{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V47Y3ICD2EBSOCOVC562MX7D7F","short_pith_number":"pith:V47Y3ICD","canonical_record":{"source":{"id":"2501.17144","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T18:45:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"79b03442f4d38f599c1398a6d4745afa0e39fb33df25fcf04d7bb2a995e00aa6","abstract_canon_sha256":"4856cdb89ac441b43034f3bc67997a2d4c3da55d4bd97c8140e593677d841505"},"schema_version":"1.0"},"canonical_sha256":"af3f8da043d1032709d5177da65fe3f978a517631ed30cfa6f01b6131512d21c","source":{"kind":"arxiv","id":"2501.17144","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.17144","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"arxiv_version","alias_value":"2501.17144v1","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17144","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_12","alias_value":"V47Y3ICD2EBS","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_16","alias_value":"V47Y3ICD2EBSOCOV","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_8","alias_value":"V47Y3ICD","created_at":"2026-07-05T10:06:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V47Y3ICD2EBSOCOVC562MX7D7F","target":"record","payload":{"canonical_record":{"source":{"id":"2501.17144","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T18:45:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"79b03442f4d38f599c1398a6d4745afa0e39fb33df25fcf04d7bb2a995e00aa6","abstract_canon_sha256":"4856cdb89ac441b43034f3bc67997a2d4c3da55d4bd97c8140e593677d841505"},"schema_version":"1.0"},"canonical_sha256":"af3f8da043d1032709d5177da65fe3f978a517631ed30cfa6f01b6131512d21c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:35.004932Z","signature_b64":"AGvutaZRKWGjV56LMQqxoeJPxbn2YEpliXLsuYwfFeRfF3ueLDw3CXlYOaUqgHpSm5rYo6H2x8hF4Xv1x6RABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af3f8da043d1032709d5177da65fe3f978a517631ed30cfa6f01b6131512d21c","last_reissued_at":"2026-07-05T10:06:35.004550Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:35.004550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.17144","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":"2026-07-05T10:06:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OpsoKk1bLY5h8qe7PKucRt3gvu6GOz8QdLGs9XH8YDPMp/R9TKLYhORMY9BeJHXbJ109NRIMzg8eqbK6eLgnDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:50:38.341336Z"},"content_sha256":"53a595774ec5f5710c9c44916b3df31cde9ec295721ada31dff3b25f08b522a5","schema_version":"1.0","event_id":"sha256:53a595774ec5f5710c9c44916b3df31cde9ec295721ada31dff3b25f08b522a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V47Y3ICD2EBSOCOVC562MX7D7F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alex Deng, Deren Lei, Emily Ching, Ken Archer, Mengya Hu, Mingyu Wang, Rui Xu, Siyao Li, Yaxi Li","submitted_at":"2025-01-28T18:45:07Z","abstract_excerpt":"Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detecting LLM hallucinations. Recent approaches to document-level synthetic data generation involve iteratively removing sentences from documents and annotating factuality using LLM-based prompts. While effective, this method is computationally expensive for long documents and limited by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17144","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/2501.17144/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":"2026-07-05T10:06:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EFR1YEwcN1ugdYDZGNqBR+6NFRbLwC+NJdV4qY2f25yNhYhUJN7dELwX6RhCq+wiwAVYQFj715EhcIycjYxdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:50:38.341941Z"},"content_sha256":"4c1c7f9370e6b188aedce3d6014e948ee845c694d8f445ffeeee3f3a5d7174a7","schema_version":"1.0","event_id":"sha256:4c1c7f9370e6b188aedce3d6014e948ee845c694d8f445ffeeee3f3a5d7174a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V47Y3ICD2EBSOCOVC562MX7D7F/bundle.json","state_url":"https://pith.science/pith/V47Y3ICD2EBSOCOVC562MX7D7F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V47Y3ICD2EBSOCOVC562MX7D7F/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":"2026-08-15T16:50:38Z","links":{"resolver":"https://pith.science/pith/V47Y3ICD2EBSOCOVC562MX7D7F","bundle":"https://pith.science/pith/V47Y3ICD2EBSOCOVC562MX7D7F/bundle.json","state":"https://pith.science/pith/V47Y3ICD2EBSOCOVC562MX7D7F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V47Y3ICD2EBSOCOVC562MX7D7F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V47Y3ICD2EBSOCOVC562MX7D7F","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":"4856cdb89ac441b43034f3bc67997a2d4c3da55d4bd97c8140e593677d841505","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T18:45:07Z","title_canon_sha256":"79b03442f4d38f599c1398a6d4745afa0e39fb33df25fcf04d7bb2a995e00aa6"},"schema_version":"1.0","source":{"id":"2501.17144","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.17144","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"arxiv_version","alias_value":"2501.17144v1","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17144","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_12","alias_value":"V47Y3ICD2EBS","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_16","alias_value":"V47Y3ICD2EBSOCOV","created_at":"2026-07-05T10:06:35Z"},{"alias_kind":"pith_short_8","alias_value":"V47Y3ICD","created_at":"2026-07-05T10:06:35Z"}],"graph_snapshots":[{"event_id":"sha256:4c1c7f9370e6b188aedce3d6014e948ee845c694d8f445ffeeee3f3a5d7174a7","target":"graph","created_at":"2026-07-05T10:06:35Z","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/2501.17144/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detecting LLM hallucinations. Recent approaches to document-level synthetic data generation involve iteratively removing sentences from documents and annotating factuality using LLM-based prompts. While effective, this method is computationally expensive for long documents and limited by","authors_text":"Alex Deng, Deren Lei, Emily Ching, Ken Archer, Mengya Hu, Mingyu Wang, Rui Xu, Siyao Li, Yaxi Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T18:45:07Z","title":"FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17144","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":"sha256:53a595774ec5f5710c9c44916b3df31cde9ec295721ada31dff3b25f08b522a5","target":"record","created_at":"2026-07-05T10:06:35Z","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":"4856cdb89ac441b43034f3bc67997a2d4c3da55d4bd97c8140e593677d841505","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-28T18:45:07Z","title_canon_sha256":"79b03442f4d38f599c1398a6d4745afa0e39fb33df25fcf04d7bb2a995e00aa6"},"schema_version":"1.0","source":{"id":"2501.17144","kind":"arxiv","version":1}},"canonical_sha256":"af3f8da043d1032709d5177da65fe3f978a517631ed30cfa6f01b6131512d21c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af3f8da043d1032709d5177da65fe3f978a517631ed30cfa6f01b6131512d21c","first_computed_at":"2026-07-05T10:06:35.004550Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:06:35.004550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AGvutaZRKWGjV56LMQqxoeJPxbn2YEpliXLsuYwfFeRfF3ueLDw3CXlYOaUqgHpSm5rYo6H2x8hF4Xv1x6RABg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:06:35.004932Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.17144","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53a595774ec5f5710c9c44916b3df31cde9ec295721ada31dff3b25f08b522a5","sha256:4c1c7f9370e6b188aedce3d6014e948ee845c694d8f445ffeeee3f3a5d7174a7"],"state_sha256":"8ac6c94df7351e88fd09b83eab4ec630678086add3d5e05eecf0205771665d3e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6qL1n/8MXnxpkMGA0wnW60WIPLfJWGwULmpwPD6gSCfhk/NLYesL4XZRyaDF65PH9lyoSWD5x2wqI7h8k5pNAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T16:50:38.346246Z","bundle_sha256":"c9ba139b9d4900d55b2581de00dbe345fd881541f3a374f5d8cbf81b97906257"}}