{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OVAJQYWJFX36O56ZZIRWAIGTBS","short_pith_number":"pith:OVAJQYWJ","canonical_record":{"source":{"id":"2605.15533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T02:09:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4527fce4094b1bce1e90ba27bdae54de8aacefe3aa83f8be8f81e67d504db627","abstract_canon_sha256":"9810eb3b8d31fe3635c1d839ea309f54499ed86cef865472ac69155ef138c3a6"},"schema_version":"1.0"},"canonical_sha256":"75409862c92df7e777d9ca236020d30cbccdc797da08fbea94bb6b7b7eacfa50","source":{"kind":"arxiv","id":"2605.15533","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15533","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15533v1","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15533","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_12","alias_value":"OVAJQYWJFX36","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_16","alias_value":"OVAJQYWJFX36O56Z","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_8","alias_value":"OVAJQYWJ","created_at":"2026-05-20T00:01:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OVAJQYWJFX36O56ZZIRWAIGTBS","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T02:09:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4527fce4094b1bce1e90ba27bdae54de8aacefe3aa83f8be8f81e67d504db627","abstract_canon_sha256":"9810eb3b8d31fe3635c1d839ea309f54499ed86cef865472ac69155ef138c3a6"},"schema_version":"1.0"},"canonical_sha256":"75409862c92df7e777d9ca236020d30cbccdc797da08fbea94bb6b7b7eacfa50","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:03.828454Z","signature_b64":"YfzYmwgHdWYbWFAICYxrHn2BhrKXYYh+wNYwTkKE5K2E/NCYGuQDwM/AHCoElomOTmShoL4XYU4V0MoCntRiDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75409862c92df7e777d9ca236020d30cbccdc797da08fbea94bb6b7b7eacfa50","last_reissued_at":"2026-05-20T00:01:03.827501Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:03.827501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15533","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":"2026-05-20T00:01:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tRhni+jF2ssbxOBjHAhyMPe55MriuXPVj9XHAKg4nR2fPUks0jDwPA/Z7cOc2wd18K8tWYf1wFXr581ePO/6Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T02:45:50.313651Z"},"content_sha256":"5b6f904654f3860de495456342a6a7a9f32da8b9b46f846d953cc091334a310b","schema_version":"1.0","event_id":"sha256:5b6f904654f3860de495456342a6a7a9f32da8b9b46f846d953cc091334a310b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OVAJQYWJFX36O56ZZIRWAIGTBS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts.","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Junlan Feng, Liang Li, Qian Wang, Song Wu, Xinyu Chen, Zili Yi","submitted_at":"2026-05-15T02:09:06Z","abstract_excerpt":"Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model training, they often underutilize the rich information embedded within noisy latent, leading to unsatisfactory results. To address this, we propose a \\textit{tuning-free, instruction-based} video editing framework. We approach video editing from the perspective of noisy latent: we design a Structural Noise Initialization Strategy (SNIS) to secure a superior editing starting point by assigning higher noise levels to edited regions (to facilitate content "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We propose a tuning-free, instruction-based video editing framework... Experiments show that our proposed method achieves better visual quality and state-of-the-art performance.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That assigning higher noise levels to edited regions and lower noise levels to unedited regions, combined with the proposed Noise Guidance Mechanism, will reliably preserve unedited content and overall coherence using only the generative model's video prior.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Proposes SNIS and NGM to enable tuning-free instruction-based video editing with improved visual quality and claimed SOTA results.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c2e9e24942f277fbeecca1f823a07fd12cafa3f70badefbd9d263da81712b697"},"source":{"id":"2605.15533","kind":"arxiv","version":1},"verdict":{"id":"57e78cf6-b7b7-420f-9b28-16208b1b6087","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T14:26:07.529392Z","strongest_claim":"We propose a tuning-free, instruction-based video editing framework... Experiments show that our proposed method achieves better visual quality and state-of-the-art performance.","one_line_summary":"Proposes SNIS and NGM to enable tuning-free instruction-based video editing with improved visual quality and claimed SOTA results.