{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","short_pith_number":"pith:I7QQJW4C","canonical_record":{"source":{"id":"2607.22980","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T01:35:16Z","cross_cats_sorted":[],"title_canon_sha256":"84b705c593febda71d7383bd8b42432a9a460fc5c5b6c7101b8d087784979105","abstract_canon_sha256":"e5e76a0a72713fae2a8259ecebd228749500756d0c1db9484f9186bea67c5858"},"schema_version":"1.0"},"canonical_sha256":"47e104db827f2b46c638d4aa09eb7825af9c4328f1ab8272c97786f6731f9bfd","source":{"kind":"arxiv","id":"2607.22980","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22980","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22980v1","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22980","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_12","alias_value":"I7QQJW4CP4VU","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_16","alias_value":"I7QQJW4CP4VUNRRY","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_8","alias_value":"I7QQJW4C","created_at":"2026-07-28T00:22:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","target":"record","payload":{"canonical_record":{"source":{"id":"2607.22980","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T01:35:16Z","cross_cats_sorted":[],"title_canon_sha256":"84b705c593febda71d7383bd8b42432a9a460fc5c5b6c7101b8d087784979105","abstract_canon_sha256":"e5e76a0a72713fae2a8259ecebd228749500756d0c1db9484f9186bea67c5858"},"schema_version":"1.0"},"canonical_sha256":"47e104db827f2b46c638d4aa09eb7825af9c4328f1ab8272c97786f6731f9bfd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:22:05.570884Z","signature_b64":"2LzEkfpzvDK9cp8BiF2ssJq3r1crdMHwTqSGKtWgQI2Zx5QvQgMourpWVjFud+LeNYQQAXLU+80SuQaicXdJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47e104db827f2b46c638d4aa09eb7825af9c4328f1ab8272c97786f6731f9bfd","last_reissued_at":"2026-07-28T00:22:05.569918Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:22:05.569918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.22980","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":"2026-07-28T00:22:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"udyKXZnfOXcMpI9Y6ykMwpUhTDrAdG6E9OQKJDVwYvxqcxPQaw7xzxbFy4H9yXDG2w4qOuKL/J+gl2uHb9ZUBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T14:49:14.224304Z"},"content_sha256":"68b3f6b56f47c0688aa0c373429b0e8f81ce1da877b39869b1d28194fb1a76d0","schema_version":"1.0","event_id":"sha256:68b3f6b56f47c0688aa0c373429b0e8f81ce1da877b39869b1d28194fb1a76d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ethan Davis","submitted_at":"2026-07-25T01:35:16Z","abstract_excerpt":"Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind to whether predicted probabilities are well calibrated - a meaningful gap given nonstationary electroencephalogram (EEG) signals and the risk of overconfident point-estimate classifiers under distribution shift. We conducted a large-scale study contrasting Bayesian complete-pooling models against frequentist baselines for cross-subject, left-hand versus right-hand motor imagery EEG classification across 20 datasets. Six frequentist pipelines wer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22980","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.22980/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T00:22:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Y/jLIqusJAduOgi15vRHVxhbo1Y6ZPCshE6T9/MZ0hPMV2WMGeF3Nvkok9Cs4zjiO4LWaQMpuSmlDeQ8jzFCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T14:49:14.224826Z"},"content_sha256":"7828fc095684995770fd51da0e2ca2b46fd321040bb9d98ec66ea143c8e314f1","schema_version":"1.0","event_id":"sha256:7828fc095684995770fd51da0e2ca2b46fd321040bb9d98ec66ea143c8e314f1"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","target":"integrity","payload":{"note":"Identifier '10.7551/mitpress/3206.001' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Carl Edward Rasmussen and Christopher K. I. Williams.Gaussian Processes for Machine Learning. The MIT Press, 11 2005. ISBN 9780262256834. doi: 10.7551/mitpress/3206.001","arxiv_id":"2607.22980","detector":"doi_compliance","evidence":{"doi":"10.7551/mitpress/3206.001","arxiv_id":null,"ref_index":40,"raw_excerpt":"Carl Edward Rasmussen and Christopher K. I. Williams.Gaussian Processes for Machine Learning. The MIT Press, 11 2005. ISBN 9780262256834. doi: 