{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","short_pith_number":"pith:FEZNRIT7","canonical_record":{"source":{"id":"2608.03627","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T13:14:06Z","cross_cats_sorted":[],"title_canon_sha256":"4180c20ba796b10dbc47a7a9820d4ca7dcf5eb28151e6e555e91f8f6fb151a70","abstract_canon_sha256":"c50aa6349fba47bfa6c9fce078fdc14e7db4ec123bb9a3c5e7fc50466c188da9"},"schema_version":"1.0"},"canonical_sha256":"2932d8a27f0f35422b0e61ca46c9ea9b77efc5d83922b7f0e32bb4f8e8a4f760","source":{"kind":"arxiv","id":"2608.03627","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.03627","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"arxiv_version","alias_value":"2608.03627v1","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03627","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_12","alias_value":"FEZNRIT7B42U","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_16","alias_value":"FEZNRIT7B42UEKYO","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_8","alias_value":"FEZNRIT7","created_at":"2026-08-05T01:36:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","target":"record","payload":{"canonical_record":{"source":{"id":"2608.03627","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T13:14:06Z","cross_cats_sorted":[],"title_canon_sha256":"4180c20ba796b10dbc47a7a9820d4ca7dcf5eb28151e6e555e91f8f6fb151a70","abstract_canon_sha256":"c50aa6349fba47bfa6c9fce078fdc14e7db4ec123bb9a3c5e7fc50466c188da9"},"schema_version":"1.0"},"canonical_sha256":"2932d8a27f0f35422b0e61ca46c9ea9b77efc5d83922b7f0e32bb4f8e8a4f760","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T01:36:47.094577Z","signature_b64":"gAQbMGRjRL2e7Wqe6wex82xNWRxRPLw4jJqCIW8aOIP92opJ3WCi4ORKUS/VINgwDdVIvpNcAGeoR9RvkQkZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2932d8a27f0f35422b0e61ca46c9ea9b77efc5d83922b7f0e32bb4f8e8a4f760","last_reissued_at":"2026-08-05T01:36:47.093108Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T01:36:47.093108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.03627","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":"2026-08-05T01:36:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"edsTEbqBYLtPx8PcLo2PmnB9Xb4c1YXeQu02xz8dqpNbiJNkHowOrqknKTp7nqSnZe67WceQoQi1DKdxxxydDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:45:37.310109Z"},"content_sha256":"1817d409274c787dc837e6e370f43602b0c472607acff06e1e58d416494dbe9d","schema_version":"1.0","event_id":"sha256:1817d409274c787dc837e6e370f43602b0c472607acff06e1e58d416494dbe9d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Noel Crespi, Razieh Chalehchaleh, Reza Farahbakhsh","submitted_at":"2026-08-04T13:14:06Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored. This study presents the first systematic investigation of gender bias in LLM-based fake news detection using real-world data. We augment the LIAR benchmark with three gender variants of speaker job titles (Neutral, Male, Female) for each statement to test whether veracity judgments vary solely based on gender presentation. Six state-of-the-art LLMs are evaluated across multiple bias and fairness metrics. All models exhibit gender sensiti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03627","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/2608.03627/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":"2026-08-05T01:36:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fncmsCqveGAa2GmCxQpKB2dFfhGCBM/ATiP6rMZXxLf75e6zMM0WQ8RnjTdXS8Y6/3i+xGLo9RtYPTJi0CMoDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:45:37.310601Z"},"content_sha256":"a681dcd00317101b348f60708e80f933bceb05be4bb9a277ca9f5aca40e8eb17","schema_version":"1.0","event_id":"sha256:a681dcd00317101b348f60708e80f933bceb05be4bb9a277ca9f5aca40e8eb17"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2023.findings-emnlp.243) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Wan, Y., Pu, G., Sun, J., Garimella, A., Chang, K.W., Peng, N.: “kelly is a warm person, joseph is a role model”: Gender biases in LLM-generated reference let- ters. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for","arxiv_id":"2608.03627","detector":"doi_compliance","evidence":{"ref_index":38,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.18653/v1/2023","reconstructed_doi":"10.18653/v1/2023.findings-emnlp.243"},"severity":"advisory","ref_index":38,"audited_at":"2026-08-05T15:58:04.980889Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2023.findings-emnlp.243","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"60d43ce97e7c6e270b15c73da51a2985b0b509766404cce0d6b9999378b2e3a4","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18094,"payload_sha256":"738554b6915a7f1a08cfd9b623aefb940993529f18cad07c4e5494495432ce1b","signature_b64":"ExXagdCOHxmKQyUO1DfBNlZIt+DRZQ8pNnttWLR9BPIx78wCEix9t7b4QB9HzHSmW3uqREPqwMAN1IE8zuEQBg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-05T15:58:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i6gh7P1Ta4LgD1k74QV2qVx6S9pNbB8eHRkNOoREVKqesHbDch4fQJXR5qXMfLG98fWpC/tNnzxdpFlMH53jAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:45:37.314452Z"},"content_sha256":"0eac6102579a52e8629102b3cb75ed80d3bb380986f06edfbb3136bf4a936326","schema_version":"1.0","event_id":"sha256:0eac6102579a52e8629102b3cb75ed80d3bb380986f06edfbb3136bf4a936326"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.gebnlp-1.10) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Sobhani, N., Delany, S.: Towards fairer NLP models: Handling gender bias in clas- sification tasks. In: Faleńska, A., Basta, C., Costa-jussà, M., Goldfarb-Tarrant, S., Nozza, D. (eds.) Proceedings of the 5th Workshop on Gender Bias in Natur","arxiv_id":"2608.03627","detector":"doi_compliance","evidence":{"ref_index":33,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.18653/v1/2024","reconstructed_doi":"10.18653/v1/2024.gebnlp-1.10"},"severity":"advisory","ref_index":33,"audited_at":"2026-08-05T15:58:04.980889Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2024.gebnlp-1.10","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"6512c4d26e272f2d3b18f23aad75728d7fc1213a7ca9f548d4d965aa60fc8598","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18093,"payload_sha256":"c829a14b4c93db8ed8c940be9e6d4216209e86c88d4ed916b076484cc71a0fd5","signature_b64":"29Ai2NWwPq05U8QybXYXmKD8aY1mWgbLpUzv97uz+pSHvuzIzOm6t6oAG/+WkHxvJ7zqCBtAm0RXlexuVGDcAw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-05T15:58:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fk6N4ABZTY0V3RmvLtpjVywHcTq0co6aBFxPliZhms8f1/tqFTT3bFaPW2+YYmTDZ9clCG99udZcMROkWrFqAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:45:37.314905Z"},"content_sha256":"d1aa283df78353513c59f6ce3b8481ad52a32d591704646d7e44e45b5c2581fb","schema_version":"1.0","event_id":"sha256:d1aa283df78353513c59f6ce3b8481ad52a32d591704646d7e44e45b5c2581fb"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/TAI.2024.3471735) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Kuntur, S., Wróblewska, A., Paprzycki, M., Ganzha, M.: Under the influence: A survey of large language models in fake news detection. IEEE Transactions on Artificial Intelligence6(2), 458–476 (2025).https://doi.org/10.1109/TAI.2024. 3471735","arxiv_id":"2608.03627","detector":"doi_compliance","evidence":{"ref_index":21,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/tai.2024","reconstructed_doi":"10.1109/TAI.2024.3471735"},"severity":"advisory","ref_index":21,"audited_at":"2026-08-05T15:58:04.980889Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/TAI.2024.3471735","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"984843386b9752e64e2c89237bb1b7580bbba75e5e55ad37ac6bb20528cfa4e4","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18092,"payload_sha256":"89ef373f1b2af97377574cf98dace3c2ce880269ec8de3d362e23c0d88fd0aff","signature_b64":"BnLTs1QR0GHocq2s/exwfZZ+lZbEXM6I3YgxOGq4CZwVlHmuIdJ1d50WKiN+WypWzLHuBc9yyWYBqR627XzyAA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-05T15:58:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ypeqLLuP8zgkgp7Gds1IOOTFFRGRKtgYXt4wdGy38terBySsKNuowrYNqnv8wMTPX7r23p6kXW4f6mXJK23Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:45:37.315304Z"},"content_sha256":"b3fd752deb134ce5bed90007d94db6ebc651e1b5db1ff901d9ee5b9106a1183a","schema_version":"1.0","event_id":"sha256:b3fd752deb134ce5bed90007d94db6ebc651e1b5db1ff901d9ee5b9106a1183a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FEZNRIT7B42UEKYOMHFENSPKTN/bundle.json","state_url":"https://pith.science/pith/FEZNRIT7B42UEKYOMHFENSPKTN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FEZNRIT7B42UEKYOMHFENSPKTN/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":"2026-08-08T04:45:37Z","links":{"resolver":"https://pith.science/pith/FEZNRIT7B42UEKYOMHFENSPKTN","bundle":"https://pith.science/pith/FEZNRIT7B42UEKYOMHFENSPKTN/bundle.json","state":"https://pith.science/pith/FEZNRIT7B42UEKYOMHFENSPKTN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FEZNRIT7B42UEKYOMHFENSPKTN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FEZNRIT7B42UEKYOMHFENSPKTN","merge_version":"pith-open-graph-merge-v1","event_count":5,"valid_event_count":5,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c50aa6349fba47bfa6c9fce078fdc14e7db4ec123bb9a3c5e7fc50466c188da9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T13:14:06Z","title_canon_sha256":"4180c20ba796b10dbc47a7a9820d4ca7dcf5eb28151e6e555e91f8f6fb151a70"},"schema_version":"1.0","source":{"id":"2608.03627","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.03627","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"arxiv_version","alias_value":"2608.03627v1","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03627","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_12","alias_value":"FEZNRIT7B42U","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_16","alias_value":"FEZNRIT7B42UEKYO","created_at":"2026-08-05T01:36:47Z"},{"alias_kind":"pith_short_8","alias_value":"FEZNRIT7","created_at":"2026-08-05T01:36:47Z"}],"graph_snapshots":[{"event_id":"sha256:a681dcd00317101b348f60708e80f933bceb05be4bb9a277ca9f5aca40e8eb17","target":"graph","created_at":"2026-08-05T01:36:47Z","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/2608.03627/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored. This study presents the first systematic investigation of gender bias in LLM-based fake news detection using real-world data. We augment the LIAR benchmark with three gender variants of speaker job titles (Neutral, Male, Female) for each statement to test whether veracity judgments vary solely based on gender presentation. Six state-of-the-art LLMs are evaluated across multiple bias and fairness metrics. All models exhibit gender sensiti","authors_text":"Noel Crespi, Razieh Chalehchaleh, Reza Farahbakhsh","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T13:14:06Z","title":"Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03627","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":"sha256:1817d409274c787dc837e6e370f43602b0c472607acff06e1e58d416494dbe9d","target":"record","created_at":"2026-08-05T01:36:47Z","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":"c50aa6349fba47bfa6c9fce078fdc14e7db4ec123bb9a3c5e7fc50466c188da9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T13:14:06Z","title_canon_sha256":"4180c20ba796b10dbc47a7a9820d4ca7dcf5eb28151e6e555e91f8f6fb151a70"},"schema_version":"1.0","source":{"id":"2608.03627","kind":"arxiv","version":1}},"canonical_sha256":"2932d8a27f0f35422b0e61ca46c9ea9b77efc5d83922b7f0e32bb4f8e8a4f760","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2932d8a27f0f35422b0e61ca46c9ea9b77efc5d83922b7f0e32bb4f8e8a4f760","first_computed_at":"2026-08-05T01:36:47.093108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-05T01:36:47.093108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gAQbMGRjRL2e7Wqe6wex82xNWRxRPLw4jJqCIW8aOIP92opJ3WCi4ORKUS/VINgwDdVIvpNcAGeoR9RvkQkZBg==","signature_status":"signed_v1","signed_at":"2026-08-05T01:36:47.094577Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.03627","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:0eac6102579a52e8629102b3cb75ed80d3bb380986f06edfbb3136bf4a936326","sha256:b3fd752deb134ce5bed90007d94db6ebc651e1b5db1ff901d9ee5b9106a1183a","sha256:d1aa283df78353513c59f6ce3b8481ad52a32d591704646d7e44e45b5c2581fb"]}],"invalid_events":[],"applied_event_ids":["sha256:1817d409274c787dc837e6e370f43602b0c472607acff06e1e58d416494dbe9d","sha256:a681dcd00317101b348f60708e80f933bceb05be4bb9a277ca9f5aca40e8eb17"],"state_sha256":"30e558ddd232f5ad48ba7c3b196b480b7a24a754b3c47f9d4ac967e0d46c53cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vnWm4rCGUfC94C5loTdZQr9zfYDnvai4xtKIQGYZ2r44l6CsTp8iHlxiu5RxOcDWpOydHK/9WSY6ePe9W5RJAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:45:37.318385Z","bundle_sha256":"f259b036abb1bcad082c7ded1c1629cdb4e104b0466d89cecdb046dc3f42703d"}}