{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:I3SUNKSALH4YUYZPDPTIMEQBMV","short_pith_number":"pith:I3SUNKSA","canonical_record":{"source":{"id":"2605.16887","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T09:00:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bc04160b356ad1c8ebf64e91c06c0e24dffffb0de5e3c0418820726c613b7687","abstract_canon_sha256":"d1bea2e7509096c03cad3bdee49dfff5081df854334bc037ee39f6fcbe357a57"},"schema_version":"1.0"},"canonical_sha256":"46e546aa4059f98a632f1be68612016578f818711a6e0d003168d786a976c63c","source":{"kind":"arxiv","id":"2605.16887","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16887","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16887v1","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16887","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_12","alias_value":"I3SUNKSALH4Y","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_16","alias_value":"I3SUNKSALH4YUYZP","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_8","alias_value":"I3SUNKSA","created_at":"2026-05-20T00:03:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:I3SUNKSALH4YUYZPDPTIMEQBMV","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16887","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T09:00:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bc04160b356ad1c8ebf64e91c06c0e24dffffb0de5e3c0418820726c613b7687","abstract_canon_sha256":"d1bea2e7509096c03cad3bdee49dfff5081df854334bc037ee39f6fcbe357a57"},"schema_version":"1.0"},"canonical_sha256":"46e546aa4059f98a632f1be68612016578f818711a6e0d003168d786a976c63c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:28.405920Z","signature_b64":"5Nt4B0hdI3z5fpo21dBqg0rawtlnL7sMjUyay41i3WqHDZn5WMLxbDs/8IesHo8kUhymuFZVZ99nEnrbBNgUCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46e546aa4059f98a632f1be68612016578f818711a6e0d003168d786a976c63c","last_reissued_at":"2026-05-20T00:03:28.405104Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:28.405104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16887","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":"2026-05-20T00:03:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ThrocqSxSOr2GBuXuheh8+75B3mfxFYxbNX5YDWC/F9qWOJGGTEimSII8ew7wWbFByUYZ4v7IdRYx8WkpywvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T16:28:49.376130Z"},"content_sha256":"3cae7f408b844a2ac5b21fd4093433f65713ff77c2a4455c2f97f394bb739ce0","schema_version":"1.0","event_id":"sha256:3cae7f408b844a2ac5b21fd4093433f65713ff77c2a4455c2f97f394bb739ce0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:I3SUNKSALH4YUYZPDPTIMEQBMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mind the Gap: Learning Modality-Agnostic Representations with a Cross-Modality UNet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information.","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Enyi Li, Jinchao Liu, Margarita Osadchy, Xin Niu, Yan Wang, Yongchun Fang","submitted_at":"2026-05-16T09:00:54Z","abstract_excerpt":"Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap include reducing the distributional differences of representations of different modalities, learning indistinguishable representations or explicit modality transfer. The first two approaches suffer from the loss of discriminant information while removing the modality-specific variations. The third one heavily relies on the successful modality transfer, could face catastrophic performance drop when explicit modality transfers are not possible or di"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that cross-modality transformation combined with in-modality reconstruction and adversarial loss can retain discriminant identity information without the losses of prior distributional alignment or transfer methods, and that robustness to occlusions serves as a reliable indicator of successful modality-gap bridging.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"af49fb58e6e7110e6d8a5095015d9c7bb91194875c8b4fdd7a00c7f6b2e6b32c"},"source":{"id":"2605.16887","kind":"arxiv","version":1},"verdict":{"id":"2b1fec1a-42e0-40eb-bc19-3816eb104a70","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:47:03.136954Z","strongest_claim":"We proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space.","one_line_summary":"cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that cross-modality transformation combined with in-modality reconstruction and adversarial loss can retain discriminant identity information without the losses of prior distributional alignment or transfer methods, and that robustness to occlusions serves as a reliable indicator of successful modality-gap