10.7551/mitpress/3206.001","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":40,"audited_at":"2026-08-01T04:09:36.162485Z","event_type":"pith.integrity.v1","detected_doi":"10.7551/mitpress/3206.001","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"799d9b50dd425e04e77749cc7482fac9c2364b957c1453d3c01d9da45dbdfd0d","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15056,"payload_sha256":"527001d0dd68a705ea7296d450f7e240fbaccd4240d2e2878763eafa98649be6","signature_b64":"nShUK+tvQe0le5C7pCZlGQ7OYGk0mk5Vvr0dFJNa54FI66TBolP/wqLz80V9FIgWPsPNOXu2/QHPmreJlgjRCw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T04:13:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ErZw6Adi3ys8XrTgwiiV2BkZeFem+wcdnATk0CM7rG3a1mUoQCoeAWN1OmAwBKGnw5JiFiv5AvNBJ7vJcLrIDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T14:49:14.228604Z"},"content_sha256":"8c8f06f9f859a8a33ef7365e572759225bbb97fb12ba798049efb2e9d5ad76f6","schema_version":"1.0","event_id":"sha256:8c8f06f9f859a8a33ef7365e572759225bbb97fb12ba798049efb2e9d5ad76f6"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1002/hbm.23730 resolves to 'Deep learning with convolutional neural networks for EEG decoding and visualization'. A reader following the printed text alone cannot reach it.","snippet":"Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball. Deep learning with convolutional neural netw","arxiv_id":"2607.22980","detector":"doi_compliance","evidence":{"ref_index":32,"verdict_class":"incontrovertible","resolved_title":"Deep learning with convolutional neural networks for EEG decoding and visualization","printed_excerpt":"10.1002/hbm","reconstructed_doi":"10.1002/hbm.23730"},"severity":"advisory","ref_index":32,"audited_at":"2026-08-01T04:09:36.162485Z","event_type":"pith.integrity.v1","detected_doi":"10.1002/hbm.23730","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"1a1105e8ee6f9fe24ea561014efffe35426412023074fb06ba6505e1ea67ad6a","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"Deep learning with convolutional neural networks for EEG decoding and visualization","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15055,"payload_sha256":"e1a2fa9cbaf55b627ac4068818380b3474c1a4221a9d8fa4b631a25cab58b9f3","signature_b64":"wo19Zw8WuKlddAR88ZAV8dA/89BwapsyM7nQe3ROfStVv23pzZbdPjYU2sWbjVsLfcq4sqJV5+95OCk4pYC7AQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T04:13:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5EJcEao2YExIrmGqcskwKlGgxlO4ELy5p98/OI8b6WqGLHp1AgH+Z6en7xHp/jK7mqeal5GXmaOkgVEh63GNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T14:49:14.228988Z"},"content_sha256":"6bbb92fc35ba09af879b5603284abc507ed1afc5f7fae548ef161b0a462b7873","schema_version":"1.0","event_id":"sha256:6bbb92fc35ba09af879b5603284abc507ed1afc5f7fae548ef161b0a462b7873"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","target":"integrity","payload":{"note":"Identifier '10.1175/1520-0450(1973)012' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Allan H. Murphy. A new vector partition of the probability score.Journal of Applied Meteo- rology, 12(4):595–600, 1973. doi: 10.1175/1520-0450(1973)012⟨0595:ANVPOT⟩2.0.CO;2","arxiv_id":"2607.22980","detector":"doi_compliance","evidence":{"doi":"10.1175/1520-0450(1973)012","arxiv_id":null,"ref_index":41,"raw_excerpt":"Allan H. Murphy. A new vector partition of the probability score.Journal of Applied Meteo- rology, 12(4):595–600, 1973. doi: 10.1175/1520-0450(1973)012⟨0595:ANVPOT⟩2.0.CO;2","parse_status":"well_formed","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"],"resolution_status":"hard_miss"},"severity":"critical","ref_index":41,"audited_at":"2026-08-01T04:09:36.162485Z","event_type":"pith.integrity.v1","detected_doi":"10.1175/1520-0450(1973)012","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"8b6cdc0ecf6c52e0aa633a9eb7ccaded5fad13c1f153812263b896ddeb2a90a1","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":15054,"payload_sha256":"9425a3e3941a710a4a8cddee330b59d81dfba949c8e3d431994660e88b0daeb3","signature_b64":"sT91tbFQ2eYZO1TXOHSiGC+e9/PpqwaqLLkMYwkXo7nSayhWcDMlbEbG3R5pGx37/0aYux693XZpKMkoQlc/Dw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-01T04:13:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tlUApiTJ8ACLHWsHxv1A43AHqm25VALQlRurLXSA6X7FKvZk/Z0rB8oj923syRomnJ4AqjA62nHR/CnPJQkgBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T14:49:14.229399Z"},"content_sha256":"6c3601011a8119e19e94c3de2e6e31fd9eaa2270b686353167bdacd02c122dcf","schema_version":"1.0","event_id