{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","short_pith_number":"pith:YMAAKVYJ","canonical_record":{"source":{"id":"2608.01032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-02T06:27:55Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"11046688f0de064eac1f33148da4df69edff94a76a4e3098d3795b0e8ac997f6","abstract_canon_sha256":"cfc2da572de6f5729ca88a3aec0a8eede4b7dba6dcb345901d4ecbb77ffb8488"},"schema_version":"1.0"},"canonical_sha256":"c300055709121551dc525fe32e63d856432e02d233029472ab544696fff54747","source":{"kind":"arxiv","id":"2608.01032","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.01032","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"arxiv_version","alias_value":"2608.01032v1","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.01032","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_12","alias_value":"YMAAKVYJCIKV","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_16","alias_value":"YMAAKVYJCIKVDXCS","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_8","alias_value":"YMAAKVYJ","created_at":"2026-08-04T01:56:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","target":"record","payload":{"canonical_record":{"source":{"id":"2608.01032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-02T06:27:55Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"11046688f0de064eac1f33148da4df69edff94a76a4e3098d3795b0e8ac997f6","abstract_canon_sha256":"cfc2da572de6f5729ca88a3aec0a8eede4b7dba6dcb345901d4ecbb77ffb8488"},"schema_version":"1.0"},"canonical_sha256":"c300055709121551dc525fe32e63d856432e02d233029472ab544696fff54747","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:56:15.278917Z","signature_b64":"77n9iQtw3r1E1gToqpXH725svKPu0q95XZyt3pP1GzhDHN1840Z7B0ALTBk3SW2/RfLO6k7T8z3W7UVtdrheBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c300055709121551dc525fe32e63d856432e02d233029472ab544696fff54747","last_reissued_at":"2026-08-04T01:56:15.277299Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:56:15.277299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.01032","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":"2026-08-04T01:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hl1Myr4ZYBZLuYVyw8zV+tOEVGPhM9i5XfwYRe1m/f9/CmEvBHO3996/j/wtCV0Ps6D/kbGCkNYb/GCmlMjhAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:41:58.515559Z"},"content_sha256":"4e9eb0c68aa179c2c773f0ac1ce7f146a0bb503efc711b17f22f304fd29c84fe","schema_version":"1.0","event_id":"sha256:4e9eb0c68aa179c2c773f0ac1ce7f146a0bb503efc711b17f22f304fd29c84fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Fourth Quadrant: A Stylized View of Benign Misfitting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Anant Sahai, Gireeja Ranade","submitted_at":"2026-08-02T06:27:55Z","abstract_excerpt":"Training error is what we can observe on a training set; test error is the quantity we actually care about. We study linear regression with squared-error in a deterministic $(d+1)$-dimensional single-spike model. Each stylized training vector has the same informative spike coordinate, of amplitude $\\sqrt{\\gamma}$ with $\\gamma>1$. The remaining directions are nuisance, and the nuisance components of distinct training vectors all have equal norm and are mutually orthogonal. The training labels are all $1$. Fresh test points are drawn from $\\vec{x}_{\\rm test} \\sim \\mathcal{N}(\\vec{0},\\operatornam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.01032","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/2608.01032/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":"2026-08-04T01:56:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7T8Pfka9my3nqZ23phYVHKjqqA5X4pKJgW58Mek2984AzEjVQeN7bjjPLGNyYGjGEtkWO69/Y2/Gl5EZWMhECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:41:58.516213Z"},"content_sha256":"c6d56d59a1bedac0c37b58e53d20fd7e30f85d5795a42afa6cbac52020592201","schema_version":"1.0","event_id":"sha256:c6d56d59a1bedac0c37b58e53d20fd7e30f85d5795a42afa6cbac52020592201"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","target":"integrity","payload":{"note":"Identifier '10.1088/1751-8121/ab45e3' 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":"Stefano Spigler, Mario Geiger, St´ ephane d’Ascoli, Levent Sagun, Giulio Biroli, and Matthieu Wyart. A jamming transition from under- to over-parametrization affects generalization in deep learning.Journal of Physics A: Mathematical and The","arxiv_id":"2608.01032","detector":"doi_compliance","evidence":{"doi":"10.1088/1751-8121/ab45e3","arxiv_id":null,"ref_index":187,"raw_excerpt":"Stefano