{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:NQZJRTWSZ2VM4SY6TMLB63JZHB","short_pith_number":"pith:NQZJRTWS","canonical_record":{"source":{"id":"2605.16905","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T09:36:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"90f7693c2b15925555eff1cde3d9680db6f0c9bc1f0bb9169094e4a9f28777f3","abstract_canon_sha256":"ffd5883723a437c8abd061a48a0b81d191c37533a5a979ccf60315c28a06ae4f"},"schema_version":"1.0"},"canonical_sha256":"6c3298ced2ceaace4b1e9b161f6d3938572d3b8f8867fc08f4cde8aa2aa6e1cb","source":{"kind":"arxiv","id":"2605.16905","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16905","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16905v1","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16905","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"NQZJRTWSZ2VM","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"NQZJRTWSZ2VM4SY6","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"NQZJRTWS","created_at":"2026-05-20T00:03:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:NQZJRTWSZ2VM4SY6TMLB63JZHB","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16905","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T09:36:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"90f7693c2b15925555eff1cde3d9680db6f0c9bc1f0bb9169094e4a9f28777f3","abstract_canon_sha256":"ffd5883723a437c8abd061a48a0b81d191c37533a5a979ccf60315c28a06ae4f"},"schema_version":"1.0"},"canonical_sha256":"6c3298ced2ceaace4b1e9b161f6d3938572d3b8f8867fc08f4cde8aa2aa6e1cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:29.424853Z","signature_b64":"4GRhmLchWM+w4a6D/vMmlwbaMoY/nybYrlEJHlQbTCtrmXH8VxQfAxyRbnPdl8cIyYUETLigVsx6ZOscD4Z8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c3298ced2ceaace4b1e9b161f6d3938572d3b8f8867fc08f4cde8aa2aa6e1cb","last_reissued_at":"2026-05-20T00:03:29.423937Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:29.423937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16905","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":"2026-05-20T00:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+DXuRQFX2oBpDu6CPJr5Ft2/tt3DxZ07kqzxkhbATNU+Pt5HgAe1u67JvBMB/eH90YSB12tjPFm3hqgKN14iBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:08:41.067139Z"},"content_sha256":"0f2807fe43dff6bed5c89500abdb55236c31c8f1d1a040c9184d5782e902e1a8","schema_version":"1.0","event_id":"sha256:0f2807fe43dff6bed5c89500abdb55236c31c8f1d1a040c9184d5782e902e1a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:NQZJRTWSZ2VM4SY6TMLB63JZHB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias.","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Chia-Ying Hsieh, Chun-Shu Wei, Hsin-Yuan Fang","submitted_at":"2026-05-16T09:36:58Z","abstract_excerpt":"Post-hoc saliency methods are widely used to interpret deep neural networks, but their faithfulness is difficult to evaluate reliably. Existing evaluations mask features according to saliency-induced feature ordering and measure performance degradation, but this degradation can be confounded by the masking operator: zero masking may create out-of-distribution artifacts, while interpolation-based masking may preserve residual predictive information. We propose Adversarial Information Masking (AIM), a saliency-guided adversarial feature replacement framework for evaluating both saliency-map fait"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on image, audio, and EEG tasks suggest that AIM reduces masking-induced bias compared with zero and interpolation-based masking, while revealing modality-dependent differences between signed and unsigned attributions.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The adversarial counterpart of the input can be generated such that feature replacement removes predictive information without introducing new confounding artifacts or residual signals that affect the faithfulness measurement.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8e398304c0617e6acfa117b36375ffb39ae9eabc6965f480be21a32d909f4449"},"source":{"id":"2605.16905","kind":"arxiv","version":1},"verdict":{"id":"7859b897-c28b-48d3-89c8-e33c7e5bf22b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:20:05.738400Z","strongest_claim":"Experiments on image, audio, and EEG tasks suggest that AIM reduces masking-induced bias compared with zero and interpolation-based masking, while revealing modality-dependent differences between signed and unsigned attributions.","one_line_summary":"AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The adversarial counterpart of the input can be generated such that feature replacement removes predictive information without introducing new confounding artifacts or residual signals that affect the faithfulness