{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","short_pith_number":"pith:DO3JTEXF","canonical_record":{"source":{"id":"2605.15942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:27:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ea187a45bc71ff9692217a11c6d03fdaa2fc9177aba74a43b73f3843dd4bcfaa","abstract_canon_sha256":"45b6672d517bfa7fc312383ce76f370d2c4d741dd3fc2b83c039cf2829bcc25c"},"schema_version":"1.0"},"canonical_sha256":"1bb69992e51ca5338d5fcb6036845f58b88468cbc0e576dcd0f64830d8ab5708","source":{"kind":"arxiv","id":"2605.15942","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15942","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15942v1","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15942","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"DO3JTEXFDSST","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"DO3JTEXFDSSTHDK7","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"DO3JTEXF","created_at":"2026-05-20T00:01:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15942","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:27:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ea187a45bc71ff9692217a11c6d03fdaa2fc9177aba74a43b73f3843dd4bcfaa","abstract_canon_sha256":"45b6672d517bfa7fc312383ce76f370d2c4d741dd3fc2b83c039cf2829bcc25c"},"schema_version":"1.0"},"canonical_sha256":"1bb69992e51ca5338d5fcb6036845f58b88468cbc0e576dcd0f64830d8ab5708","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:45.981886Z","signature_b64":"xbZyQg4AsUu/2EY0QH1Wu8a2apRqTZ7lXIsDa6zhZg9IeTIPPJBHZWlG9QKa7/Wltglf+s8YYFamuKk0tKVcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1bb69992e51ca5338d5fcb6036845f58b88468cbc0e576dcd0f64830d8ab5708","last_reissued_at":"2026-05-20T00:01:45.981371Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:45.981371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15942","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":"2026-05-20T00:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IUc7VbliD+77dEGFEaJjPBlClsl8lmE/CUniBHT9qIca9x9VhxQiVdkQjVM8hHIIY+jLBh8GM9FbW9gWmwjaDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:34:49.313903Z"},"content_sha256":"033edc224a3b8f1e5eea808536d2e918008540cce7e59aab6bf5015d7c521fe4","schema_version":"1.0","event_id":"sha256:033edc224a3b8f1e5eea808536d2e918008540cce7e59aab6bf5015d7c521fe4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenhao Wang, Yao Zhu, Yingrui Ji, Yu Meng","submitted_at":"2026-05-15T13:27:01Z","abstract_excerpt":"Open-vocabulary segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained descriptions are typically encoded as holistic sentences that entangle multiple semantic units. We propose a Decomposed Vision-Language Alignment framework that explicitly factorizes textual prompts into a concept token and multiple attribute tokens, enabling separate cross-modal interactions for each semantic unit. At the feature level, we introduce a Feature-Gated Cross-Attention module that generates attribute-specific gating maps to fuse informat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15942","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/2605.15942/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:44.886323Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.724609Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"832522e9f83ce5b81e0cab25d7631343216ddf060756166d6f7710e9320aece5"},"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":"2026-05-20T00:01:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PwO2W0d+ezxdaiNIyGQ/0OjDph53mwSy84eirtGuCPcgAW6YRsoT/vYGu2BxajAtAHPkDQ39bVaNgIQ2AC24AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:34:49.314303Z"},"content_sha256":"e94e740a36ba379015653d2f49b05852367adc4b40afab3c355e08611ff7b7fb","schema_version":"1.0","event_id":"sha256:e94e740a36ba379015653d2f49b05852367adc4b40afab3c355e08611ff7b7fb"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1007/978-3-031-19842-7_42) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Xu, J., Hou, J., Zhang, Y., Feng, R., Wang, Y., Qiao, Y., Xie, W.: A simple base- line for open-vocabulary semantic segmentation with pre-trained vision-language model. In: ECCV. pp. 736–753 (2022).https://doi.org/10.1007/978-3-031- 19842-7","arxiv_id":"2605.15942","detector":"doi_compliance","evidence":{"ref_index":34,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Xu, J., Hou, J., Zhang, Y., Feng, R., Wang, Y., Qiao, Y., Xie, W.: A simple base- line for open-vocabulary semantic segmentation with pre-trained vision-language model. In: ECCV. pp. 736–753 (2022).https://doi.org/10.1007/978-3-031- 19842-7","reconstructed_doi":"10.1007/978-3-031-19842-7_42"},"severity":"advisory","ref_index":34,"audited_at":"2026-05-20T18:54:22.964963Z","event_type":"pith.integrity.v1","detected_doi":"10.1007/978-3-031-19842-7_42","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"17dfba111a21524ca6721b5e3813bb1c1b76e45fa5071a9c97607e825dbc6c89","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5413,"payload_sha256":"9d46921332e7dd91f03820eeb9f50740c593df2d6c809117a3cdfa64a99769bb","signature_b64":"YUwyFJKp4XusLh3tRJR4mcFsHTmBoky+MHHGRr1ap8PMWue/QwCGr5Qk0Q6InyXUh6stfVTH1PVLUrA/+M4WDg==","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":"2026-05-20T18:58:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VqvG13GASGFy/fwglJ+HrMssFHIME7s7QWFay7vMP8Igi2clDVL4GrMYKtkYT0pm1MUmm9+/UoO4o9wlqsqGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:34:49.315270Z"},"content_sha256":"d87c6f7042d9a3774d57a084bd09ba3c76ebb2ab712d89de51849b4bbbace3fe","schema_version":"1.0","event_id":"sha256:d87c6f7042d9a3774d57a084bd09ba3c76ebb2ab712d89de51849b4bbbace3fe"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/TPAMI.2015.2487979) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for image classification. PAMI38(7), 1425–1438 (2016).https://doi.org/10.1109/TPAMI. 