{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZJO7RUMKN7ODWP47N6UAZMNQGY","short_pith_number":"pith:ZJO7RUMK","canonical_record":{"source":{"id":"2605.16923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T10:30:33Z","cross_cats_sorted":[],"title_canon_sha256":"84332c3940048bf55a4d64a3c40622f092a81e5852b7159860955672a5381151","abstract_canon_sha256":"cfc8dcf6bcbee9256ab03daf3842467dc0e4f4211f9b2ffa1f146454a879d3c1"},"schema_version":"1.0"},"canonical_sha256":"ca5df8d18a6fdc3b3f9f6fa80cb1b03613758551162b7b12babb9392558d1b81","source":{"kind":"arxiv","id":"2605.16923","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16923","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16923v1","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16923","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZJO7RUMKN7OD","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZJO7RUMKN7ODWP47","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZJO7RUMK","created_at":"2026-05-20T00:03:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZJO7RUMKN7ODWP47N6UAZMNQGY","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T10:30:33Z","cross_cats_sorted":[],"title_canon_sha256":"84332c3940048bf55a4d64a3c40622f092a81e5852b7159860955672a5381151","abstract_canon_sha256":"cfc8dcf6bcbee9256ab03daf3842467dc0e4f4211f9b2ffa1f146454a879d3c1"},"schema_version":"1.0"},"canonical_sha256":"ca5df8d18a6fdc3b3f9f6fa80cb1b03613758551162b7b12babb9392558d1b81","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:30.724270Z","signature_b64":"q2EKYEiJP2SHvHmJ5AGWWv4hWLIH6lpD152I84R//hDMn1GsXbD1KjVjk9QQm3sr7IU34YGZcBHedy+hMo5yAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca5df8d18a6fdc3b3f9f6fa80cb1b03613758551162b7b12babb9392558d1b81","last_reissued_at":"2026-05-20T00:03:30.723296Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:30.723296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16923","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":"2026-05-20T00:03:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mo3A49CT8HHGRvwi2e00qMJdhyMmYQCJmEddoT+Ax3hnkcSX+mYMpuy8skHqwNNZVckgv+WWcqYcLWjnrKQQAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T13:41:13.744743Z"},"content_sha256":"1c24fe79ed6af82ac430453f2b7253a0724ab01f7477b08bac605a812f8d13c7","schema_version":"1.0","event_id":"sha256:1c24fe79ed6af82ac430453f2b7253a0724ab01f7477b08bac605a812f8d13c7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZJO7RUMKN7ODWP47N6UAZMNQGY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alan Wee-Chung Liew, Hui Tian, Xiang Gao, Xuefei Yin, Yanming Zhu","submitted_at":"2026-05-16T10:30:33Z","abstract_excerpt":"Decoding visual information from electroencephalography (EEG) signals remains a fundamental challenge in brain-computer interfaces and medical rehabilitation. Existing EEG visual decoding methods mainly focus on learning a single global EEG embedding for cross-modal alignment, but they largely overlook the staged and hierarchical characteristics of human visual processing. To address this limitation, we propose a neuroscience-inspired staged representation learning framework that reformulates EEG visual decoding as a stage-specific representation decomposition problem. The proposed framework o"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed neuroscience-inspired staged representation learning framework organizes EEG representation learning into three complementary phases—low-level visual representation learning, high-level semantic representation learning, and integrative information fusion—while introducing multimodal dual-level semantic learning and semantic latent channels, achieving superior performance under subject-dependent zero-shot evaluation and improved exact retrieval under subject-independent zero-shot evaluation on the THINGS-EEG benchmark.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the staged decomposition into low-level visual, high-level semantic, and integrative phases, combined with separation of coarse label-level and fine image-level semantics, directly captures the hierarchical characteristics of human visual processing in a way that improves cross-modal alignment beyond single global embeddings (invoked in the abstract's motivation and method description).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A staged representation learning method with dual-level semantic disentanglement improves zero-shot EEG visual decoding on the THINGS-EEG benchmark by modeling low-level, high-level, and integrative phases.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a20bbede051649805f6a289271260033b29c9a5800272019f197311a4f20599a"},"source":{"id":"2605.16923","kind":"arxiv","version":1},"verdict":{"id":"53d2d3aa-99c7-4cd8-ba1b-b296e9a054a3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:59:48.132838Z","strongest_claim":"The proposed neuroscience-inspired staged representation learning framework organizes EEG representation learning into three complementary phases—low-level visual representation learning, high-level semantic representation learning, and integrative information fusion—while introducing multimodal dual-level semantic learning and semantic latent channels, achieving superior performance under subject-dependent zero-shot evaluation and improved exact retrieval under subject-independent zero-shot evaluation on the THINGS-EEG benchmark.","one_line_summary":"A staged representation learning method with dual-level semantic disentanglement improves zero-shot EEG visual decoding on the THINGS-EEG benchmark by modeling low-level, high-level, and integrative phases.