measurement.","pith_extraction_headline":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16905/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T20:52:07.546928Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:31:40.108583Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.106381Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.273609Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.352970Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"57e21711284585bd058094597733eabbda66c701fa15a67054d52bf8ac51a12a"},"references":{"count":76,"sample":[{"doi":"","year":2014,"title":"Visualizing and understanding convolutional networks","work_id":"80d9e54b-65a8-44c0-9742-a5474f5e7f8f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Evaluating the visualization of what a deep neural network has learned.IEEE Transactions on Neural Networks and Learning Systems, 28(11):2660–2673, 2016","work_id":"2543e86a-2c10-4595-a293-9e4da0fa05ec","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"A unified approach to interpreting model predictions","work_id":"bcddc314-fe48-4632-bd5c-a5d30175a310","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Towards better understanding of gradient-based attribution methods for deep neural networks.arXiv preprint arXiv:1711.06104","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","ref_index":4,"cited_arxiv_id":"1711.06104","is_internal_anchor":true},{"doi":"","year":2017,"title":"right to explanation","work_id":"7e743ce2-8e35-43a8-89c7-92e4ee23ea5e","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":76,"snapshot_sha256":"4d978ea3cd6294f26d116f46910302600a34a0b5661a5c694ace3d36bf3bbb0f","internal_anchors":5},"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":"7859b897-c28b-48d3-89c8-e33c7e5bf22b"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d4y7qwSfQ7UkI5zwJgWJG2OAfW67p0iG9oVyQ0Di2kfenNeHhgRumuYIfH3hbY7gf2Usit9CFLrtNP5tmFXxBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T04:08:41.067923Z"},"content_sha256":"6af6aec2727f86c488b16f57ae69049dc262268031ecbe0390573a0f459ded61","schema_version":"1.0","event_id":"sha256:6af6aec2727f86c488b16f57ae69049dc262268031ecbe0390573a0f459ded61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/bundle.json","state_url":"https://pith.science/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/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":"2026-05-21T04:08:41Z","links":{"resolver":"https://pith.science/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB","bundle":"https://pith.science/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/bundle.json","state":"https://pith.science/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NQZJRTWSZ2VM4SY6TMLB63JZHB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:NQZJRTWSZ2VM4SY6TMLB63JZHB","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":"ffd5883723a437c8abd061a48a0b81d191c37533a5a979ccf60315c28a06ae4f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T09:36:58Z","title_canon_sha256":"90f7693c2b15925555eff1cde3d9680db6f0c9bc1f0bb9169094e4a9f28777f3"},"schema_version":"1.0","source":{"id":"2605.16905","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16905","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16905v1","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16905","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"NQZJRTWSZ2VM","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"NQZJRTWSZ2VM4SY6","created_at":"2026-05-20T00:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"NQZJRTWS","created_at":"2026-05-20T00:03:29Z"}],"graph_snapshots":[{"event_id":"sha256:6af6aec2727f86c488b16f57ae69049dc262268031ecbe0390573a0f459ded61","target":"graph","created_at":"2026-05-20T00:03:29Z","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 on image, audio, and EEG tasks suggest that AIM reduces masking-induced bias compared with zero and interpolation-based masking, while revealing modality-dependent differences between signed and unsigned attributions."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The adversarial counterpart of the input can be generated such that feature replacement removes predictive information without introducing new confounding artifacts or residual signals that affect the faithfulness measurement."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias."}],"snapshot_sha256":"8e398304c0617e6acfa117b36375ffb39ae9eabc6965f480be21a32d909f4449"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T20:52:07.546928Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:31:40.108583Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.106381Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.273609Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.352970Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16905/integrity.json","findings":[],"snapshot_sha256":"57e21711284585bd058094597733eabbda66c701fa15a67054d52bf8ac51a12a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Post-hoc saliency methods are widely used to interpret deep neural networks, but their faithfulness is difficult to evaluate reliably. Existing evaluations mask features according to saliency-induced feature ordering and measure performance degradation, but this degradation can be confounded by the masking operator: zero masking may create out-of-distribution artifacts, while interpolation-based masking may preserve residual predictive information. We propose Adversarial Information Masking (AIM), a saliency-guided adversarial feature replacement framework for evaluating both saliency-map fait","authors_text":"Chia-Ying Hsieh, Chun-Shu Wei, Hsin-Yuan Fang","cross_cats":["cs.CV"],"headline":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T09:36:58Z","title":"AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps"},"references":{"count":76,"internal_anchors":5,"resolved_work":76,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Visualizing and understanding convolutional networks","work_id":"80d9e54b-65a8-44c0-9742-a5474f5e7f8f","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Evaluating the visualization of what a deep neural network has learned.IEEE Transactions on Neural Networks and Learning Systems, 28(11):2660–2673, 2016","work_id":"2543e86a-2c10-4595-a293-9e4da0fa05ec","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"A unified approach to interpreting model predictions","work_id":"bcddc314-fe48-4632-bd5c-a5d30175a310","year":2017},{"cited_arxiv_id":"1711.06104","doi":"","is_internal_anchor":true,"ref_index":4,"title":"Towards better understanding of gradient-based attribution methods for deep neural networks.arXiv preprint arXiv:1711.06104","work_id":"be0ee150-9b01-455f-8764-dd5b18aee12b","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"right to explanation","work_id":"7e743ce2-8e35-43a8-89c7-92e4ee23ea5e","year":2017}],"snapshot_sha256":"4d978ea3cd6294f26d116f46910302600a34a0b5661a5c694ace3d36bf3bbb0f"},"source":{"id":"2605.16905","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:20:05.738400Z","id":"7859b897-c28b-48d3-89c8-e33c7e5bf22b","model_set":{"reader":"grok-4.3"},"one_line_summary":"AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AIM uses adversarial feature replacement to evaluate saliency map faithfulness with less masking bias.","strongest_claim":"Experiments on image, audio, and EEG tasks suggest that AIM reduces masking-induced bias compared with zero and interpolation-based masking, while revealing modality-dependent differences between signed and unsigned attributions.","weakest_assumption":"The adversarial counterpart of the input can be generated such that feature replacement removes predictive information without introducing new confounding artifacts or residual signals that affect the faithfulness measurement."}},"verdict_id":"7859b897-c28b-48d3-89c8-e33c7e5bf22b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0f2807fe43dff6bed5c89500abdb55236c31c8f1d1a040c9184d5782e902e1a8","target":"record","created_at":"2026-05-20T00:03:29Z","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":"ffd5883723a437c8abd061a48a0b81d191c37533a5a979ccf60315c28a06ae4f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T09:36:58Z","title_canon_sha256":"90f7693c2b15925555eff1cde3d9680db6f0c9bc1f0bb9169094e4a9f28777f3"},"schema_version":"1.0","source":{"id":"2605.16905","kind":"arxiv","version":1}},"canonical_sha256":"6c3298ced2ceaace4b1e9b161f6d3938572d3b8f8867fc08f4cde8aa2aa6e1cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c3298ced2ceaace4b1e9b161f6d3938572d3b8f8867fc08f4cde8aa2aa6e1cb","first_computed_at":"2026-05-20T00:03:29.423937Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:29.423937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4GRhmLchWM+w4a6D/vMmlwbaMoY/nybYrlEJHlQbTCtrmXH8VxQfAxyRbnPdl8cIyYUETLigVsx6ZOscD4Z8BQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:29.424853Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16905","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f2807fe43dff6bed5c89500abdb55236c31c8f1d1a040c9184d5782e902e1a8","sha256:6af6aec2727f86c488b16f57ae69049dc262268031ecbe0390573a0f459ded61"],"state_sha256":"c8a9f4c23a07c374e673a7e4bdb767037db18821b5461468af17e98358b392b6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gD5kdJTdSGQpE6SlIVPst82e81BzjSgoux7Q8QytVaZTnL7KlRwhHZn6fbEFKSD7ifb9ALqP4c/NwNozd3olAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T04:08:41.070799Z","bundle_sha256":"ab29405a01325cec4b270946c22c3b7e2195e933bf50d6e55bc01d3722807440"}}