{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XNLEITJVAHVTDHRG76EJUV5RDB","short_pith_number":"pith:XNLEITJV","canonical_record":{"source":{"id":"2402.14273","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T04:20:14Z","cross_cats_sorted":[],"title_canon_sha256":"564485daae935565182c9849b46459adedfa33784ea9346fbf87ee01f1027ece","abstract_canon_sha256":"5631c5621739a3280c8f6606a2db535c06d60f12f34a15e9d45bdb7827f3fc2b"},"schema_version":"1.0"},"canonical_sha256":"bb56444d3501eb319e26ff889a57b1186b372bcd6918fcd0523da7f6c3d25765","source":{"kind":"arxiv","id":"2402.14273","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14273","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14273v1","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14273","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_12","alias_value":"XNLEITJVAHVT","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_16","alias_value":"XNLEITJVAHVTDHRG","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_8","alias_value":"XNLEITJV","created_at":"2026-07-05T07:48:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XNLEITJVAHVTDHRG76EJUV5RDB","target":"record","payload":{"canonical_record":{"source":{"id":"2402.14273","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T04:20:14Z","cross_cats_sorted":[],"title_canon_sha256":"564485daae935565182c9849b46459adedfa33784ea9346fbf87ee01f1027ece","abstract_canon_sha256":"5631c5621739a3280c8f6606a2db535c06d60f12f34a15e9d45bdb7827f3fc2b"},"schema_version":"1.0"},"canonical_sha256":"bb56444d3501eb319e26ff889a57b1186b372bcd6918fcd0523da7f6c3d25765","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:48:07.956203Z","signature_b64":"0GJOkjHzT/biQOy6T82EmSpHqPkHHocW9NK/uQfMbjuSEYLfMJxcvTrVC35Dms5eMIAfG5BaDLZoycs9Q3m6DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb56444d3501eb319e26ff889a57b1186b372bcd6918fcd0523da7f6c3d25765","last_reissued_at":"2026-07-05T07:48:07.955711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:48:07.955711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.14273","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":"2026-07-05T07:48:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lp5dDy6Kr/Rdeojn+YXhDnt1Nz19ubldcSTxAVq5A3IUHPgTYL1lpK6F0g4EfZVhPMdnFEtUazlpNcVJjCO/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:40:09.856952Z"},"content_sha256":"e12c9f61bc83a1695383af0043df2df9e1d98eb74b083ec07cd6a924f8dc7f74","schema_version":"1.0","event_id":"sha256:e12c9f61bc83a1695383af0043df2df9e1d98eb74b083ec07cd6a924f8dc7f74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XNLEITJVAHVTDHRG76EJUV5RDB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Language Models Act as Knowledge Bases at Scale?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Qiyuan He, Wenya Wang, Yizhong Wang","submitted_at":"2024-02-22T04:20:14Z","abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating responses to complex queries through large-scale pre-training. However, the efficacy of these models in memorizing and reasoning among large-scale structured knowledge, especially world knowledge that explicitly covers abundant factual information remains questionable. Addressing this gap, our research investigates whether LLMs can effectively store, recall, and reason with knowledge on a large scale comparable to latest knowledge bases (KBs) such as Wikidata. Specifically, we focus on three c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14273","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/2402.14273/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":"2026-07-05T07:48:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cXQHWz7WGx8eF8owi8ayJ8jOOCsNbcsWLfiNFf8nSogyzrZkwtdL654QUelmIjjQk1TX8XmfzxiXq7V6rKBTCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:40:09.857433Z"},"content_sha256":"75e5ef550a1c284ceab8020676ee81f5debd11a6bb0c0c97106f2523ac0af27a","schema_version":"1.0","event_id":"sha256:75e5ef550a1c284ceab8020676ee81f5debd11a6bb0c0c97106f2523ac0af27a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XNLEITJVAHVTDHRG76EJUV5RDB/bundle.json","state_url":"https://pith.science