{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","short_pith_number":"pith:SMLD5RD7","canonical_record":{"source":{"id":"2608.01755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:24:36Z","cross_cats_sorted":[],"title_canon_sha256":"9f038b8a333d4c3b50a4ad6f45cb7d0fc6c9d9f5fa1de92b9ab96c6e97326e38","abstract_canon_sha256":"fca932e17df74ce3d153910ee5b60eedf216e14919fdc914d963ba82686c92d6"},"schema_version":"1.0"},"canonical_sha256":"93163ec47f2ca62ccb3772f1e7e009fbfe03d7bdfc433f64579452ff5a5dffe4","source":{"kind":"arxiv","id":"2608.01755","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.01755","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"arxiv_version","alias_value":"2608.01755v1","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.01755","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_12","alias_value":"SMLD5RD7FSTC","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_16","alias_value":"SMLD5RD7FSTCZSZX","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_8","alias_value":"SMLD5RD7","created_at":"2026-08-04T02:06:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","target":"record","payload":{"canonical_record":{"source":{"id":"2608.01755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:24:36Z","cross_cats_sorted":[],"title_canon_sha256":"9f038b8a333d4c3b50a4ad6f45cb7d0fc6c9d9f5fa1de92b9ab96c6e97326e38","abstract_canon_sha256":"fca932e17df74ce3d153910ee5b60eedf216e14919fdc914d963ba82686c92d6"},"schema_version":"1.0"},"canonical_sha256":"93163ec47f2ca62ccb3772f1e7e009fbfe03d7bdfc433f64579452ff5a5dffe4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T02:06:48.570567Z","signature_b64":"6gr1R6aluYbEaJ/rMQWyHuvli8vo/tUGM/J8/YbztirzH/ZR6exn8pwmaDNfGa5Br0WalS6X0Eq3fbBRgpFtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93163ec47f2ca62ccb3772f1e7e009fbfe03d7bdfc433f64579452ff5a5dffe4","last_reissued_at":"2026-08-04T02:06:48.568983Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T02:06:48.568983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.01755","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":"2026-08-04T02:06:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X9UoxU89RmXZxDHeJOy8zWOQzkVZBg3wxOl+QJcfF1wq7NVniiUL7qak/21fItLWa3Q6naahjICQ64348nrkDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:09:23.142476Z"},"content_sha256":"9877ab493157bb4acb7c10f085238e0b89f8e66accccb6ddfe8e55caec455a06","schema_version":"1.0","event_id":"sha256:9877ab493157bb4acb7c10f085238e0b89f8e66accccb6ddfe8e55caec455a06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Deqing Wang, Guli Zhang, Hao Geng, Hongyan Xie, Kaixuan Wang, Yakun Zhu, Yang Zhou, Yikun Ban, Zixuan Huang","submitted_at":"2026-08-03T06:24:36Z","abstract_excerpt":"Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision-Language Model (VLM) components, yet existing annotation pipelines commonly expose the teacher model to the logged ground-truth (GT) future trajectory. We empirically show that this induces trajectory anchoring bias: teacher models rationalize the revealed outcome rather than infer a decision from scene evidence, producing less causally faithful CoTs and substantially more severe hallucinations, especially in causal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.01755","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/2608.01755/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":"2026-08-04T02:06:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AukbVzGMhWJuAibIcd8tcuB8l0y9jrsTF1z+BMhtUm87SLMQEBmHBb4iuwDzj0O9FTQlDMF2yCsvZUbrBhePDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:09:23.142966Z"},"content_sha256":"60388069d328aba6b13883ee497c2d086db95439969908249a36ff59819ca048","schema_version":"1.0","event_id":"sha256:60388069d328aba6b13883ee497c2d086db95439969908249a36ff59819ca048"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","target":"integrity","payload":{"note":"Identifier '10.1609/aaai.v39i6.32640.https://ojs.aaai.org/index' 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":"Weijian Ma, Ruoxin Chen, Keyue Zhang, Shuang