{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KMUTJ22YUCYOVZ55YGKVLYOFGY","short_pith_number":"pith:KMUTJ22Y","canonical_record":{"source":{"id":"2410.11588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T13:24:44Z","cross_cats_sorted":[],"title_canon_sha256":"500db9f949b6ac738a0d7d194a7700d79e27736e8fc8aa21aed04cc201971c1a","abstract_canon_sha256":"e10b6e0f26057b69eb76e9d4ff84c1f634a0242b707b789dcd748dee131a9b43"},"schema_version":"1.0"},"canonical_sha256":"532934eb58a0b0eae7bdc19555e1c53607930d4641213e8adb00672dd352ed81","source":{"kind":"arxiv","id":"2410.11588","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.11588","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"arxiv_version","alias_value":"2410.11588v1","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11588","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_12","alias_value":"KMUTJ22YUCYO","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_16","alias_value":"KMUTJ22YUCYOVZ55","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_8","alias_value":"KMUTJ22Y","created_at":"2026-07-05T09:20:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KMUTJ22YUCYOVZ55YGKVLYOFGY","target":"record","payload":{"canonical_record":{"source":{"id":"2410.11588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T13:24:44Z","cross_cats_sorted":[],"title_canon_sha256":"500db9f949b6ac738a0d7d194a7700d79e27736e8fc8aa21aed04cc201971c1a","abstract_canon_sha256":"e10b6e0f26057b69eb76e9d4ff84c1f634a0242b707b789dcd748dee131a9b43"},"schema_version":"1.0"},"canonical_sha256":"532934eb58a0b0eae7bdc19555e1c53607930d4641213e8adb00672dd352ed81","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:56.147799Z","signature_b64":"8kliU51lIjJYQKHrIyMK0Mj0cjhI8Ty/g2mNrhCoKa5I5NuIX6Tc7f5uy6DDYyoUtZzmbyVUDXRddZAWaEFKBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"532934eb58a0b0eae7bdc19555e1c53607930d4641213e8adb00672dd352ed81","last_reissued_at":"2026-07-05T09:20:56.147403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:56.147403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.11588","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:20:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xu/Hg/pNtY+FXYITsXlohFeBqQICxLME7sPff3RCICv1Gpj1mhIl6Gwv1NbgU5Pdx/0g3hsr6zfWdhPgPrGGDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:24:28.836472Z"},"content_sha256":"f2b128cc7ffa67fffa57cbe88c21dfb76830a915e80b7c4e0953b6a7d569464e","schema_version":"1.0","event_id":"sha256:f2b128cc7ffa67fffa57cbe88c21dfb76830a915e80b7c4e0953b6a7d569464e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KMUTJ22YUCYOVZ55YGKVLYOFGY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causal Reasoning in Large Language Models: A Knowledge Graph Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eojin Kang, H. Howie Huang, Juae Kim, Yejin Kim","submitted_at":"2024-10-15T13:24:44Z","abstract_excerpt":"Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. While both strategies are considered crucial, it remains unclear which has a greater impact on model performance or whether a combination of both is necessary. This paper answers this question by proposing a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships. We conduct experiments on the commonsense question answering task that is based on a KG. The KG inhe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11588","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/2410.11588/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":"2026-07-05T09:20:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LbvnNhvqQFoK0Hdja3LvaPj95NbE5UQkV1lU1K8vV3PH3IbOOxs6OMXNqM/Z6lQvsKCIxQBz/Bwckc0PuDjwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T03:24:28.837410Z"},"content_sha256":"238bd2090adcb8bace80e79b1f2dffe4ba6ac994e0ba172b34bd4ef62b486377","schema_version":"1.0","event_id":"sha256:238bd2090adcb8bace80e79b1f2dffe4ba6ac994e0ba172b34bd4ef62b486377"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/bundle.json","state_url":"https://pith.science/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/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":"2026-08-05T03:24:28Z","links":{"resolver":"https://pith.science/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY","bundle":"https://pith.science/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/bundle.json","state":"https://pith.science/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KMUTJ22YUCYOVZ55YGKVLYOFGY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KMUTJ22YUCYOVZ55YGKVLYOFGY","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":"e10b6e0f26057b69eb76e9d4ff84c1f634a0242b707b789dcd748dee131a9b43","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T13:24:44Z","title_canon_sha256":"500db9f949b6ac738a0d7d194a7700d79e27736e8fc8aa21aed04cc201971c1a"},"schema_version":"1.0","source":{"id":"2410.11588","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.11588","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"arxiv_version","alias_value":"2410.11588v1","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11588","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_12","alias_value":"KMUTJ22YUCYO","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_16","alias_value":"KMUTJ22YUCYOVZ55","created_at":"2026-07-05T09:20:56Z"},{"alias_kind":"pith_short_8","alias_value":"KMUTJ22Y","created_at":"2026-07-05T09:20:56Z"}],"graph_snapshots":[{"event_id":"sha256:238bd2090adcb8bace80e79b1f2dffe4ba6ac994e0ba172b34bd4ef62b486377","target":"graph","created_at":"2026-07-05T09:20:56Z","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/2410.11588/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. While both strategies are considered crucial, it remains unclear which has a greater impact on model performance or whether a combination of both is necessary. This paper answers this question by proposing a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships. We conduct experiments on the commonsense question answering task that is based on a KG. The KG inhe","authors_text":"Eojin Kang, H. Howie Huang, Juae Kim, Yejin Kim","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T13:24:44Z","title":"Causal Reasoning in Large Language Models: A Knowledge Graph Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11588","kind":"arxiv","version":1},"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:f2b128cc7ffa67fffa57cbe88c21dfb76830a915e80b7c4e0953b6a7d569464e","target":"record","created_at":"2026-07-05T09:20:56Z","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":"e10b6e0f26057b69eb76e9d4ff84c1f634a0242b707b789dcd748dee131a9b43","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T13:24:44Z","title_canon_sha256":"500db9f949b6ac738a0d7d194a7700d79e27736e8fc8aa21aed04cc201971c1a"},"schema_version":"1.0","source":{"id":"2410.11588","kind":"arxiv","version":1}},"canonical_sha256":"532934eb58a0b0eae7bdc19555e1c53607930d4641213e8adb00672dd352ed81","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"532934eb58a0b0eae7bdc19555e1c53607930d4641213e8adb00672dd352ed81","first_computed_at":"2026-07-05T09:20:56.147403Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:20:56.147403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8kliU51lIjJYQKHrIyMK0Mj0cjhI8Ty/g2mNrhCoKa5I5NuIX6Tc7f5uy6DDYyoUtZzmbyVUDXRddZAWaEFKBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:20:56.147799Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.11588","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2b128cc7ffa67fffa57cbe88c21dfb76830a915e80b7c4e0953b6a7d569464e","sha256:238bd2090adcb8bace80e79b1f2dffe4ba6ac994e0ba172b34bd4ef62b486377"],"state_sha256":"fd9f4cc8d26e241be866e6c2f286e3b9e2d38d9191a4b56e6c35e517336743b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oU9RM7AlrsihMMNy0JeeQzhvvrP+ro1AQrWoiKEU2vd24vQi7+GgQ7DWTMqYTZ7P+I4QYG5bLHnfxIzNUN1TBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T03:24:28.842704Z","bundle_sha256":"55caa1b581556c6b6d62389c851146ac51a99bb932b9cc3e6abf11dadf0435cf"}}