHD3pxjVTYMxLfkGgjpiwdokkObaUHCG65XLA8z9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"acb585a9be7b32f0a510c04b6f3f94d72f6dcaaf8029c0ecee699cf8ef79faa7","last_reissued_at":"2026-07-05T08:03:08.358338Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:03:08.358338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.08505","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:03:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HhefY6d+qE4H8+veQE0M+353sWuAm8Xj1YVFJ5IQ0yPsXDkzYsZ754+X0jR+z5iGw863u1IdCMhSoBzuk6cfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:53:20.563920Z"},"content_sha256":"bfa78449a7036d8c84411ff4a43301cf715491835c82bdc966606aeaeba73073","schema_version":"1.0","event_id":"sha256:bfa78449a7036d8c84411ff4a43301cf715491835c82bdc966606aeaeba73073"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VS2YLKN6PMZPBJIQYBFW6P4U24","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Phillip Howard, Steven Bethard, Tiep Le, Xin Su","submitted_at":"2023-11-14T19:53:53Z","abstract_excerpt":"An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructure"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08505","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/2311.08505/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":"2026-07-05T08:03:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dQuPPxmKJZSud1Pw/uMcu+mJLltIOb19jMDWNjnGfppgjkuY3PgV7g7Myqd532pf2aOQeXm5jG9U4VVnTLPRBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:53:20.564408Z"},"content_sha256":"48b7e957c786495af4e2283be1ece83a585ce59979760e0ac943cc2098542d27","schema_version":"1.0","event_id":"sha256:48b7e957c786495af4e2283be1ece83a585ce59979760e0ac943cc2098542d27"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/bundle.json","state_url":"https://pith.science/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/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":"2026-08-09T05:53:20Z","links":{"resolver":"https://pith.science/pith/VS2YLKN6PMZPBJIQYBFW6P4U24","bundle":"https://pith.science/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/bundle.json","state":"https://pith.science/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VS2YLKN6PMZPBJIQYBFW6P4U24/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VS2YLKN6PMZPBJIQYBFW6P4U24","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":"2bddafc9ae702b1959371915940dfa0abccfe515c2a83159c9a392718fd2f405","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T19:53:53Z","title_canon_sha256":"0c4257bf4f161f65528cb8eb918e0bf0dfab66e30ab944ad4b75504376360c58"},"schema_version":"1.0","source":{"id":"2311.08505","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.08505","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"arxiv_version","alias_value":"2311.08505v2","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08505","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_12","alias_value":"VS2YLKN6PMZP","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_16","alias_value":"VS2YLKN6PMZPBJIQ","created_at":"2026-07-05T08:03:08Z"},{"alias_kind":"pith_short_8","alias_value":"VS2YLKN6","created_at":"2026-07-05T08:03:08Z"}],"graph_snapshots":[{"event_id":"sha256:48b7e957c786495af4e2283be1ece83a585ce59979760e0ac943cc2098542d27","target":"graph","created_at":"2026-07-05T08:03:08Z","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/2311.08505/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructure","authors_text":"Phillip Howard, Steven Bethard, Tiep Le, Xin Su","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T19:53:53Z","title":"Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08505","kind":"arxiv","version":2},"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:bfa78449a7036d8c84411ff4a43301cf715491835c82bdc966606aeaeba73073","target":"record","created_at":"2026-07-05T08:03:08Z","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":"2bddafc9ae702b1959371915940dfa0abccfe515c2a83159c9a392718fd2f405","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T19:53:53Z","title_canon_sha256":"0c4257bf4f161f65528cb8eb918e0bf0dfab66e30ab944ad4b75504376360c58"},"schema_version":"1.0","source":{"id":"2311.08505","kind":"arxiv","version":2}},"canonical_sha256":"acb585a9be7b32f0a510c04b6f3f94d72f6dcaaf8029c0ecee699cf8ef79faa7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"acb585a9be7b32f0a510c04b6f3f94d72f6dcaaf8029c0ecee699cf8ef79faa7","first_computed_at":"2026-07-05T08:03:08.358338Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:03:08.358338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d2QZcYc1SdvuyI3G0bvyBIPvInWwIYaA+ErZCPextWS7LHD3pxjVTYMxLfkGgjpiwdokkObaUHCG65XLA8z9Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T08:03:08.358858Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.08505","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bfa78449a7036d8c84411ff4a43301cf715491835c82bdc966606aeaeba73073","sha256:48b7e957c786495af4e2283be1ece83a585ce59979760e0ac943cc2098542d27"],"state_sha256":"070b1b6f810b2e2ca856ed3e25d4c6be49f7ff78a797c7126c055053ac36b79d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XCXXEXKl8rf3MkS+YwIce5W5/6MduJ/fLaHfhBtx02Dcf11s52kFigyVDAa4hziMwv+jlgkkiYiB6qbFxOViDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:53:20.568438Z","bundle_sha256":"d1a8f5cb58faefa882f5715a01e4c7513af0478eb54bc536571e827c0a105069"}}