bridging.","pith_extraction_headline":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16887/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.574284Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T22:01:02.809005Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.286219Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.364100Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a1d9fe02068590a917dee141e1a44768386a145746cbd3d51cb9762f24e8296e"},"references":{"count":82,"sample":[{"doi":"","year":2011,"title":"Matching forensic sketches to mug shot photos,","work_id":"a4ec2e4c-5d57-4213-8339-e34fd6e994d2","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2015,"title":"Composite sketch recognition via deep network - a transfer learning approach,","work_id":"94b1d091-ee8f-4c3a-b84b-c085bbc6841e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1979,"title":"Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recog- nition,","work_id":"d0a4162c-e2e9-4777-9744-ffdc5a529971","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2003,"title":"Face sketch synthesis and recognition,","work_id":"cbe175eb-4f0c-43bc-b53e-e2b18fe31a2d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2005,"title":"A nonlinear approach for face sketch synthesis and recognition,","work_id":"829bf5b9-d69d-4eb5-9a30-2d0da069d6cc","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":82,"snapshot_sha256":"21a38342a26abeee446f28ba6f649d1e77622d9510dc49a0ed6c1b5a86b41b14","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":"2b1fec1a-42e0-40eb-bc19-3816eb104a70"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nGd1jTcD4B/14yA0XslrD9EUCk3oiZ1ebPeESRIdn9uMlTHp6YkFJdrzWhJZNWqEuuxjP0Wfj37Mk2mmDXKxBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T16:28:49.377422Z"},"content_sha256":"dca066bdf35ecb3f2cceb122e2bb541ce4c763ad3d1f78d703925434b85f65e5","schema_version":"1.0","event_id":"sha256:dca066bdf35ecb3f2cceb122e2bb541ce4c763ad3d1f78d703925434b85f65e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/bundle.json","state_url":"https://pith.science/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/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":"2026-05-22T16:28:49Z","links":{"resolver":"https://pith.science/pith/I3SUNKSALH4YUYZPDPTIMEQBMV","bundle":"https://pith.science/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/bundle.json","state":"https://pith.science/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I3SUNKSALH4YUYZPDPTIMEQBMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:I3SUNKSALH4YUYZPDPTIMEQBMV","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":"d1bea2e7509096c03cad3bdee49dfff5081df854334bc037ee39f6fcbe357a57","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T09:00:54Z","title_canon_sha256":"bc04160b356ad1c8ebf64e91c06c0e24dffffb0de5e3c0418820726c613b7687"},"schema_version":"1.0","source":{"id":"2605.16887","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16887","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16887v1","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16887","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_12","alias_value":"I3SUNKSALH4Y","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_16","alias_value":"I3SUNKSALH4YUYZP","created_at":"2026-05-20T00:03:28Z"},{"alias_kind":"pith_short_8","alias_value":"I3SUNKSA","created_at":"2026-05-20T00:03:28Z"}],"graph_snapshots":[{"event_id":"sha256:dca066bdf35ecb3f2cceb122e2bb541ce4c763ad3d1f78d703925434b85f65e5","target":"graph","created_at":"2026-05-20T00:03:28Z","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 proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that cross-modality transformation combined with in-modality reconstruction and adversarial loss can retain discriminant identity information without the losses of prior distributional alignment or transfer methods, and that robustness to occlusions serves as a reliable indicator of successful modality-gap bridging."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information."