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the staged decomposition into low-level visual, high-level semantic, and integrative phases, combined with separation of coarse label-level and fine image-level semantics, directly captures the hierarchical characteristics of human visual processing in a way that improves cross-modal alignment beyond single global embeddings (invoked in the abstract's motivation and method description).","pith_extraction_headline":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16923/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.130553Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:10:56.119329Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T20:22:26.955087Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.260276Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.340954Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"080edee39f7eb476c0b0cf36437f4dbd495c85eec244a336fa0a68317ee993e5"},"references":{"count":32,"sample":[{"doi":"","year":2024,"title":"Visual neural decoding via improved visual-eeg semantic consistency","work_id":"dfd9a681-9556-432b-b38e-639cbfcd879c","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Visually evoked potentials","work_id":"0c44d3cd-f63b-43fe-b6e7-99a18d387ca6","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"DeLaTorre-Ortiz,C.,Ruotsalo,T.,2024.Perceptualvisualsimilarity fromeeg:Predictionandimagegeneration,in:ACMMM,pp.11146– 11155","work_id":"7946cf54-fd9a-4c4b-af11-8fce27cb92b3","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Eeg-based brain-computer interface enables real-time robotic hand control at individual finger level","work_id":"a0b43fa3-67a5-4afe-bd38-5d6fbad6513f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Decoding visual neural repre- sentations by multimodal learning of brain-visual-linguistic features","work_id":"5d2de542-7d0f-4437-bcd2-b195dab57e13","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":32,"snapshot_sha256":"10de226ef0a3a7848579f57070bd8723c12c60dc05f21b45ba772174ae22a421","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2a02932175a2b32ef68bad8b6de16f4e871b550abc7d331b3bfefd1efb49759e"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"53d2d3aa-99c7-4cd8-ba1b-b296e9a054a3"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zoLVj41lt7GFqp/OhoM3agTjie2DuDhl2zKpJ/W/qd2lyeOEZ2HAnPAIXQ0EDyJ40+dYvcFSNuFICs/fPkE9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T13:41:13.746090Z"},"content_sha256":"05432e488b029efdeb6a299a53429f952e2412044b88780e3de9fdf641ede69b","schema_version":"1.0","event_id":"sha256:05432e488b029efdeb6a299a53429f952e2412044b88780e3de9fdf641ede69b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/bundle.json","state_url":"https://pith.science/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/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":"2026-05-28T13:41:13Z","links":{"resolver":"https://pith.science/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY","bundle":"https://pith.science/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/bundle.json","state":"https://pith.science/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZJO7RUMKN7ODWP47N6UAZMNQGY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZJO7RUMKN7ODWP47N6UAZMNQGY","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":"cfc8dcf6bcbee9256ab03daf3842467dc0e4f4211f9b2ffa1f146454a879d3c1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T10:30:33Z","title_canon_sha256":"84332c3940048bf55a4d64a3c40622f092a81e5852b7159860955672a5381151"},"schema_version":"1.0","source":{"id":"2605.16923","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16923","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16923v1","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16923","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_12","alias_value":"ZJO7RUMKN7OD","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_16","alias_value":"ZJO7RUMKN7ODWP47","created_at":"2026-05-20T00:03:30Z"},{"alias_kind":"pith_short_8","alias_value":"ZJO7RUMK","created_at":"2026-05-20T00:03:30Z"}],"graph_snapshots":[{"event_id":"sha256:05432e488b029efdeb6a299a53429f952e2412044b88780e3de9fdf641ede69b","target":"graph","created_at":"2026-05-20T00:03:30Z","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":"The proposed neuroscience-inspired staged representation learning framework organizes EEG representation learning into three complementary phases—low-level visual representation learning, high-level semantic representation learning, and integrative information fusion—while introducing multimodal dual-level semantic learning and semantic latent channels, achieving superior performance under subject-dependent zero-shot evaluation and improved exact retrieval under subject-independent zero-shot evaluation on the THINGS-EEG benchmark."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the staged decomposition into low-level visual, high-level semantic, and integrative phases, combined with separation of coarse label-level and fine image-level semantics, directly captures the hierarchical characteristics of human visual processing in a way that improves cross-modal alignment beyond single global embeddings (invoked in the abstract's motivation and method description)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A staged representation learning method with dual-level semantic disentanglement improves zero-shot EEG visual decoding on the THINGS-EEG benchmark by modeling low-level, high-level, and integrative phases."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity."}],"snapshot_sha256":"a20bbede051649805f6a289271260033b29c9a5800272019f197311a4f20599a"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2a02932175a2b32ef68bad8b6de16f4e871b550abc7d331b3bfefd1efb49759e"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.130553Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:10:56.119329Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T20:22:26.955087Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.260276Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.340954Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16923/integrity.json","findings":[],"snapshot_sha256":"080edee39f7eb476c0b0cf36437f4dbd495c85eec244a336fa0a68317ee993e5","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decoding visual information from electroencephalography (EEG) signals remains a fundamental challenge in brain-computer interfaces and medical rehabilitation. Existing EEG visual decoding methods mainly focus on learning a single global EEG embedding for cross-modal alignment, but they largely overlook the staged and hierarchical characteristics of human visual processing. To address this limitation, we propose a neuroscience-inspired staged representation learning framework that reformulates EEG visual decoding as a stage-specific representation decomposition problem. The proposed framework o","authors_text":"Alan Wee-Chung Liew, Hui Tian, Xiang Gao, Xuefei Yin, Yanming Zhu","cross_cats":[],"headline":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T10:30:33Z","title":"Neuroscience-inspired Staged Representation Learning with Disentangled Coarse- and Fine-Grained Semantics for EEG Visual Decoding"},"references":{"count":32,"internal_anchors":0,"resolved_work":32,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Visual neural decoding via improved visual-eeg semantic consistency","work_id":"dfd9a681-9556-432b-b38e-639cbfcd879c","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Visually evoked potentials","work_id":"0c44d3cd-f63b-43fe-b6e7-99a18d387ca6","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"DeLaTorre-Ortiz,C.,Ruotsalo,T.,2024.Perceptualvisualsimilarity fromeeg:Predictionandimagegeneration,in:ACMMM,pp.11146– 11155","work_id":"7946cf54-fd9a-4c4b-af11-8fce27cb92b3","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Eeg-based brain-computer interface enables real-time robotic hand control at individual finger level","work_id":"a0b43fa3-67a5-4afe-bd38-5d6fbad6513f","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Decoding visual neural repre- sentations by multimodal learning of brain-visual-linguistic features","work_id":"5d2de542-7d0f-4437-bcd2-b195dab57e13","year":2023}],"snapshot_sha256":"10de226ef0a3a7848579f57070bd8723c12c60dc05f21b45ba772174ae22a421"},"source":{"id":"2605.16923","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:59:48.132838Z","id":"53d2d3aa-99c7-4cd8-ba1b-b296e9a054a3","model_set":{"reader":"grok-4.3"},"one_line_summary":"A staged representation learning method with dual-level semantic disentanglement improves zero-shot EEG visual decoding on the THINGS-EEG benchmark by modeling low-level, high-level, and integrative phases.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Staged decomposition of EEG signals into low-level visual and high-level semantic phases improves zero-shot visual decoding from brain activity.","strongest_claim":"The proposed neuroscience-inspired staged representation learning framework organizes EEG representation learning into three complementary phases—low-level visual representation learning, high-level semantic representation learning, and integrative information fusion—while introducing multimodal dual-level semantic learning and semantic latent channels, achieving superior performance under subject-dependent zero-shot evaluation and improved exact retrieval under subject-independent zero-shot evaluation on the THINGS-EEG benchmark.","weakest_assumption":"That the staged decomposition into low-level visual, high-level semantic, and integrative phases, combined with separation of coarse label-level and fine image-level semantics, directly captures the hierarchical characteristics of human visual processing in a way that improves cross-modal alignment beyond single global embeddings (invoked in the abstract's motivation and method description)."}},"verdict_id":"53d2d3aa-99c7-4cd8-ba1b-b296e9a054a3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c24fe79ed6af82ac430453f2b7253a0724ab01f7477b08bac605a812f8d13c7","target":"record","created_at":"2026-05-20T00:03:30Z","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":"cfc8dcf6bcbee9256ab03daf3842467dc0e4f4211f9b2ffa1f146454a879d3c1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-16T10:30:33Z","title_canon_sha256":"84332c3940048bf55a4d64a3c40622f092a81e5852b7159860955672a5381151"},"schema_version":"1.0","source":{"id":"2605.16923","kind":"arxiv","version":1}},"canonical_sha256":"ca5df8d18a6fdc3b3f9f6fa80cb1b03613758551162b7b12babb9392558d1b81","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca5df8d18a6fdc3b3f9f6fa80cb1b03613758551162b7b12babb9392558d1b81","first_computed_at":"2026-05-20T00:03:30.723296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:30.723296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q2EKYEiJP2SHvHmJ5AGWWv4hWLIH6lpD152I84R//hDMn1GsXbD1KjVjk9QQm3sr7IU34YGZcBHedy+hMo5yAQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:30.724270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16923","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c24fe79ed6af82ac430453f2b7253a0724ab01f7477b08bac605a812f8d13c7","sha256:05432e488b029efdeb6a299a53429f952e2412044b88780e3de9fdf641ede69b"],"state_sha256":"633e549be1b7fdfd320e3c9094c80c479925a566dfa3df6e26b4399c2de6d42f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5DVhssQzduSQqjwtB+yCSgtewY2qqI+pXKIuy01yOHhBWnC9r8ZTB8G6sdfUMGEOFPprkqp+4s3a/jEgwgzPAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T13:41:13.751158Z","bundle_sha256":"2a4fa628623308bde1edee0dbd9442e549010d5602c280a34e156590bdb55936"}}