{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5LNFDIPA3LFNE4RFUNCZUJLLIG","short_pith_number":"pith:5LNFDIPA","schema_version":"1.0","canonical_sha256":"eada51a1e0dacad27225a3459a256b41a419f96c178940e6a6aedbd6edec2fdc","source":{"kind":"arxiv","id":"2503.02802","version":2},"attestation_state":"computed","paper":{"title":"Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CC","stat.TH"],"primary_cat":"math.ST","authors_text":"Alina Harbuzova, Guy Bresler","submitted_at":"2025-03-04T17:25:32Z","abstract_excerpt":"In this work, we show the first average-case reduction transforming the sparse Spiked Covariance Model into the sparse Spiked Wigner Model and as a consequence obtain the first computational equivalence result between two well-studied high-dimensional statistics models. Our approach leverages a new perturbation equivariance property for Gram-Schmidt orthogonalization, enabling removal of dependence in the noise while preserving the signal."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2503.02802","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-03-04T17:25:32Z","cross_cats_sorted":["cs.CC","stat.TH"],"title_canon_sha256":"f4c589d6aeaf8ea66a7582725e747b3a0be72bbd68ef0d6a7faa022400c129f8","abstract_canon_sha256":"78c29fceaf5b51fc1a716afd6606504fd4a2e65da94f2fe1080a55cf9d30737b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:43.726085Z","signature_b64":"AT7MfdNWGL1hqXw/aUCezOICuUTjL12TCm0ni1PIKNn2y6X+RSn3CUPvqSXm/u9I7OTCMV+4LVfRSd9AfL+aAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eada51a1e0dacad27225a3459a256b41a419f96c178940e6a6aedbd6edec2fdc","last_reissued_at":"2026-07-05T11:21:43.725503Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:43.725503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CC","stat.TH"],"primary_cat":"math.ST","authors_text":"Alina Harbuzova, Guy Bresler","submitted_at":"2025-03-04T17:25:32Z","abstract_excerpt":"In this work, we show the first average-case reduction transforming the sparse Spiked Covariance Model into the sparse Spiked Wigner Model and as a consequence obtain the first computational equivalence result between two well-studied high-dimensional statistics models. Our approach leverages a new perturbation equivariance property for Gram-Schmidt orthogonalization, enabling removal of dependence in the noise while preserving the signal."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02802","kind":"arxiv","version":2},"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/2503.02802/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.02802","created_at":"2026-07-05T11:21:43.725576+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02802v2","created_at":"2026-07-05T11:21:43.725576+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02802","created_at":"2026-07-05T11:21:43.725576+00:00"},{"alias_kind":"pith_short_12","alias_value":"5LNFDIPA3LFN","created_at":"2026-07-05T11:21:43.725576+00:00"},{"alias_kind":"pith_short_16","alias_value":"5LNFDIPA3LFNE4RF","created_at":"2026-07-05T11:21:43.725576+00:00"},{"alias_kind":"pith_short_8","alias_value":"5LNFDIPA","created_at":"2026-07-05T11:21:43.725576+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10748","citing_title":"Computational Complexity of Statistics: New Insights from Low-Degree Polynomials","ref_index":2022,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG","json":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG.json","graph_json":"https://pith.science/api/pith-number/5LNFDIPA3LFNE4RFUNCZUJLLIG/graph.json","events_json":"https://pith.science/api/pith-number/5LNFDIPA3LFNE4RFUNCZUJLLIG/events.json","paper":"https://pith.science/paper/5LNFDIPA"},"agent_actions":{"view_html":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG","download_json":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG.json","view_paper":"https://pith.science/paper/5LNFDIPA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02802&json=true","fetch_graph":"https://pith.science/api/pith-number/5LNFDIPA3LFNE4RFUNCZUJLLIG/graph.json","fetch_events":"https://pith.science/api/pith-number/5LNFDIPA3LFNE4RFUNCZUJLLIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG/action/storage_attestation","attest_author":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG/action/author_attestation","sign_citation":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG/action/citation_signature","submit_replication":"https://pith.science/pith/5LNFDIPA3LFNE4RFUNCZUJLLIG/action/replication_record"}},"created_at":"2026-07-05T11:21:43.725576+00:00","updated_at":"2026-07-05T11:21:43.725576+00:00"}