{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BXP66B3TNJIF7VJL6V25EKIFZT","short_pith_number":"pith:BXP66B3T","canonical_record":{"source":{"id":"2501.19224","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-31T15:31:01Z","cross_cats_sorted":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"title_canon_sha256":"fdd6189e320da9167e0a3c4b7aa2e0bf5bb32f0913d962ecf789e458d6f397fb","abstract_canon_sha256":"f906e5ff5b6b0227c34121674c5a40df2b95771b1a54f0611a043b26d1d396db"},"schema_version":"1.0"},"canonical_sha256":"0ddfef07736a505fd52bf575d22905ccff4bd8bc0ff16387f3e6184dd049fa30","source":{"kind":"arxiv","id":"2501.19224","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.19224","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.19224v2","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.19224","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"BXP66B3TNJIF","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"BXP66B3TNJIF7VJL","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"BXP66B3T","created_at":"2026-07-05T10:24:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BXP66B3TNJIF7VJL6V25EKIFZT","target":"record","payload":{"canonical_record":{"source":{"id":"2501.19224","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-31T15:31:01Z","cross_cats_sorted":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"title_canon_sha256":"fdd6189e320da9167e0a3c4b7aa2e0bf5bb32f0913d962ecf789e458d6f397fb","abstract_canon_sha256":"f906e5ff5b6b0227c34121674c5a40df2b95771b1a54f0611a043b26d1d396db"},"schema_version":"1.0"},"canonical_sha256":"0ddfef07736a505fd52bf575d22905ccff4bd8bc0ff16387f3e6184dd049fa30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:30.580248Z","signature_b64":"Qc9rlbkovo9Jt3rT39MMyeHmOpnriZe/iMDFf3fJDYRyXGSU3QBLvrvG2DEUKegCxu2TodLaN3zrQ4KEL+3EBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddfef07736a505fd52bf575d22905ccff4bd8bc0ff16387f3e6184dd049fa30","last_reissued_at":"2026-07-05T10:24:30.579667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:30.579667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.19224","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d/biegenc+e53hFWB9/CyQbbObHsCFg2O8jfo92YuOv80Q5NMY7nVA2qqZiy+uEhsgTYoJUyoREIu/6nOegeCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:04:58.956219Z"},"content_sha256":"ba4db716d54f8543945c9659b4aa5fb8210ccf7d4ad3c2b2bba65144dcc517db","schema_version":"1.0","event_id":"sha256:ba4db716d54f8543945c9659b4aa5fb8210ccf7d4ad3c2b2bba65144dcc517db"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BXP66B3TNJIF7VJL6V25EKIFZT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast exact recovery of noisy matrix from few entries: the infinity norm approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"primary_cat":"math.ST","authors_text":"BaoLinh Tran, Van Vu","submitted_at":"2025-01-31T15:31:01Z","abstract_excerpt":"The matrix recovery (completion) problem, a central problem in data science and theoretical computer science, is to recover a matrix $A$ from a relatively small sample of entries.\n  While such a task is impossible in general, it has been shown that one can recover $A$ exactly in polynomial time, with high probability, from a random subset of entries, under three (basic and necessary) assumptions: (1) the rank of $A$ is very small compared to its dimensions (low rank), (2) $A$ has delocalized singular vectors (incoherence), and (3) the sample size is sufficiently large.\n  There are many differe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.19224","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/2501.19224/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":"2026-07-05T10:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R60fdndItm+3nZ/RY1pQc+DgRBRFZo4grsudx34DalJl80esR8UOjNkXkHEwMvrHgD0+yoOFFm6ZESgy0gYVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:04:58.957244Z"},"content_sha256":"d6564fa98a96e52aec042e1cc138777acea291e2c6321d8b4eec5c7021d7a54a","schema_version":"1.0","event_id":"sha256:d6564fa98a96e52aec042e1cc138777acea291e2c6321d8b4eec5c7021d7a54a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BXP66B3TNJIF7VJL6V25EKIFZT/bundle.json","state_url":"https://pith.science/pith/BXP66B3TNJIF7VJL