{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PTLIJZYJTPW3WRTT3BWF5T3542","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":"d94c715036a49dfcc703cba69b78fa506350ffc88a44376e5d2a2ddfadc53d26","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2026-05-18T08:34:07Z","title_canon_sha256":"8375ae6467e44c3324af3e341207739552638f76311fb07bdb00f5f9953eabd1"},"schema_version":"1.0","source":{"id":"2605.18042","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18042","created_at":"2026-05-20T00:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18042v1","created_at":"2026-05-20T00:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18042","created_at":"2026-05-20T00:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"PTLIJZYJTPW3","created_at":"2026-05-20T00:05:12Z"},{"alias_kind":"pith_short_16","alias_value":"PTLIJZYJTPW3WRTT","created_at":"2026-05-20T00:05:12Z"},{"alias_kind":"pith_short_8","alias_value":"PTLIJZYJ","created_at":"2026-05-20T00:05:12Z"}],"graph_snapshots":[{"event_id":"sha256:d61b4f9d2017e21db7da5d0d6448ce086885b5b3e6bacdf6fcd2333c48223519","target":"graph","created_at":"2026-05-20T00:05:12Z","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":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T23:41:59.304548Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.495131Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.18042/integrity.json","findings":[],"snapshot_sha256":"6cf8b8a0c6f72bdfb21d4ead286c75c5e77575304ff95926e937b11342b10514","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We revisit the problem of robust linear regression under Gaussian covariates with an unknown covariance matrix of condition number $\\kappa$. For this fundamental problem, significant gaps remain in our understanding of the trade-offs among sample complexity, condition number, runtime, and prediction error for efficient algorithms. Our first result is a near-linear-time algorithm that uses $\\widetilde{O}(d/\\epsilon^4)$ samples, where $d$ is the dimension and $\\epsilon$ is the corruption rate, and achieves prediction error $O(\\sqrt{\\epsilon\\kappa})$ under the condition $\\epsilon\\kappa \\lesssim 1","authors_text":"Deeksha Adil, Deepak Narayanan Sridharan, Hongjie Chen, Jaros{\\l}aw B{\\l}asiok","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2026-05-18T08:34:07Z","title":"On efficient robust regression with subquadratic samples"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18042","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":"sha256:8f44e3d5d66a7707b9797de6279f40355e6133939ce6506c6e01edbc8ba80fc0","target":"record","created_at":"2026-05-20T00:05:12Z","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":"d94c715036a49dfcc703cba69b78fa506350ffc88a44376e5d2a2ddfadc53d26","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2026-05-18T08:34:07Z","title_canon_sha256":"8375ae6467e44c3324af3e341207739552638f76311fb07bdb00f5f9953eabd1"},"schema_version":"1.0","source":{"id":"2605.18042","kind":"arxiv","version":1}},"canonical_sha256":"7cd684e7099bedbb4673d86c5ecf7de6824e66edf98482b220b482037999f0b0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7cd684e7099bedbb4673d86c5ecf7de6824e66edf98482b220b482037999f0b0","first_computed_at":"2026-05-20T00:05:12.864117Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:05:12.864117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GUK1gVuoSchCAm9h7WctQzeZj67iiR8p1SOTimeKFS0EsbO2R8GR9ykqAPyJ1V7YlYSp4VCO6ALdFpb+nlJoDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:05:12.864855Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18042","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8f44e3d5d66a7707b9797de6279f40355e6133939ce6506c6e01edbc8ba80fc0","sha256:d61b4f9d2017e21db7da5d0d6448ce086885b5b3e6bacdf6fcd2333c48223519"],"state_sha256":"6e02a3b396fe95de964e9496ddcd20705f498fcef013e0621661681c61cf209b"}