2015.2487979","arxiv_id":"2605.15942","detector":"doi_compliance","evidence":{"ref_index":1,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Akata, Z., Perronnin, F., Harchaoui, Z., Schmid, C.: Label-embedding for image classification. PAMI38(7), 1425–1438 (2016).https://doi.org/10.1109/TPAMI. 2015.2487979","reconstructed_doi":"10.1109/TPAMI.2015.2487979"},"severity":"advisory","ref_index":1,"audited_at":"2026-05-20T18:54:22.964963Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/TPAMI.2015.2487979","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"89412b68a7c367445a9eb6a06a7927eef53da7f90c665acf03160ab7b5b3cad6","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":5412,"payload_sha256":"7952cf8a9a796f752d55778b5ebd7c4299b7b353f7fd2cfe343f5997849bf4dd","signature_b64":"+I9+FSGQ2sNrM988DaTJF4/4K01qydr58IQ8xPzyVhaZVpgDL65MaTu/Kbn+Y9CKUFTwPhjiWZ6HBA7f2rvnAg==","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":"2026-05-20T18:58:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTU0k7VhCnVPINL4Q2Kv3sNKFQzeeZdAPKy/4FtibroFKrC4IAZZzzEREXfMjZjd1LhNm3MIGMwRFDkTGnK2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:34:49.315588Z"},"content_sha256":"c925961b245a3ae420be42faf65fc080d1d8c46cce5e903e09274c668f584196","schema_version":"1.0","event_id":"sha256:c925961b245a3ae420be42faf65fc080d1d8c46cce5e903e09274c668f584196"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/bundle.json","state_url":"https://pith.science/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/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":"2026-05-21T04:34:49Z","links":{"resolver":"https://pith.science/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC","bundle":"https://pith.science/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/bundle.json","state":"https://pith.science/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DO3JTEXFDSSTHDK7ZNQDNBC7LC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DO3JTEXFDSSTHDK7ZNQDNBC7LC","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":"45b6672d517bfa7fc312383ce76f370d2c4d741dd3fc2b83c039cf2829bcc25c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:27:01Z","title_canon_sha256":"ea187a45bc71ff9692217a11c6d03fdaa2fc9177aba74a43b73f3843dd4bcfaa"},"schema_version":"1.0","source":{"id":"2605.15942","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15942","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15942v1","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15942","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_12","alias_value":"DO3JTEXFDSST","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_16","alias_value":"DO3JTEXFDSSTHDK7","created_at":"2026-05-20T00:01:45Z"},{"alias_kind":"pith_short_8","alias_value":"DO3JTEXF","created_at":"2026-05-20T00:01:45Z"}],"graph_snapshots":[{"event_id":"sha256:e94e740a36ba379015653d2f49b05852367adc4b40afab3c355e08611ff7b7fb","target":"graph","created_at":"2026-05-20T00:01:45Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:44.886323Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.724609Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15942/integrity.json","findings":[],"snapshot_sha256":"832522e9f83ce5b81e0cab25d7631343216ddf060756166d6f7710e9320aece5","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Open-vocabulary segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained descriptions are typically encoded as holistic sentences that entangle multiple semantic units. We propose a Decomposed Vision-Language Alignment framework that explicitly factorizes textual prompts into a concept token and multiple attribute tokens, enabling separate cross-modal interactions for each semantic unit. At the feature level, we introduce a Feature-Gated Cross-Attention module that generates attribute-specific gating maps to fuse informat","authors_text":"Chenhao Wang, Yao Zhu, Yingrui Ji, Yu Meng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:27:01Z","title":"Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15942","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":"sha256:033edc224a3b8f1e5eea808536d2e918008540cce7e59aab6bf5015d7c521fe4","target":"record","created_at":"2026-05-20T00:01:45Z","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":"45b6672d517bfa7fc312383ce76f370d2c4d741dd3fc2b83c039cf2829bcc25c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:27:01Z","title_canon_sha256":"ea187a45bc71ff9692217a11c6d03fdaa2fc9177aba74a43b73f3843dd4bcfaa"},"schema_version":"1.0","source":{"id":"2605.15942","kind":"arxiv","version":1}},"canonical_sha256":"1bb69992e51ca5338d5fcb6036845f58b88468cbc0e576dcd0f64830d8ab5708","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1bb69992e51ca5338d5fcb6036845f58b88468cbc0e576dcd0f64830d8ab5708","first_computed_at":"2026-05-20T00:01:45.981371Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:45.981371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xbZyQg4AsUu/2EY0QH1Wu8a2apRqTZ7lXIsDa6zhZg9IeTIPPJBHZWlG9QKa7/Wltglf+s8YYFamuKk0tKVcCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:45.981886Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15942","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:c925961b245a3ae420be42faf65fc080d1d8c46cce5e903e09274c668f584196","sha256:d87c6f7042d9a3774d57a084bd09ba3c76ebb2ab712d89de51849b4bbbace3fe"]}],"invalid_events":[],"applied_event_ids":["sha256:033edc224a3b8f1e5eea808536d2e918008540cce7e59aab6bf5015d7c521fe4","sha256:e94e740a36ba379015653d2f49b05852367adc4b40afab3c355e08611ff7b7fb"],"state_sha256":"3563ff46d64a689ca0b8d468183c5a8640f8be611b9fa2a82fca5153d467cacd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O3vfXx1cuwQHIyvfmiCIdTZNAiAJtI3RVvWGkabZrgl0bKgT20J2n87HWQZorQ0SP3pnfnM836AsodKsCf0DAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T04:34:49.318042Z","bundle_sha256":"5d939e0db2b3f31df0904c1db06f09d1de1d949c349597c3ed8430a0c2d050ba"}}