}],"snapshot_sha256":"af49fb58e6e7110e6d8a5095015d9c7bb91194875c8b4fdd7a00c7f6b2e6b32c"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.574284Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T22:01:02.809005Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.286219Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.364100Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16887/integrity.json","findings":[],"snapshot_sha256":"a1d9fe02068590a917dee141e1a44768386a145746cbd3d51cb9762f24e8296e","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap include reducing the distributional differences of representations of different modalities, learning indistinguishable representations or explicit modality transfer. The first two approaches suffer from the loss of discriminant information while removing the modality-specific variations. The third one heavily relies on the successful modality transfer, could face catastrophic performance drop when explicit modality transfers are not possible or di","authors_text":"Enyi Li, Jinchao Liu, Margarita Osadchy, Xin Niu, Yan Wang, Yongchun Fang","cross_cats":["cs.LG"],"headline":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T09:00:54Z","title":"Mind the Gap: Learning Modality-Agnostic Representations with a Cross-Modality UNet"},"references":{"count":82,"internal_anchors":0,"resolved_work":82,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Matching forensic sketches to mug shot photos,","work_id":"a4ec2e4c-5d57-4213-8339-e34fd6e994d2","year":2011},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Composite sketch recognition via deep network - a transfer learning approach,","work_id":"94b1d091-ee8f-4c3a-b84b-c085bbc6841e","year":2015},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recog- nition,","work_id":"d0a4162c-e2e9-4777-9744-ffdc5a529971","year":1979},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Face sketch synthesis and recognition,","work_id":"cbe175eb-4f0c-43bc-b53e-e2b18fe31a2d","year":2003},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"A nonlinear approach for face sketch synthesis and recognition,","work_id":"829bf5b9-d69d-4eb5-9a30-2d0da069d6cc","year":2005}],"snapshot_sha256":"21a38342a26abeee446f28ba6f649d1e77622d9510dc49a0ed6c1b5a86b41b14"},"source":{"id":"2605.16887","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T21:47:03.136954Z","id":"2b1fec1a-42e0-40eb-bc19-3816eb104a70","model_set":{"reader":"grok-4.3"},"one_line_summary":"cmUNet learns modality-agnostic representations via cross-modality transformation and in-modality reconstruction with adversarial loss, enabling MarrNet to achieve superior cross-modality matching on spectrum, re-identification, and face recognition tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A compact encoder-decoder network learns modality-agnostic representations while retaining identity-related information.","strongest_claim":"We proposed a compact encoder-decoder neural module (cmUNet) to learn modality-agnostic representations while retaining identity-related information. This is achieved through cross-modality transformation and in-modality reconstruction, enhanced by an adversarial/perceptual loss which encourages indistinguishability of representations in the original sample space.","weakest_assumption":"The assumption that cross-modality transformation combined with in-modality reconstruction and adversarial loss can retain discriminant identity information without the losses of prior distributional alignment or transfer methods, and that robustness to occlusions serves as a reliable indicator of successful modality-gap bridging."}},"verdict_id":"2b1fec1a-42e0-40eb-bc19-3816eb104a70"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3cae7f408b844a2ac5b21fd4093433f65713ff77c2a4455c2f97f394bb739ce0","target":"record","created_at":"2026-05-20T00:03:28Z","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":"d1bea2e7509096c03cad3bdee49dfff5081df854334bc037ee39f6fcbe357a57","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T09:00:54Z","title_canon_sha256":"bc04160b356ad1c8ebf64e91c06c0e24dffffb0de5e3c0418820726c613b7687"},"schema_version":"1.0","source":{"id":"2605.16887","kind":"arxiv","version":1}},"canonical_sha256":"46e546aa4059f98a632f1be68612016578f818711a6e0d003168d786a976c63c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46e546aa4059f98a632f1be68612016578f818711a6e0d003168d786a976c63c","first_computed_at":"2026-05-20T00:03:28.405104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:28.405104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5Nt4B0hdI3z5fpo21dBqg0rawtlnL7sMjUyay41i3WqHDZn5WMLxbDs/8IesHo8kUhymuFZVZ99nEnrbBNgUCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:28.405920Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16887","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cae7f408b844a2ac5b21fd4093433f65713ff77c2a4455c2f97f394bb739ce0","sha256:dca066bdf35ecb3f2cceb122e2bb541ce4c763ad3d1f78d703925434b85f65e5"],"state_sha256":"fd50184e7ba793efde6683d9b6524feba9a5835d7473e9982522fae097968da3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V8kwn0cFtlcE9TFifddKzu+F6bXGmFHTxgfQ9fJFt0Nn6lvEphyrn1JwCe7CXHLAL6N53X74efkRKLciamKsAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T16:28:49.381700Z","bundle_sha256":"71f46abda85b1d01717db1dfdd9529d4b78858076790d36db197233056dc4961"}}