Wu, and Shouhong Ding. Instruct where the model fails: Generative data augmentation via guided self-contrastive fine-tuning.Proceedings of the AAAI Conference on Artificial Intelligence, 39(6):59","arxiv_id":"2608.01755","detector":"doi_compliance","evidence":{"doi":"10.1609/aaai.v39i6.32640.https://ojs.aaai.org/index","arxiv_id":null,"ref_index":35,"raw_excerpt":"Weijian Ma, Ruoxin Chen, Keyue Zhang, Shuang Wu, and Shouhong Ding. Instruct where the model fails: Generative data augmentation via guided self-contrastive fine-tuning.Proceedings of the AAAI Conference on Artificial Intelligence, 39(6):5991–5999, Apr. 2025. doi: 10.1609/aaai.v39i6.32640.https://ojs.aaai.org/index. php/AAAI/article/view/32640","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":35,"audited_at":"2026-08-07T00:23:00.179787Z","event_type":"pith.integrity.v1","detected_doi":"10.1609/aaai.v39i6.32640.https://ojs.aaai.org/index","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"2c58015d3e7ff96ec4c0bd1e55ed0537e2f06bb525bd4ae6a12a6e71d1d05b2d","paper_version":2,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18422,"payload_sha256":"d0b4b6cb6eaa058d258d0fecc4bb47a35ac5b9a437be264c1d39b8e6b921c137","signature_b64":"kK8/GVRpKs1Y+X5Fo0tWMyo4jilFyRCQlwkVqZl7szDH4RsHxZdxUiaMsJyCN3fzM9fvriQmqPUA6rUhgzdTCw==","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":"2026-08-07T00:23:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ngCxNA21Gc0IiXuSxcc3RJUFM8BmfplZEBiH7Cq15HCAl2wrA8z9asu33opdFkLIVJuyxx6QU7kup/d3ce1zBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:09:23.146504Z"},"content_sha256":"b141d18c37beb609b096dd80dfbdab82649d86e023490894aaafacaedc734d95","schema_version":"1.0","event_id":"sha256:b141d18c37beb609b096dd80dfbdab82649d86e023490894aaafacaedc734d95"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","target":"integrity","payload":{"note":"Identifier '10.18653/v1/2025.findings-emnlp.723.https://aclanthology.org/2025' 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":"Sriram Balasubramanian, Samyadeep Basu, and Soheil Feizi. A closer look at bias and chain-of-thought faith- fulness of large (vision) language models. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng, editor","arxiv_id":"2608.01755","detector":"doi_compliance","evidence":{"doi":"10.18653/v1/2025.findings-emnlp.723.https://aclanthology.org/2025","arxiv_id":null,"ref_index":3,"raw_excerpt":"Sriram Balasubramanian, Samyadeep Basu, and Soheil Feizi. A closer look at bias and chain-of-thought faith- fulness of large (vision) language models. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng, editors,Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13406– 13439, Suzhou, China, November 2025. Association for Computational Linguis","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":3,"audited_at":"2026-08-07T00:23:00.179787Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2025.findings-emnlp.723.https://aclanthology.org/2025","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"0c778400417e17683bfd4832461bd0dc4b405cf615566fd3443b595f6f975f91","paper_version":2,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18421,"payload_sha256":"f62f05ef1019b54e3bf9d631f3e657e03e916d5e49a499639d4956f16d717b50","signature_b64":"Hg/qWumQjOt3YvN6OZq0gK4plYxllncqFcnIyBk0g3/5aO3zr8idVbFNN8D7uhGwSeBfU99fs0/QqQOyauZ9BQ==","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":"2026-08-07T00:23:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yLYp3hproKffhpx7YwwXGr4vnVO051ZeUSO1Fe5hZ+l29H3+8sjruoRfr3eS96IrPReIC6SvaOMXYOMg/GPgBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:09:23.146926Z"},"content_sha256":"38fc3f88109c29022b90f7b5dc321eb214884b6e251a77a488db594a3b041b8d","schema_version":"1.0","event_id":"sha256:38fc3f88109c29022b90f7b5dc321eb214884b6e251a77a488db594a3b041b8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/bundle.json","state_url":"https://pith.science/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/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":"2026-08-07T06:09:23Z","links":{"