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That assigning higher noise levels to edited regions and lower noise levels to unedited regions, combined with the proposed Noise Guidance Mechanism, will reliably preserve unedited content and overall coherence using only the generative model's video prior.","pith_extraction_headline":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15533/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T14:37:37.929898Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T14:31:17.452863Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T14:22:01.903765Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.034905Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.833523Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.370841Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.618853Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"d9b79db1537768f067b1e3dc6e8dceee8c62d98fd88fac4705073bf2303ac26a"},"references":{"count":35,"sample":[{"doi":"","year":2026,"title":"Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance","work_id":"f2edce87-70a5-4c4f-965d-5e551b608de2","ref_index":1,"cited_arxiv_id":"2605.15533","is_internal_anchor":true},{"doi":"","year":null,"title":"RELATED WORKS Relevant works in image editing focus on converting image generation models into editing models through prompt guid- anceandattentionmanipulation[1,2,3]. Owingtothedelayed development of","work_id":"08afeea1-7807-4dd5-8c51-4d5f635e8daf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Replace the bear with a tiger","work_id":"d7e63a1d-77ed-41ed-8b94-6c7574de1810","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Replace the elephant with a zebra","work_id":"f1376c81-c02a-4395-a0c6-904124a6daa2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1901,"title":"Delete the rhino","work_id":"1035849c-75ab-4567-941e-b01746426095","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":35,"snapshot_sha256":"9906216fd5598a402d5b64cf0e2e5303247faf8282e05b53a69a751947728e64","internal_anchors":8},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a2234ea6824f60a6180f11e96e2aa734c97eb318e7783eed07ca89cf5813bc87"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"57e78cf6-b7b7-420f-9b28-16208b1b6087"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aIXwMdv8sWRvDsh/HBHS/pICEXy17l01WnJJh/OddwhSlnvxbr7fmywHTxN+a8PDJsxUix0Pcj3YC5+GiuK3DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T02:45:50.314955Z"},"content_sha256":"01a4f79452d9aaaaaaf0d11f7da6fdd490b99b2e12277f6ebddf41d3484f2f78","schema_version":"1.0","event_id":"sha256:01a4f79452d9aaaaaaf0d11f7da6fdd490b99b2e12277f6ebddf41d3484f2f78"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/bundle.json","state_url":"https://pith.science/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/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":"2026-05-25T02:45:50Z","links":{"resolver":"https://pith.science/pith/OVAJQYWJFX36O56ZZIRWAIGTBS","bundle":"https://pith.science/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/bundle.json","state":"https://pith.science/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OVAJQYWJFX36O56ZZIRWAIGTBS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OVAJQYWJFX36O56ZZIRWAIGTBS","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":"9810eb3b8d31fe3635c1d839ea309f54499ed86cef865472ac69155ef138c3a6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T02:09:06Z","title_canon_sha256":"4527fce4094b1bce1e90ba27bdae54de8aacefe3aa83f8be8f81e67d504db627"},"schema_version":"1.0","source":{"id":"2605.15533","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15533","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15533v1","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15533","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_12","alias_value":"OVAJQYWJFX36","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_16","alias_value":"OVAJQYWJFX36O56Z","created_at":"2026-05-20T00:01:03Z"},{"alias_kind":"pith_short_8","alias_value":"OVAJQYWJ","created_at":"2026-05-20T00:01:03Z"}],"graph_snapshots":[{"event_id":"sha256:01a4f79452d9aaaaaaf0d11f7da6fdd490b99b2e12277f6ebddf41d3484f2f78","target":"graph","created_at":"2026-05-20T00:01:03Z","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":"We propose a tuning-free, instruction-based video editing framework... Experiments show that our proposed method achieves better visual quality and state-of-the-art performance."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That assigning higher noise levels to edited regions and lower noise levels to unedited regions, combined with the proposed Noise Guidance Mechanism, will reliably preserve unedited content and overall coherence using only the generative model's video prior."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Proposes SNIS and NGM to enable tuning-free instruction-based video editing with improved visual quality and claimed SOTA results."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts."}],"snapshot_sha256":"c2e9e24942f277fbeecca1f823a07fd12cafa3f70badefbd9d263da81712b697"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a2234ea6824f60a6180f11e96e2aa734c97eb318e7783eed07ca89cf5813bc87"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T14:37:37.929898Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T14:31:17.452863Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T14:22:01.903765Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.034905Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.833523Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.370841Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.618853Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15533/integrity.json","findings":[],"snapshot_sha256":"d9b79db1537768f067b1e3dc6e8dceee8c62d98fd88fac4705073bf2303ac26a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model training, they often underutilize the rich information embedded within noisy latent, leading to unsatisfactory results. To address this, we propose a \\textit{tuning-free, instruction-based} video editing framework. We approach video editing from the perspective of noisy latent: we design a Structural Noise Initialization Strategy (SNIS) to secure a superior editing starting point by assigning higher noise levels to edited regions (to facilitate content ","authors_text":"Junlan Feng, Liang Li, Qian Wang, Song Wu, Xinyu Chen, Zili Yi","cross_cats":["cs.AI"],"headline":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T02:09:06Z","title":"Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance"},"references":{"count":35,"internal_anchors":8,"resolved_work":35,"sample":[{"cited_arxiv_id":"2605.15533","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance","work_id":"f2edce87-70a5-4c4f-965d-5e551b608de2","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"RELATED WORKS Relevant works in image editing focus on converting image generation models into editing models through prompt guid- anceandattentionmanipulation[1,2,3]. Owingtothedelayed development of","work_id":"08afeea1-7807-4dd5-8c51-4d5f635e8daf","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Replace the bear with a tiger","work_id":"d7e63a1d-77ed-41ed-8b94-6c7574de1810","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Replace the elephant with a zebra","work_id":"f1376c81-c02a-4395-a0c6-904124a6daa2","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Delete the rhino","work_id":"1035849c-75ab-4567-941e-b01746426095","year":1901}],"snapshot_sha256":"9906216fd5598a402d5b64cf0e2e5303247faf8282e05b53a69a751947728e64"},"source":{"id":"2605.15533","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T14:26:07.529392Z","id":"57e78cf6-b7b7-420f-9b28-16208b1b6087","model_set":{"reader":"grok-4.3"},"one_line_summary":"Proposes SNIS and NGM to enable tuning-free instruction-based video editing with improved visual quality and claimed SOTA results.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A tuning-free video editing method uses selective noise levels and guidance to change only the intended parts.","strongest_claim":"We propose a tuning-free, instruction-based video editing framework... Experiments show that our proposed method achieves better visual quality and state-of-the-art performance.","weakest_assumption":"That assigning higher noise levels to edited regions and lower noise levels to unedited regions, combined with the proposed Noise Guidance Mechanism, will reliably preserve unedited content and overall coherence using only the generative model's video prior."}},"verdict_id":"57e78cf6-b7b7-420f-9b28-16208b1b6087"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5b6f904654f3860de495456342a6a7a9f32da8b9b46f846d953cc091334a310b","target":"record","created_at":"2026-05-20T00:01:03Z","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":"9810eb3b8d31fe3635c1d839ea309f54499ed86cef865472ac69155ef138c3a6","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-15T02:09:06Z","title_canon_sha256":"4527fce4094b1bce1e90ba27bdae54de8aacefe3aa83f8be8f81e67d504db627"},"schema_version":"1.0","source":{"id":"2605.15533","kind":"arxiv","version":1}},"canonical_sha256":"75409862c92df7e777d9ca236020d30cbccdc797da08fbea94bb6b7b7eacfa50","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75409862c92df7e777d9ca236020d30cbccdc797da08fbea94bb6b7b7eacfa50","first_computed_at":"2026-05-20T00:01:03.827501Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:03.827501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YfzYmwgHdWYbWFAICYxrHn2BhrKXYYh+wNYwTkKE5K2E/NCYGuQDwM/AHCoElomOTmShoL4XYU4V0MoCntRiDA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:03.828454Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15533","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b6f904654f3860de495456342a6a7a9f32da8b9b46f846d953cc091334a310b","sha256:01a4f79452d9aaaaaaf0d11f7da6fdd490b99b2e12277f6ebddf41d3484f2f78"],"state_sha256":"8feb80c4799eb21e22d87dd7e697f3b4d9bf767d26f592eb253ce27b0b896cfa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DAMVXb+kULmrcRFQV4L2WoTZLHujlccxbGKomhsOYXpgyD5o/9L89I9mMb22D/rZvOg3difXNFPCkM0BWMFeBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T02:45:50.321030Z","bundle_sha256":"0323a7d50c2e2da9238b84ae2f548b4e8b16bbdcd9f87cce3aa3292bf75d175e"}}