/pith/XNLEITJVAHVTDHRG76EJUV5RDB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XNLEITJVAHVTDHRG76EJUV5RDB/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":"2026-08-07T06:40:09Z","links":{"resolver":"https://pith.science/pith/XNLEITJVAHVTDHRG76EJUV5RDB","bundle":"https://pith.science/pith/XNLEITJVAHVTDHRG76EJUV5RDB/bundle.json","state":"https://pith.science/pith/XNLEITJVAHVTDHRG76EJUV5RDB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XNLEITJVAHVTDHRG76EJUV5RDB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XNLEITJVAHVTDHRG76EJUV5RDB","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":"5631c5621739a3280c8f6606a2db535c06d60f12f34a15e9d45bdb7827f3fc2b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T04:20:14Z","title_canon_sha256":"564485daae935565182c9849b46459adedfa33784ea9346fbf87ee01f1027ece"},"schema_version":"1.0","source":{"id":"2402.14273","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.14273","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"arxiv_version","alias_value":"2402.14273v1","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.14273","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_12","alias_value":"XNLEITJVAHVT","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_16","alias_value":"XNLEITJVAHVTDHRG","created_at":"2026-07-05T07:48:07Z"},{"alias_kind":"pith_short_8","alias_value":"XNLEITJV","created_at":"2026-07-05T07:48:07Z"}],"graph_snapshots":[{"event_id":"sha256:75e5ef550a1c284ceab8020676ee81f5debd11a6bb0c0c97106f2523ac0af27a","target":"graph","created_at":"2026-07-05T07:48:07Z","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/2402.14273/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating responses to complex queries through large-scale pre-training. However, the efficacy of these models in memorizing and reasoning among large-scale structured knowledge, especially world knowledge that explicitly covers abundant factual information remains questionable. Addressing this gap, our research investigates whether LLMs can effectively store, recall, and reason with knowledge on a large scale comparable to latest knowledge bases (KBs) such as Wikidata. Specifically, we focus on three c","authors_text":"Qiyuan He, Wenya Wang, Yizhong Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T04:20:14Z","title":"Can Language Models Act as Knowledge Bases at Scale?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.14273","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:e12c9f61bc83a1695383af0043df2df9e1d98eb74b083ec07cd6a924f8dc7f74","target":"record","created_at":"2026-07-05T07:48:07Z","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":"5631c5621739a3280c8f6606a2db535c06d60f12f34a15e9d45bdb7827f3fc2b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-22T04:20:14Z","title_canon_sha256":"564485daae935565182c9849b46459adedfa33784ea9346fbf87ee01f1027ece"},"schema_version":"1.0","source":{"id":"2402.14273","kind":"arxiv","version":1}},"canonical_sha256":"bb56444d3501eb319e26ff889a57b1186b372bcd6918fcd0523da7f6c3d25765","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb56444d3501eb319e26ff889a57b1186b372bcd6918fcd0523da7f6c3d25765","first_computed_at":"2026-07-05T07:48:07.955711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:48:07.955711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0GJOkjHzT/biQOy6T82EmSpHqPkHHocW9NK/uQfMbjuSEYLfMJxcvTrVC35Dms5eMIAfG5BaDLZoycs9Q3m6DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:48:07.956203Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.14273","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e12c9f61bc83a1695383af0043df2df9e1d98eb74b083ec07cd6a924f8dc7f74","sha256:75e5ef550a1c284ceab8020676ee81f5debd11a6bb0c0c97106f2523ac0af27a"],"state_sha256":"5c9506b092496f104f6a49c07aa6c8b7c8512e39826bfddb6fb3379093fab940"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1+/5+N7zeELxWnjTdog8JD7mI2aOPYSlkir4+gR0H2OSxuWuk1faEgBVysgOIY13BSOmmr5sFXPRlPGsr/pTDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:40:09.862464Z","bundle_sha256":"9165d2767539bd05adaefee18ad13f770a39941438ad77855bec91b4e5acbcbf"}}