{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B7Y4EP6U5N6XG4KRKVBK24SI4Y","short_pith_number":"pith:B7Y4EP6U","canonical_record":{"source":{"id":"2605.16871","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-16T08:18:47Z","cross_cats_sorted":[],"title_canon_sha256":"5508fc7b3e26bdf750d4adb20c32c37df7524bb7a8f5a8c567e712822b81f0ce","abstract_canon_sha256":"0ebcf7089c0ce3380b126e36805d9f5ecebbe9c98230d9234242f7f51b042317"},"schema_version":"1.0"},"canonical_sha256":"0ff1c23fd4eb7d7371515542ad7248e63d8690dff728952bdc3415ac4d727019","source":{"kind":"arxiv","id":"2605.16871","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16871","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16871v1","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16871","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"B7Y4EP6U5N6X","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_16","alias_value":"B7Y4EP6U5N6XG4KR","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_8","alias_value":"B7Y4EP6U","created_at":"2026-05-20T00:03:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B7Y4EP6U5N6XG4KRKVBK24SI4Y","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16871","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-16T08:18:47Z","cross_cats_sorted":[],"title_canon_sha256":"5508fc7b3e26bdf750d4adb20c32c37df7524bb7a8f5a8c567e712822b81f0ce","abstract_canon_sha256":"0ebcf7089c0ce3380b126e36805d9f5ecebbe9c98230d9234242f7f51b042317"},"schema_version":"1.0"},"canonical_sha256":"0ff1c23fd4eb7d7371515542ad7248e63d8690dff728952bdc3415ac4d727019","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:27.459107Z","signature_b64":"J/Hw/PPQpN6wKQV85fjsf/4XuTiAV9W7ieJ10TEj92pSkHmpoxiWoDlbOWcZC04T7os+xf+J9oAY/GT3JskwBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ff1c23fd4eb7d7371515542ad7248e63d8690dff728952bdc3415ac4d727019","last_reissued_at":"2026-05-20T00:03:27.458428Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:27.458428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16871","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":"2026-05-20T00:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DHP2DINgkL90X9aPFyKAAvurFKCUSBfJ1mBREH6fT13KYEjBGfQ9l7+xUaIc27GpnYSaI5K92MyCpg0AKaqZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:39:51.659518Z"},"content_sha256":"2c3c75720a46421624f831f8a59d4b668a8f2b24db68ae186674f428f2ec3583","schema_version":"1.0","event_id":"sha256:2c3c75720a46421624f831f8a59d4b668a8f2b24db68ae186674f428f2ec3583"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B7Y4EP6U5N6XG4KRKVBK24SI4Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SADP: Subgoal-Aware Diffusion Policy for Explainable Robots Learned from Foundation Model Generated Demonstrations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Site Hu, Takato Horii","submitted_at":"2026-05-16T08:18:47Z","abstract_excerpt":"Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner. However, most imitation learning methods are trained solely on task-level demonstrations, without explicitly modeling subgoal structure or execution progress. This limitation is further exacerbated by the scarcity of subgoal-level supervision in standard robot learning datasets, which restricts the development of robots that can convey the subtasks they are executing during long-horizon manipulation. To address this issue, this paper proposes "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments in RLBench simulations and real-world evaluations on a UR5e robot demonstrate that SADP achieves higher task success rates than strong task-conditioned diffusion baselines, while providing subgoal-level execution signals for monitoring progress and diagnosing failures.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Foundation models can autonomously generate accurate, unbiased subgoal annotations from raw task demonstrations without introducing systematic errors that would degrade downstream policy learning or explainability.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SADP trains diffusion policies on foundation-model-generated subgoal-annotated demonstrations and adds a completion predictor to give robots built-in, subgoal-level explainability alongside improved task performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b313d615acee8d7b10c2c749619b964292155dea288f1ceaf04c07558beff267"},"source":{"id":"2605.16871","kind":"arxiv","version":1},"verdict":{"id":"e75007f2-eaf7-49a8-a2af-770e528dfba1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:47:41.639676Z","strongest_claim":"Experiments in RLBench simulations and real-world evaluations on a UR5e robot demonstrate that SADP achieves higher task success rates than strong task-conditioned diffusion baselines, while providing subgoal-level execution signals for monitoring progress and diagnosing failures.","one_line_summary":"SADP trains diffusion policies on foundation-model-generated subgoal-annotated demonstrations and adds a completion predictor to give robots built-in, subgoal-level explainability alongside improved task performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Foundation models can autonomously generate accurate, unbiased subgoal annotations from raw task demonstrations without introducing systematic errors that would degrade downstream policy learning or explainability.","pith_extraction_headline":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16871/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T21:01:24.955218Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.219289Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.297984Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.374379Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f743495df5d1830143eb052ae1597c02323247593dfd1850c5f54583bbfcb571"},"references":{"count":40,"sample":[{"doi":"","year":2017,"title":"Transparent, explainable, and accountable ai for robotics,","work_id":"1bb30ea2-44a7-4188-9ef2-e2d55235a0bb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"A review of robot learning for manipulation: Challenges, representations, and algorithms,","work_id":"1b28e85d-e519-4a08-9713-e5b873bd6430","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"A survey of demonstration learning,","work_id":"84d45fbd-7f7b-4f0d-b156-b6095d1ce7eb","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Hierarchical reinforce- ment learning: A survey and open research challenges,","work_id":"de9862f0-13cc-4eb8-9c8c-50b62397917d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction,","work_id":"5cca4f0a-bb88-4509-b90b-189cc9120011","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":40,"snapshot_sha256":"c111967b02a1c8068a19bd1a7087804343cefc1660cb7d9effe5cf11328e8dec","internal_anchors":2},"formal_canon":{"evidence_count":1,"snapshot_sha256":"0c7a65a6f1edd49b9a6983069b038d5d5343fafbb105a61c852e9735de814eef"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"e75007f2-eaf7-49a8-a2af-770e528dfba1"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Z4aV3+4fyqq5eRpXBrOFwQ4jegzS1b8bKoY8P2Al1ZoMs0t6VPQsBtgROJT1fhs0ICuxRxzPv7VO2sTk77rDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:39:51.660107Z"},"content_sha256":"2c2c998b9814487fab5d045dc56fd89b398b8d3019a3c258e6bcef4fe1221f2a","schema_version":"1.0","event_id":"sha256:2c2c998b9814487fab5d045dc56fd89b398b8d3019a3c258e6bcef4fe1221f2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/bundle.json","state_url":"https://pith.science/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/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":"2026-06-01T11:39:51Z","links":{"resolver":"https://pith.science/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y","bundle":"https://pith.science/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/bundle.json","state":"https://pith.science/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B7Y4EP6U5N6XG4KRKVBK24SI4Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B7Y4EP6U5N6XG4KRKVBK24SI4Y","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":"0ebcf7089c0ce3380b126e36805d9f5ecebbe9c98230d9234242f7f51b042317","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-16T08:18:47Z","title_canon_sha256":"5508fc7b3e26bdf750d4adb20c32c37df7524bb7a8f5a8c567e712822b81f0ce"},"schema_version":"1.0","source":{"id":"2605.16871","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16871","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16871v1","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16871","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"B7Y4EP6U5N6X","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_16","alias_value":"B7Y4EP6U5N6XG4KR","created_at":"2026-05-20T00:03:27Z"},{"alias_kind":"pith_short_8","alias_value":"B7Y4EP6U","created_at":"2026-05-20T00:03:27Z"}],"graph_snapshots":[{"event_id":"sha256:2c2c998b9814487fab5d045dc56fd89b398b8d3019a3c258e6bcef4fe1221f2a","target":"graph","created_at":"2026-05-20T00:03:27Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experiments in RLBench simulations and real-world evaluations on a UR5e robot demonstrate that SADP achieves higher task success rates than strong task-conditioned diffusion baselines, while providing subgoal-level execution signals for monitoring progress and diagnosing failures."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"Foundation models can autonomously generate accurate, unbiased subgoal annotations from raw task demonstrations without introducing systematic errors that would degrade downstream policy learning or explainability."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SADP trains diffusion policies on foundation-model-generated subgoal-annotated demonstrations and adds a completion predictor to give robots built-in, subgoal-level explainability alongside improved task performance."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation."}],"snapshot_sha256":"b313d615acee8d7b10c2c749619b964292155dea288f1ceaf04c07558beff267"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"0c7a65a6f1edd49b9a6983069b038d5d5343fafbb105a61c852e9735de814eef"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:01:24.955218Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.219289Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.297984Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.374379Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16871/integrity.json","findings":[],"snapshot_sha256":"f743495df5d1830143eb052ae1597c02323247593dfd1850c5f54583bbfcb571","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Explainable robots require not only successful task execution but also the ability to expose internal decision-making process in a user-friendly manner. However, most imitation learning methods are trained solely on task-level demonstrations, without explicitly modeling subgoal structure or execution progress. This limitation is further exacerbated by the scarcity of subgoal-level supervision in standard robot learning datasets, which restricts the development of robots that can convey the subtasks they are executing during long-horizon manipulation. To address this issue, this paper proposes ","authors_text":"Site Hu, Takato Horii","cross_cats":[],"headline":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-16T08:18:47Z","title":"SADP: Subgoal-Aware Diffusion Policy for Explainable Robots Learned from Foundation Model Generated Demonstrations"},"references":{"count":40,"internal_anchors":2,"resolved_work":40,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Transparent, explainable, and accountable ai for robotics,","work_id":"1bb30ea2-44a7-4188-9ef2-e2d55235a0bb","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A review of robot learning for manipulation: Challenges, representations, and algorithms,","work_id":"1b28e85d-e519-4a08-9713-e5b873bd6430","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A survey of demonstration learning,","work_id":"84d45fbd-7f7b-4f0d-b156-b6095d1ce7eb","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Hierarchical reinforce- ment learning: A survey and open research challenges,","work_id":"de9862f0-13cc-4eb8-9c8c-50b62397917d","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction,","work_id":"5cca4f0a-bb88-4509-b90b-189cc9120011","year":2024}],"snapshot_sha256":"c111967b02a1c8068a19bd1a7087804343cefc1660cb7d9effe5cf11328e8dec"},"source":{"id":"2605.16871","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:47:41.639676Z","id":"e75007f2-eaf7-49a8-a2af-770e528dfba1","model_set":{"reader":"grok-4.3"},"one_line_summary":"SADP trains diffusion policies on foundation-model-generated subgoal-annotated demonstrations and adds a completion predictor to give robots built-in, subgoal-level explainability alongside improved task performance.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Conditioning diffusion policies on foundation-model-generated subgoals improves both task success and explainability for robot manipulation.","strongest_claim":"Experiments in RLBench simulations and real-world evaluations on a UR5e robot demonstrate that SADP achieves higher task success rates than strong task-conditioned diffusion baselines, while providing subgoal-level execution signals for monitoring progress and diagnosing failures.","weakest_assumption":"Foundation models can autonomously generate accurate, unbiased subgoal annotations from raw task demonstrations without introducing systematic errors that would degrade downstream policy learning or explainability."}},"verdict_id":"e75007f2-eaf7-49a8-a2af-770e528dfba1"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2c3c75720a46421624f831f8a59d4b668a8f2b24db68ae186674f428f2ec3583","target":"record","created_at":"2026-05-20T00:03:27Z","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":"0ebcf7089c0ce3380b126e36805d9f5ecebbe9c98230d9234242f7f51b042317","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-16T08:18:47Z","title_canon_sha256":"5508fc7b3e26bdf750d4adb20c32c37df7524bb7a8f5a8c567e712822b81f0ce"},"schema_version":"1.0","source":{"id":"2605.16871","kind":"arxiv","version":1}},"canonical_sha256":"0ff1c23fd4eb7d7371515542ad7248e63d8690dff728952bdc3415ac4d727019","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ff1c23fd4eb7d7371515542ad7248e63d8690dff728952bdc3415ac4d727019","first_computed_at":"2026-05-20T00:03:27.458428Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:27.458428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J/Hw/PPQpN6wKQV85fjsf/4XuTiAV9W7ieJ10TEj92pSkHmpoxiWoDlbOWcZC04T7os+xf+J9oAY/GT3JskwBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:27.459107Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16871","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c3c75720a46421624f831f8a59d4b668a8f2b24db68ae186674f428f2ec3583","sha256:2c2c998b9814487fab5d045dc56fd89b398b8d3019a3c258e6bcef4fe1221f2a"],"state_sha256":"516688a40da2c5e7e1912bf03f764d83cdbc9c18f34ff0fc8f8559eb7fab1955"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DQCUZMx7N/noLElbCQXBLVbwugcJRUglRB53ZEKXIp9k26ZMYlXgC1sWIyBxTaIsHF76ojA6P0vDGYcJMFkvBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T11:39:51.662772Z","bundle_sha256":"ba6a2e84a18035ddbca875b978eb002873210619db08b5717c6e85d4b7d09c9b"}}