6V25EKIFZT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BXP66B3TNJIF7VJL6V25EKIFZT/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":"2026-08-09T22:04:58Z","links":{"resolver":"https://pith.science/pith/BXP66B3TNJIF7VJL6V25EKIFZT","bundle":"https://pith.science/pith/BXP66B3TNJIF7VJL6V25EKIFZT/bundle.json","state":"https://pith.science/pith/BXP66B3TNJIF7VJL6V25EKIFZT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BXP66B3TNJIF7VJL6V25EKIFZT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BXP66B3TNJIF7VJL6V25EKIFZT","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":"f906e5ff5b6b0227c34121674c5a40df2b95771b1a54f0611a043b26d1d396db","cross_cats_sorted":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-31T15:31:01Z","title_canon_sha256":"fdd6189e320da9167e0a3c4b7aa2e0bf5bb32f0913d962ecf789e458d6f397fb"},"schema_version":"1.0","source":{"id":"2501.19224","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.19224","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.19224v2","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.19224","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"BXP66B3TNJIF","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"BXP66B3TNJIF7VJL","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"BXP66B3T","created_at":"2026-07-05T10:24:30Z"}],"graph_snapshots":[{"event_id":"sha256:d6564fa98a96e52aec042e1cc138777acea291e2c6321d8b4eec5c7021d7a54a","target":"graph","created_at":"2026-07-05T10:24:30Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.19224/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The matrix recovery (completion) problem, a central problem in data science and theoretical computer science, is to recover a matrix $A$ from a relatively small sample of entries.\n  While such a task is impossible in general, it has been shown that one can recover $A$ exactly in polynomial time, with high probability, from a random subset of entries, under three (basic and necessary) assumptions: (1) the rank of $A$ is very small compared to its dimensions (low rank), (2) $A$ has delocalized singular vectors (incoherence), and (3) the sample size is sufficiently large.\n  There are many differe","authors_text":"BaoLinh Tran, Van Vu","cross_cats":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-31T15:31:01Z","title":"Fast exact recovery of noisy matrix from few entries: the infinity norm approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.19224","kind":"arxiv","version":2},"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:ba4db716d54f8543945c9659b4aa5fb8210ccf7d4ad3c2b2bba65144dcc517db","target":"record","created_at":"2026-07-05T10:24:30Z","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":"f906e5ff5b6b0227c34121674c5a40df2b95771b1a54f0611a043b26d1d396db","cross_cats_sorted":["cs.LG","math.CO","math.PR","stat.AP","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-01-31T15:31:01Z","title_canon_sha256":"fdd6189e320da9167e0a3c4b7aa2e0bf5bb32f0913d962ecf789e458d6f397fb"},"schema_version":"1.0","source":{"id":"2501.19224","kind":"arxiv","version":2}},"canonical_sha256":"0ddfef07736a505fd52bf575d22905ccff4bd8bc0ff16387f3e6184dd049fa30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ddfef07736a505fd52bf575d22905ccff4bd8bc0ff16387f3e6184dd049fa30","first_computed_at":"2026-07-05T10:24:30.579667Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:24:30.579667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qc9rlbkovo9Jt3rT39MMyeHmOpnriZe/iMDFf3fJDYRyXGSU3QBLvrvG2DEUKegCxu2TodLaN3zrQ4KEL+3EBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:24:30.580248Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.19224","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba4db716d54f8543945c9659b4aa5fb8210ccf7d4ad3c2b2bba65144dcc517db","sha256:d6564fa98a96e52aec042e1cc138777acea291e2c6321d8b4eec5c7021d7a54a"],"state_sha256":"43f47dd8378461dee691cbc4d9e093fb3c5808b27a13078af23c556cf1173af9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q8fcNHrVIWISDWcpQ0FWfNpsrII3TO1D6OfiDAEFnjE3AKcA1hd0LehZfaaEGYRgOeZO1gU78rVSziZL47HRCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T22:04:58.963561Z","bundle_sha256":"b7b7360b463011fda70b741ec382be393f75feb3bd83a1cc8e3e6c292040bf28"}}