Spigler, Mario Geiger, St´ ephane d’Ascoli, Levent Sagun, Giulio Biroli, and Matthieu Wyart. A jamming transition from under- to over-parametrization affects generalization in deep learning.Journal of Physics A: Mathematical and Theoretical, 52(47):474001, 2019. doi: 10.1088/1751-8121/ab45e3","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":187,"audited_at":"2026-08-06T00:48:42.925191Z","event_type":"pith.integrity.v1","detected_doi":"10.1088/1751-8121/ab45e3","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"f4731518db5f9234559023784faac3dc0357afb3afe938519d94961a91580a93","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18212,"payload_sha256":"2e4bbc6074f35d61b63b967e861b6ae5c01953e917cd18c0dcb2a05bdee90980","signature_b64":"+P6WvCavGsHoY54AemG6roR3uRtGxruom9kHDHHQDX7KKEag/WdwPiFhBCTI38s8VTA0hjQoj2SEUxV+eQaOCA==","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":"2026-08-06T00:53:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oL9Sr8D1C9HhxaHn5DJ2P8CoGNBq3tobnEixPly+1chQbgv7BJUurOyQZVj9vSuEUJuDg3E50lsaBQvJw+oTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:41:58.521196Z"},"content_sha256":"90e08603391e8406ff1c17d1f3199e8f4e947aac82ae9ae00195c6e116aa898c","schema_version":"1.0","event_id":"sha256:90e08603391e8406ff1c17d1f3199e8f4e947aac82ae9ae00195c6e116aa898c"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.4230/LIPIcs.SoCG.2017.45) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Assimakis Kattis and Aleksandar Nikolov. Lower bounds for differential privacy from gaussian width. In33rd International Symposium on Computational Geometry, volume 77 ofLeibniz International Proceedings in Informatics, pages 45:1–45:16, 20","arxiv_id":"2608.01032","detector":"doi_compliance","evidence":{"ref_index":107,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.4230/lipics.socg","reconstructed_doi":"10.4230/LIPIcs.SoCG.2017.45"},"severity":"advisory","ref_index":107,"audited_at":"2026-08-06T00:48:42.925191Z","event_type":"pith.integrity.v1","detected_doi":"10.4230/LIPIcs.SoCG.2017.45","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"4718af8960dbf9fb101b547e2cb9e5bf88b845c5c98fc1df5c63d6109fefe376","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18211,"payload_sha256":"62343aecac5c56eda3f5a72bf432e557938cc1e2f7391745ddf0cb5418aaeea0","signature_b64":"QwrYrzNyc3xBVMuWCpolTHYX6afWuDz3LDMUR0NSr5WcPwkUSgF1jE2cyJZmb0fQH+2AiLRqsY009djkiHPqDQ==","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":"2026-08-06T00:53:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mZxCD7/Ehp5jqlZbRn80kWh8QL/KQUDMqosKiQHDoIznc7DyaeyjrkALpihSMMkW2qLuJ2wd0Trzl8GACB1eDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:41:58.521703Z"},"content_sha256":"38d2bfcf1c12d7b7511df0ad9612795b2096f887f648d915e3d4921c85ac0688","schema_version":"1.0","event_id":"sha256:38d2bfcf1c12d7b7511df0ad9612795b2096f887f648d915e3d4921c85ac0688"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/TIT.2015.2484066) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Shao-Lun Huang, Changho Suh, and Lizhong Zheng. Euclidean information theory of networks. IEEE Transactions on Information Theory, 61(12):6795–6814, 2015. doi: 10.1109/TIT.2015. 2484066","arxiv_id":"2608.01032","detector":"doi_compliance","evidence":{"ref_index":92,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/tit.2015","reconstructed_doi":"10.1109/TIT.2015.2484066"},"severity":"advisory","ref_index":92,"audited_at":"2026-08-06T00:48:42.925191Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/TIT.2015.2484066","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"88218b04b1a0623c9238a17db50df1219345a7434c37b4f89a951e1143c73ace","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18210,"payload_sha256":"41513089f8b33f79daec12650f74439b26f8fcec7f7d6cc7f518088ab9b03c42","signature_b64":"EJdBfAgHSlx3pn0fJv4rM19YsUunSWW5rITvEx4smmlSOrbV0LqmWUAknMb/wx12TWACFhA/WxREeb28DQGEDQ==","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":"2026-08-06T00:53:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bk07jvZmOkGItgGvxw9mTry/+oDIlg3PbbDRiXRtt23sKSknpHZD4eTmexSIdbh0q2YYgsHoVdgjrH2tpsxcCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:41:58.522186Z"},"content_sha256":"74cd5f8bb530cc7282dcdd0c0a6ebb9df4a54bb764146d2a50729467178053dd","schema_version":"1.0","event_id":"sha256:74cd5f8bb530cc7282dcdd0c0a6ebb9df4a54bb764146d2a50729467178053dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/bundle.json","state_url