":"sha256:6c3601011a8119e19e94c3de2e6e31fd9eaa2270b686353167bdacd02c122dcf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/bundle.json","state_url":"https://pith.science/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/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":"2026-08-02T14:49:14Z","links":{"resolver":"https://pith.science/pith/I7QQJW4CP4VUNRRY2SVAT23YEW","bundle":"https://pith.science/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/bundle.json","state":"https://pith.science/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I7QQJW4CP4VUNRRY2SVAT23YEW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:I7QQJW4CP4VUNRRY2SVAT23YEW","merge_version":"pith-open-graph-merge-v1","event_count":5,"valid_event_count":5,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e5e76a0a72713fae2a8259ecebd228749500756d0c1db9484f9186bea67c5858","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T01:35:16Z","title_canon_sha256":"84b705c593febda71d7383bd8b42432a9a460fc5c5b6c7101b8d087784979105"},"schema_version":"1.0","source":{"id":"2607.22980","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22980","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22980v1","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22980","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_12","alias_value":"I7QQJW4CP4VU","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_16","alias_value":"I7QQJW4CP4VUNRRY","created_at":"2026-07-28T00:22:05Z"},{"alias_kind":"pith_short_8","alias_value":"I7QQJW4C","created_at":"2026-07-28T00:22:05Z"}],"graph_snapshots":[{"event_id":"sha256:7828fc095684995770fd51da0e2ca2b46fd321040bb9d98ec66ea143c8e314f1","target":"graph","created_at":"2026-07-28T00:22:05Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.22980/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind to whether predicted probabilities are well calibrated - a meaningful gap given nonstationary electroencephalogram (EEG) signals and the risk of overconfident point-estimate classifiers under distribution shift. We conducted a large-scale study contrasting Bayesian complete-pooling models against frequentist baselines for cross-subject, left-hand versus right-hand motor imagery EEG classification across 20 datasets. Six frequentist pipelines wer","authors_text":"Ethan Davis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T01:35:16Z","title":"Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22980","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:68b3f6b56f47c0688aa0c373429b0e8f81ce1da877b39869b1d28194fb1a76d0","target":"record","created_at":"2026-07-28T00:22:05Z","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":"e5e76a0a72713fae2a8259ecebd228749500756d0c1db9484f9186bea67c5858","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-25T01:35:16Z","title_canon_sha256":"84b705c593febda71d7383bd8b42432a9a460fc5c5b6c7101b8d087784979105"},"schema_version":"1.0","source":{"id":"2607.22980","kind":"arxiv","version":1}},"canonical_sha256":"47e104db827f2b46c638d4aa09eb7825af9c4328f1ab8272c97786f6731f9bfd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47e104db827f2b46c638d4aa09eb7825af9c4328f1ab8272c97786f6731f9bfd","first_computed_at":"2026-07-28T00:22:05.569918Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T00:22:05.569918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2LzEkfpzvDK9cp8BiF2ssJq3r1crdMHwTqSGKtWgQI2Zx5QvQgMourpWVjFud+LeNYQQAXLU+80SuQaicXdJBQ==","signature_status":"signed_v1","signed_at":"2026-07-28T00:22:05.570884Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22980","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:6bbb92fc35ba09af879b5603284abc507ed1afc5f7fae548ef161b0a462b7873","sha256:6c3601011a8119e19e94c3de2e6e31fd9eaa2270b686353167bdacd02c122dcf","sha256:8c8f06f9f859a8a33ef7365e572759225bbb97fb12ba798049efb2e9d5ad76f6"]}],"invalid_events":[],"applied_event_ids":["sha256:68b3f6b56f47c0688aa0c373429b0e8f81ce1da877b39869b1d28194fb1a76d0","sha256:7828fc095684995770fd51da0e2ca2b46fd321040bb9d98ec66ea143c8e314f1"],"state_sha256":"d8a59fa975fac959f905f06d43b0e3b425c119d69a117c1edf26ddd3f39a3659"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QDxuEqDRXcnDT5My5dyY7ZV2FEAHCX1Z1yY5+NkAG5c2zS0MkeWXs4I/aQyMwZPxduMGB6kIHvAZbz8BzsuPCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T14:49:14.233542Z","bundle_sha256":"37cad82125c375dc328ad0d94fc700439628162f2256458816380b5f11c7fc31"}}