{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","short_pith_number":"pith:AT7YZUKP","canonical_record":{"source":{"id":"2607.10221","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-11T09:21:01Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1279eb9e59cbb514ad29fce3910e4b5cbfff1c1c7f81df6a9282fa5d938ea58e","abstract_canon_sha256":"cf7644192f6c41b8bf5b0cbc3f8f232f1f0f15fc012dcaa971c711d306b060cb"},"schema_version":"1.0"},"canonical_sha256":"04ff8cd14fb3a6da8bbdf1c3212edbc441113edaaf37b34dcf53e79614a30740","source":{"kind":"arxiv","id":"2607.10221","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.10221","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"arxiv_version","alias_value":"2607.10221v1","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10221","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_12","alias_value":"AT7YZUKPWOTN","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_16","alias_value":"AT7YZUKPWOTNVC55","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_8","alias_value":"AT7YZUKP","created_at":"2026-07-14T01:20:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","target":"record","payload":{"canonical_record":{"source":{"id":"2607.10221","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-11T09:21:01Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"1279eb9e59cbb514ad29fce3910e4b5cbfff1c1c7f81df6a9282fa5d938ea58e","abstract_canon_sha256":"cf7644192f6c41b8bf5b0cbc3f8f232f1f0f15fc012dcaa971c711d306b060cb"},"schema_version":"1.0"},"canonical_sha256":"04ff8cd14fb3a6da8bbdf1c3212edbc441113edaaf37b34dcf53e79614a30740","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:31.428594Z","signature_b64":"kRnsnH3lssbmIw4sCMZ+kg7aglc0cNFkYjBBSTPCau6Oku4Yve9YO3a2YXIWfhBmGvZ+GNC1DCqOGQWFiVBACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04ff8cd14fb3a6da8bbdf1c3212edbc441113edaaf37b34dcf53e79614a30740","last_reissued_at":"2026-07-14T01:20:31.427715Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:31.427715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.10221","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":"2026-07-14T01:20:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0+RGER6z7/pl88HLku5pzsFylof4iJWiJfVIyPn1qiy/moxBrPJFJ8T1SPlnQ+3+SxvoqkapjqMXEuvWM3ANCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:46:03.385650Z"},"content_sha256":"d73f898138833a2dcbf23dcc2eb6ef6eb8ac73c9f778680140b291266217c9ec","schema_version":"1.0","event_id":"sha256:d73f898138833a2dcbf23dcc2eb6ef6eb8ac73c9f778680140b291266217c9ec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.SE","authors_text":"Anh M. T. Bui, Lam D. Dao, Phuong T. Nguyen, Vang T. Nguyen","submitted_at":"2026-07-11T09:21:01Z","abstract_excerpt":"Background. Large language models (LLMs) have become increasingly capable of understanding and generating source code, leading to their widespread adoption in software engineering tasks such as code completion, repair, and vulnerability detection. However, despite their strong empirical performance, the internal mechanisms through which LLMs recognize malicious or vulnerable code patterns remain poorly understood. Aim. We investigated where the malware detection behavior is encoded inside LLMs Feed Forward Network (FFN) neurons and verified the attribution with causal interventions on the neur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10221","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/2607.10221/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":"2026-07-14T01:20:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Bsaq4PxkB0Iwrz9fIBh0cy+rZIxiuMd2CazcttT4Bi3xbMgR9zyw8lRDIk/lVikWfnRxhOHbUUHSONsAmikBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:46:03.386594Z"},"content_sha256":"9a7f9153bc673e83fe33fb58ef87ccca467409d2399c3722954cdc608367996a","schema_version":"1.0","event_id":"sha256:9a7f9153bc673e83fe33fb58ef87ccca467409d2399c3722954cdc608367996a"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","target":"integrity","payload":{"note":"Identifier '10.18653/v1/2024' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Association for Computational Linguist- ics. URL: https://aclanthology.org/2024.findings-naacl.130/, doi:10.18653/v1/2024. findings-naacl.130. 17 Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classificat","arxiv_id":"2607.10221","detector":"doi_compliance","evidence":{"doi":"10.18653/v1/2024","arxiv_id":null,"ref_index":11,"raw_excerpt":"Association for Computational Linguist- ics. URL: https://aclanthology.org/2024.findings-naacl.130/, doi:10.18653/v1/2024. findings-naacl.130. 