":"https://pith.science/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/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":"2026-08-06T05:41:58Z","links":{"resolver":"https://pith.science/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ","bundle":"https://pith.science/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/bundle.json","state":"https://pith.science/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YMAAKVYJCIKVDXCSL7RS4Y6YKZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YMAAKVYJCIKVDXCSL7RS4Y6YKZ","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":"cfc2da572de6f5729ca88a3aec0a8eede4b7dba6dcb345901d4ecbb77ffb8488","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-02T06:27:55Z","title_canon_sha256":"11046688f0de064eac1f33148da4df69edff94a76a4e3098d3795b0e8ac997f6"},"schema_version":"1.0","source":{"id":"2608.01032","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.01032","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"arxiv_version","alias_value":"2608.01032v1","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.01032","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_12","alias_value":"YMAAKVYJCIKV","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_16","alias_value":"YMAAKVYJCIKVDXCS","created_at":"2026-08-04T01:56:15Z"},{"alias_kind":"pith_short_8","alias_value":"YMAAKVYJ","created_at":"2026-08-04T01:56:15Z"}],"graph_snapshots":[{"event_id":"sha256:c6d56d59a1bedac0c37b58e53d20fd7e30f85d5795a42afa6cbac52020592201","target":"graph","created_at":"2026-08-04T01:56:15Z","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/2608.01032/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training error is what we can observe on a training set; test error is the quantity we actually care about. We study linear regression with squared-error in a deterministic $(d+1)$-dimensional single-spike model. Each stylized training vector has the same informative spike coordinate, of amplitude $\\sqrt{\\gamma}$ with $\\gamma>1$. The remaining directions are nuisance, and the nuisance components of distinct training vectors all have equal norm and are mutually orthogonal. The training labels are all $1$. Fresh test points are drawn from $\\vec{x}_{\\rm test} \\sim \\mathcal{N}(\\vec{0},\\operatornam","authors_text":"Anant Sahai, Gireeja Ranade","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-02T06:27:55Z","title":"The Fourth Quadrant: A Stylized View of Benign Misfitting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.01032","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":"sha256:4e9eb0c68aa179c2c773f0ac1ce7f146a0bb503efc711b17f22f304fd29c84fe","target":"record","created_at":"2026-08-04T01:56:15Z","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":"cfc2da572de6f5729ca88a3aec0a8eede4b7dba6dcb345901d4ecbb77ffb8488","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-08-02T06:27:55Z","title_canon_sha256":"11046688f0de064eac1f33148da4df69edff94a76a4e3098d3795b0e8ac997f6"},"schema_version":"1.0","source":{"id":"2608.01032","kind":"arxiv","version":1}},"canonical_sha256":"c300055709121551dc525fe32e63d856432e02d233029472ab544696fff54747","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c300055709121551dc525fe32e63d856432e02d233029472ab544696fff54747","first_computed_at":"2026-08-04T01:56:15.277299Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-04T01:56:15.277299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"77n9iQtw3r1E1gToqpXH725svKPu0q95XZyt3pP1GzhDHN1840Z7B0ALTBk3SW2/RfLO6k7T8z3W7UVtdrheBw==","signature_status":"signed_v1","signed_at":"2026-08-04T01:56:15.278917Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.01032","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:38d2bfcf1c12d7b7511df0ad9612795b2096f887f648d915e3d4921c85ac0688","sha256:74cd5f8bb530cc7282dcdd0c0a6ebb9df4a54bb764146d2a50729467178053dd","sha256:90e08603391e8406ff1c17d1f3199e8f4e947aac82ae9ae00195c6e116aa898c"]}],"invalid_events":[],"applied_event_ids":["sha256:4e9eb0c68aa179c2c773f0ac1ce7f146a0bb503efc711b17f22f304fd29c84fe","sha256:c6d56d59a1bedac0c37b58e53d20fd7e30f85d5795a42afa6cbac52020592201"],"state_sha256":"df99d181ad3b1fb56ecd6b193e697a90fe057c38203e021190fb4d96402e4998"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d/I5gfwXt3vxkYSBDIyA8CEfpZzqz6VU1nQkeeeCD/rpj5t+/Gxfyrkx+6D1hyCnT8Usek8PAgiAaZFI0Jh5Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T05:41:58.525961Z","bundle_sha256":"d1e0430fefc76b1fa418afda069c4f8058b30526d0c6211eaf6e095fec8f65ee"}}