resolver":"https://pith.science/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P","bundle":"https://pith.science/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/bundle.json","state":"https://pith.science/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SMLD5RD7FSTCZSZXOLY6PYAJ7P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SMLD5RD7FSTCZSZXOLY6PYAJ7P","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fca932e17df74ce3d153910ee5b60eedf216e14919fdc914d963ba82686c92d6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:24:36Z","title_canon_sha256":"9f038b8a333d4c3b50a4ad6f45cb7d0fc6c9d9f5fa1de92b9ab96c6e97326e38"},"schema_version":"1.0","source":{"id":"2608.01755","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.01755","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"arxiv_version","alias_value":"2608.01755v1","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.01755","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_12","alias_value":"SMLD5RD7FSTC","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_16","alias_value":"SMLD5RD7FSTCZSZX","created_at":"2026-08-04T02:06:48Z"},{"alias_kind":"pith_short_8","alias_value":"SMLD5RD7","created_at":"2026-08-04T02:06:48Z"}],"graph_snapshots":[{"event_id":"sha256:60388069d328aba6b13883ee497c2d086db95439969908249a36ff59819ca048","target":"graph","created_at":"2026-08-04T02:06:48Z","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/2608.01755/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision-Language Model (VLM) components, yet existing annotation pipelines commonly expose the teacher model to the logged ground-truth (GT) future trajectory. We empirically show that this induces trajectory anchoring bias: teacher models rationalize the revealed outcome rather than infer a decision from scene evidence, producing less causally faithful CoTs and substantially more severe hallucinations, especially in causal","authors_text":"Deqing Wang, Guli Zhang, Hao Geng, Hongyan Xie, Kaixuan Wang, Yakun Zhu, Yang Zhou, Yikun Ban, Zixuan Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:24:36Z","title":"Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.01755","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":"sha256:9877ab493157bb4acb7c10f085238e0b89f8e66accccb6ddfe8e55caec455a06","target":"record","created_at":"2026-08-04T02:06:48Z","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":"fca932e17df74ce3d153910ee5b60eedf216e14919fdc914d963ba82686c92d6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:24:36Z","title_canon_sha256":"9f038b8a333d4c3b50a4ad6f45cb7d0fc6c9d9f5fa1de92b9ab96c6e97326e38"},"schema_version":"1.0","source":{"id":"2608.01755","kind":"arxiv","version":1}},"canonical_sha256":"93163ec47f2ca62ccb3772f1e7e009fbfe03d7bdfc433f64579452ff5a5dffe4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93163ec47f2ca62ccb3772f1e7e009fbfe03d7bdfc433f64579452ff5a5dffe4","first_computed_at":"2026-08-04T02:06:48.568983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-04T02:06:48.568983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6gr1R6aluYbEaJ/rMQWyHuvli8vo/tUGM/J8/YbztirzH/ZR6exn8pwmaDNfGa5Br0WalS6X0Eq3fbBRgpFtDw==","signature_status":"signed_v1","signed_at":"2026-08-04T02:06:48.570567Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.01755","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:38fc3f88109c29022b90f7b5dc321eb214884b6e251a77a488db594a3b041b8d","sha256:b141d18c37beb609b096dd80dfbdab82649d86e023490894aaafacaedc734d95"]}],"invalid_events":[],"applied_event_ids":["sha256:9877ab493157bb4acb7c10f085238e0b89f8e66accccb6ddfe8e55caec455a06","sha256:60388069d328aba6b13883ee497c2d086db95439969908249a36ff59819ca048"],"state_sha256":"5458d174887ea415437408a293526ce5c8b2b28eb7fd458c4c9e30df6648bc6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LW4vfB4bZD5k3+OJdFdO9V1Vvm04dMvqM7MLwC/5JRMeGRZfj13KLOXZHi/+I6g66k1arovKJ/BRUdmp8XRBCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:09:23.149589Z","bundle_sha256":"0d717ce32712b7ad93904baa22460062f0fac72ad09fa2b870b8e022f98b9ec8"}}