17 Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classification tasks.Information processing & management, 45(4):427–437,","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":11,"audited_at":"2026-07-29T15:01:59.664185Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2024","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"eb282e17b8719590303a72d1fb88d0fd10c698a1bdd22b7c05b5cbcc29ca8607","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":13744,"payload_sha256":"4aab5fa737d17516c1d1af3931c66e43ad2686847ef8cfa8033b6bf061818425","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T15:06:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nq7bXQUnXEz23i0bnjn5NHcahVdIxidUAwX85EyiEU5L61l3s2ifAynya18u8ZQmp/HaZQpq1QhZ4iA5Z/YcBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:46:03.391214Z"},"content_sha256":"197685989a3dfabce0d20b4a7104b010a94ec7971201569663e60f871168c20c","schema_version":"1.0","event_id":"sha256:197685989a3dfabce0d20b4a7104b010a94ec7971201569663e60f871168c20c"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.48550/ARXIV.2311.17035) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"URL: https://doi.org/10.48550/arXiv. 2311.17035,arXiv:2311.17035,doi:10.48550/ARXIV.2311.17035. 12 Robert G Newcombe. Two-sided confidence intervals for the single proportion: comparison of seven methods.Statistics in medicine, 17(8):857–87","arxiv_id":"2607.10221","detector":"doi_compliance","evidence":{"ref_index":8,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.48550/arxiv","reconstructed_doi":"10.48550/ARXIV.2311.17035"},"severity":"advisory","ref_index":8,"audited_at":"2026-07-29T15:01:59.664185Z","event_type":"pith.integrity.v1","detected_doi":"10.48550/ARXIV.2311.17035","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"1a14f63e54a260b81725d60cdd9c9b9f4ef92d1856369b7b0af8807a67399e85","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":13743,"payload_sha256":"3ea0c9bf8cba3703b62a7c1d84e07bea393664f5c0669660b54572627a2ea6b3","signature_b64":null,"signing_key_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T15:06:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jZ+7TG45TPwM1aAk8LvJKIgV5sqP6dsV6NlInUBkSXviTnqoCj7gHW80BXKNfUsustrOoRqwFekZfLDY6fxpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:46:03.391869Z"},"content_sha256":"8687964a98d0f71ebb647b6ea9051dc604384466cd1953bbcb9d389c28e04b98","schema_version":"1.0","event_id":"sha256:8687964a98d0f71ebb647b6ea9051dc604384466cd1953bbcb9d389c28e04b98"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.findings-naacl.130.17) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Association for Computational Linguist- ics. URL: https://aclanthology.org/2024.findings-naacl.130/, doi:10.18653/v1/2024. findings-naacl.130. 17 Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classificat","arxiv_id":"2607.10221","detector":"doi_compliance","evidence":{"ref_index":11,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Association for Computational Linguist- ics. URL: https://aclanthology.org/2024.findings-naacl.130/, doi:10.18653/v1/2024. findings-naacl.130. 17 Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classificat","reconstructed_doi":"10.18653/v1/2024.findings-naacl.130.17"},"severity":"advisory","ref_index":11,"audited_at":"2026-07-14T13:28:51.347503Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2024.findings-naacl.130.17","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"5bcbcd0e22b41888a81b0f1044aac69e92541219d072ccef3b82c3d6f714fa24","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":13347,"payload_sha256":"573173d3924b2300f7f447dbb4252bb9c571ec436e9393ae0f4eb697a5876ced","signature_b64":"Cj3s44uVRwsQbY/Zj/rdy1fxmWQF9H6UPNuPHe7H/Z5RRiR686vFWL1H6TwPioi3wVkv5p7bfed0PCWlYIf/BA==","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":"2026-07-14T13:31:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vZ+Of3DvM2nsJIhREZdQ5E1DQXpWjS76EiYHAreVWwameMqcds3GlFcpzTIxUU3ebSucnCqwFfoL02mjOsjEAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T01:46:03.392568Z"},"content_sha256":"f7f44bdc697f63284f43c09eaaa44671d87099c5e13f1cc616decbffeecc3b30","schema_version":"1.0","event_id":"sha256:f7f44bdc697f63284f43c09eaaa44671d87099c5e13f1cc616decbffeecc3b30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AT7YZUKPWOTNVC556HBSCLW3YR/bundle.json","state_url":"https://pith.science/pith/AT7YZUKPWOTNVC556HBSCLW3YR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AT7YZUKPWOTNVC556HBSCLW3YR/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":"2026-08-05T01:46:03Z","links":{"resolver":"https://pith.science/pith/AT7YZUKPWOTNVC556HBSCLW3YR","bundle":"https://pith.science/pith/AT7YZUKPWOTNVC556HBSCLW3YR/bundle.json","state":"https://pith.science/pith/AT7YZUKPWOTNVC556HBSCLW3YR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AT7YZUKPWOTNVC556HBSCLW3YR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AT7YZUKPWOTNVC556HBSCLW3YR","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":"cf7644192f6c41b8bf5b0cbc3f8f232f1f0f15fc012dcaa971c711d306b060cb","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-11T09:21:01Z","title_canon_sha256":"1279eb9e59cbb514ad29fce3910e4b5cbfff1c1c7f81df6a9282fa5d938ea58e"},"schema_version":"1.0","source":{"id":"2607.10221","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.10221","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"arxiv_version","alias_value":"2607.10221v1","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10221","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_12","alias_value":"AT7YZUKPWOTN","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_16","alias_value":"AT7YZUKPWOTNVC55","created_at":"2026-07-14T01:20:31Z"},{"alias_kind":"pith_short_8","alias_value":"AT7YZUKP","created_at":"2026-07-14T01:20:31Z"}],"graph_snapshots":[{"event_id":"sha256:9a7f9153bc673e83fe33fb58ef87ccca467409d2399c3722954cdc608367996a","target":"graph","created_at":"2026-07-14T01:20:31Z","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/2607.10221/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Background. Large language models (LLMs) have become increasingly capable of understanding and generating source code, leading to their widespread adoption in software engineering tasks such as code completion, repair, and vulnerability detection. However, despite their strong empirical performance, the internal mechanisms through which LLMs recognize malicious or vulnerable code patterns remain poorly understood. Aim. We investigated where the malware detection behavior is encoded inside LLMs Feed Forward Network (FFN) neurons and verified the attribution with causal interventions on the neur","authors_text":"Anh M. T. Bui, Lam D. Dao, Phuong T. Nguyen, Vang T. Nguyen","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-11T09:21:01Z","title":"Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10221","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:d73f898138833a2dcbf23dcc2eb6ef6eb8ac73c9f778680140b291266217c9ec","target":"record","created_at":"2026-07-14T01:20:31Z","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":"cf7644192f6c41b8bf5b0cbc3f8f232f1f0f15fc012dcaa971c711d306b060cb","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-11T09:21:01Z","title_canon_sha256":"1279eb9e59cbb514ad29fce3910e4b5cbfff1c1c7f81df6a9282fa5d938ea58e"},"schema_version":"1.0","source":{"id":"2607.10221","kind":"arxiv","version":1}},"canonical_sha256":"04ff8cd14fb3a6da8bbdf1c3212edbc441113edaaf37b34dcf53e79614a30740","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04ff8cd14fb3a6da8bbdf1c3212edbc441113edaaf37b34dcf53e79614a30740","first_computed_at":"2026-07-14T01:20:31.427715Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T01:20:31.427715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kRnsnH3lssbmIw4sCMZ+kg7aglc0cNFkYjBBSTPCau6Oku4Yve9YO3a2YXIWfhBmGvZ+GNC1DCqOGQWFiVBACQ==","signature_status":"signed_v1","signed_at":"2026-07-14T01:20:31.428594Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.10221","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:197685989a3dfabce0d20b4a7104b010a94ec7971201569663e60f871168c20c","sha256:8687964a98d0f71ebb647b6ea9051dc604384466cd1953bbcb9d389c28e04b98","sha256:f7f44bdc697f63284f43c09eaaa44671d87099c5e13f1cc616decbffeecc3b30"]}],"invalid_events":[],"applied_event_ids":["sha256:d73f898138833a2dcbf23dcc2eb6ef6eb8ac73c9f778680140b291266217c9ec","sha256:9a7f9153bc673e83fe33fb58ef87ccca467409d2399c3722954cdc608367996a"],"state_sha256":"cc47dc0083960d5012a52f36ea1ecf0d84fce4de9bf6aa21e71d3ae0485dfd0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"59h2JwsgjF9xlFJF+3x1f9D58V7oNTV6EbDKGvAfRIAORkrZ0nODm260blfgyYa2twS7PB3bItq/g11MKQfFCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T01:46:03.397716Z","bundle_sha256":"01a2dbe8676b6f1254d70914709cdd9ce64b38f533c7c9602992a8459fe9edb5"}}