{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2YCQSZRS4OWPOXH4G76VHYNTS2","short_pith_number":"pith:2YCQSZRS","canonical_record":{"source":{"id":"2509.08803","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2025-09-10T17:36:25Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CY"],"title_canon_sha256":"4767b10a28445e0874c12d3f53746d0915eb8e4a21c19dc7aed324acf02ab3ee","abstract_canon_sha256":"0a76d01d25ad5545826e6b0e8413d16fbd124992baeec4e4b7055670af1673fa"},"schema_version":"1.0"},"canonical_sha256":"d605096632e3acf75cfc37fd53e1b3969e56c288f2b412743792d0feb62c3f99","source":{"kind":"arxiv","id":"2509.08803","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.08803","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"arxiv_version","alias_value":"2509.08803v1","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08803","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_12","alias_value":"2YCQSZRS4OWP","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_16","alias_value":"2YCQSZRS4OWPOXH4","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_8","alias_value":"2YCQSZRS","created_at":"2026-07-05T12:08:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2YCQSZRS4OWPOXH4G76VHYNTS2","target":"record","payload":{"canonical_record":{"source":{"id":"2509.08803","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2025-09-10T17:36:25Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CY"],"title_canon_sha256":"4767b10a28445e0874c12d3f53746d0915eb8e4a21c19dc7aed324acf02ab3ee","abstract_canon_sha256":"0a76d01d25ad5545826e6b0e8413d16fbd124992baeec4e4b7055670af1673fa"},"schema_version":"1.0"},"canonical_sha256":"d605096632e3acf75cfc37fd53e1b3969e56c288f2b412743792d0feb62c3f99","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:36.581813Z","signature_b64":"p2fTzgtnkvXi6mMb51xx5HVlUypKtIAkEPsVYb+gurgXa0whWgc5Gge94Z/Be4Tk906MabwxYIh7bFPGnNFSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d605096632e3acf75cfc37fd53e1b3969e56c288f2b412743792d0feb62c3f99","last_reissued_at":"2026-07-05T12:08:36.581402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:36.581402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2509.08803","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":"2026-07-05T12:08:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"10k/yYH6s4UTBR4YyBg1WRjjxWpf1msS4drzSoCxkJMYBEJwblUWYml0NwKvybXCN4iaUNfvJHHCywnD6IrQCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:27:52.803414Z"},"content_sha256":"8787093104394a1e27a790389ad58fb89751df358809d0f578392ffa2f32a7bc","schema_version":"1.0","event_id":"sha256:8787093104394a1e27a790389ad58fb89751df358809d0f578392ffa2f32a7bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2YCQSZRS4OWPOXH4G76VHYNTS2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaling Truth: The Confidence Paradox in AI Fact-Checking","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CY"],"primary_cat":"cs.SI","authors_text":"Abdul H. Azeemi, Abdullah Ghani, Agha A. Raza, Asher Javaid, Ayesha Ali, Ihsan A. Qazi, Muhammad Abdullah Sohail, Wassay Sajjad, Zafar A. Qazi, Zohaib Khan","submitted_at":"2025-09-10T17:36:25Z","abstract_excerpt":"The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages. Our methodology tests model generalizability on claims postdating training cutoffs and four prompti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08803","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/2509.08803/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":"2026-07-05T12:08:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0y3EghNoba50b7zuLNtfjbPLP8PAV/UU3gnX+V3XvSguUGzaOXTF75j07luEN4/15tAVWuAeD5Y7MfEueFh2CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:27:52.806840Z"},"content_sha256":"092d0e17c6ead48ba68a8e1cd9d979e941c7cf860f531bb6ec72125ee35584e4","schema_version":"1.0","event_id":"sha256:092d0e17c6ead48ba68a8e1cd9d979e941c7cf860f531bb6ec72125ee35584e4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/bundle.json","state_url":"https://pith.science/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/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":"2026-08-05T16:27:52Z","links":{"resolver":"https://pith.science/pith/2YCQSZRS4OWPOXH4G76VHYNTS2","bundle":"https://pith.science/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/bundle.json","state":"https://pith.science/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2YCQSZRS4OWPOXH4G76VHYNTS2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2YCQSZRS4OWPOXH4G76VHYNTS2","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":"0a76d01d25ad5545826e6b0e8413d16fbd124992baeec4e4b7055670af1673fa","cross_cats_sorted":["cs.AI","cs.CL","cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2025-09-10T17:36:25Z","title_canon_sha256":"4767b10a28445e0874c12d3f53746d0915eb8e4a21c19dc7aed324acf02ab3ee"},"schema_version":"1.0","source":{"id":"2509.08803","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.08803","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"arxiv_version","alias_value":"2509.08803v1","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08803","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_12","alias_value":"2YCQSZRS4OWP","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_16","alias_value":"2YCQSZRS4OWPOXH4","created_at":"2026-07-05T12:08:36Z"},{"alias_kind":"pith_short_8","alias_value":"2YCQSZRS","created_at":"2026-07-05T12:08:36Z"}],"graph_snapshots":[{"event_id":"sha256:092d0e17c6ead48ba68a8e1cd9d979e941c7cf860f531bb6ec72125ee35584e4","target":"graph","created_at":"2026-07-05T12:08:36Z","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/2509.08803/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages. Our methodology tests model generalizability on claims postdating training cutoffs and four prompti","authors_text":"Abdul H. Azeemi, Abdullah Ghani, Agha A. Raza, Asher Javaid, Ayesha Ali, Ihsan A. Qazi, Muhammad Abdullah Sohail, Wassay Sajjad, Zafar A. Qazi, Zohaib Khan","cross_cats":["cs.AI","cs.CL","cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2025-09-10T17:36:25Z","title":"Scaling Truth: The Confidence Paradox in AI Fact-Checking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08803","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":"sha256:8787093104394a1e27a790389ad58fb89751df358809d0f578392ffa2f32a7bc","target":"record","created_at":"2026-07-05T12:08:36Z","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":"0a76d01d25ad5545826e6b0e8413d16fbd124992baeec4e4b7055670af1673fa","cross_cats_sorted":["cs.AI","cs.CL","cs.CY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2025-09-10T17:36:25Z","title_canon_sha256":"4767b10a28445e0874c12d3f53746d0915eb8e4a21c19dc7aed324acf02ab3ee"},"schema_version":"1.0","source":{"id":"2509.08803","kind":"arxiv","version":1}},"canonical_sha256":"d605096632e3acf75cfc37fd53e1b3969e56c288f2b412743792d0feb62c3f99","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d605096632e3acf75cfc37fd53e1b3969e56c288f2b412743792d0feb62c3f99","first_computed_at":"2026-07-05T12:08:36.581402Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:08:36.581402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p2fTzgtnkvXi6mMb51xx5HVlUypKtIAkEPsVYb+gurgXa0whWgc5Gge94Z/Be4Tk906MabwxYIh7bFPGnNFSDg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:08:36.581813Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.08803","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8787093104394a1e27a790389ad58fb89751df358809d0f578392ffa2f32a7bc","sha256:092d0e17c6ead48ba68a8e1cd9d979e941c7cf860f531bb6ec72125ee35584e4"],"state_sha256":"4e54540e75c05cd1dd6597a6bb9bc2fdc98401d253e940fae773778489a572b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AuWuH+NysOnycqHBNxOrECun4sfj6NodOr7d1YC/EQGxSZgzgnS3G02ZYc8iruOacGWuCRnnr97mUxPf++UwDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:27:52.819012Z","bundle_sha256":"081a45bb030e14ba6ddb5c93d322bc4665ae831e96584874bee7b122efad8d45"}}