{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XK3WLXAGIKEXCVTYOU6L3PPBSH","short_pith_number":"pith:XK3WLXAG","canonical_record":{"source":{"id":"2607.20140","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-22T13:41:17Z","cross_cats_sorted":[],"title_canon_sha256":"120b2f7d1fd404cb81f4552986db9212e7e8de84d6ba39ec3dc8754379b43a6d","abstract_canon_sha256":"a7107ceac968dfa9e9d46adefdacb4d4a900502fdcf1ff56511d025655a929da"},"schema_version":"1.0"},"canonical_sha256":"bab765dc064289715678753cbdbde191f3fd86d82da8db295a0b7c96b9aa0b75","source":{"kind":"arxiv","id":"2607.20140","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.20140","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"arxiv_version","alias_value":"2607.20140v1","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20140","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_12","alias_value":"XK3WLXAGIKEX","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_16","alias_value":"XK3WLXAGIKEXCVTY","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_8","alias_value":"XK3WLXAG","created_at":"2026-07-23T01:25:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XK3WLXAGIKEXCVTYOU6L3PPBSH","target":"record","payload":{"canonical_record":{"source":{"id":"2607.20140","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-22T13:41:17Z","cross_cats_sorted":[],"title_canon_sha256":"120b2f7d1fd404cb81f4552986db9212e7e8de84d6ba39ec3dc8754379b43a6d","abstract_canon_sha256":"a7107ceac968dfa9e9d46adefdacb4d4a900502fdcf1ff56511d025655a929da"},"schema_version":"1.0"},"canonical_sha256":"bab765dc064289715678753cbdbde191f3fd86d82da8db295a0b7c96b9aa0b75","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T01:25:03.582877Z","signature_b64":"bHocjK7Ni20DsR8ofgBygIKW0u0I+d6O3VKFm1aUH0phq6PAGca/4EI54c2xX0t2qmShJsO2SzgP3m46aIF0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bab765dc064289715678753cbdbde191f3fd86d82da8db295a0b7c96b9aa0b75","last_reissued_at":"2026-07-23T01:25:03.582019Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T01:25:03.582019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.20140","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":"2026-07-23T01:25:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NjCPclJPyTrdn/LGMngTw48zoo/Re7KjoXVfVVwYry3fQRHJnx+o2WqwoyG9u3ESLQPwzFfmnWMSYVgcXWrADQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:15:01.081875Z"},"content_sha256":"4017e1cc9f07476e85e39a33bcd19b812e5c77ee3759ea596913c2a0a1cdcd29","schema_version":"1.0","event_id":"sha256:4017e1cc9f07476e85e39a33bcd19b812e5c77ee3759ea596913c2a0a1cdcd29"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XK3WLXAGIKEXCVTYOU6L3PPBSH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CURED: Creating, Understanding, and Repairing Errors Demonstrator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Felix Bie{\\ss}mann, Nicholas Chandler, Philipp Jung, Sebastian J\\\"ager","submitted_at":"2026-07-22T13:41:17Z","abstract_excerpt":"Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20140","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/2607.20140/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":"2026-07-23T01:25:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5PGl4sa0LbN1G88bTxgFrClv6RYQfz4jywG/U0hMDDdzRIVAXscUvbWyqfmmfpXHJ7J6l4/Dblj8p5yk89kXBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:15:01.082373Z"},"content_sha256":"d2dd71e7b4720b0a5ca1d7e8ba2a3b912e0ec43d98b55f6e8cf72e870e0768fc","schema_version":"1.0","event_id":"sha256:d2dd71e7b4720b0a5ca1d7e8ba2a3b912e0ec43d98b55f6e8cf72e870e0768fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/bundle.json","state_url":"https://pith.science/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/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":"2026-08-04T10:15:01Z","links":{"resolver":"https://pith.science/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH","bundle":"https://pith.science/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/bundle.json","state":"https://pith.science/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XK3WLXAGIKEXCVTYOU6L3PPBSH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XK3WLXAGIKEXCVTYOU6L3PPBSH","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":"a7107ceac968dfa9e9d46adefdacb4d4a900502fdcf1ff56511d025655a929da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-22T13:41:17Z","title_canon_sha256":"120b2f7d1fd404cb81f4552986db9212e7e8de84d6ba39ec3dc8754379b43a6d"},"schema_version":"1.0","source":{"id":"2607.20140","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.20140","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"arxiv_version","alias_value":"2607.20140v1","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20140","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_12","alias_value":"XK3WLXAGIKEX","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_16","alias_value":"XK3WLXAGIKEXCVTY","created_at":"2026-07-23T01:25:03Z"},{"alias_kind":"pith_short_8","alias_value":"XK3WLXAG","created_at":"2026-07-23T01:25:03Z"}],"graph_snapshots":[{"event_id":"sha256:d2dd71e7b4720b0a5ca1d7e8ba2a3b912e0ec43d98b55f6e8cf72e870e0768fc","target":"graph","created_at":"2026-07-23T01:25:03Z","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/2607.20140/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demon","authors_text":"Felix Bie{\\ss}mann, Nicholas Chandler, Philipp Jung, Sebastian J\\\"ager","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-22T13:41:17Z","title":"CURED: Creating, Understanding, and Repairing Errors Demonstrator"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20140","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":"sha256:4017e1cc9f07476e85e39a33bcd19b812e5c77ee3759ea596913c2a0a1cdcd29","target":"record","created_at":"2026-07-23T01:25:03Z","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":"a7107ceac968dfa9e9d46adefdacb4d4a900502fdcf1ff56511d025655a929da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-22T13:41:17Z","title_canon_sha256":"120b2f7d1fd404cb81f4552986db9212e7e8de84d6ba39ec3dc8754379b43a6d"},"schema_version":"1.0","source":{"id":"2607.20140","kind":"arxiv","version":1}},"canonical_sha256":"bab765dc064289715678753cbdbde191f3fd86d82da8db295a0b7c96b9aa0b75","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bab765dc064289715678753cbdbde191f3fd86d82da8db295a0b7c96b9aa0b75","first_computed_at":"2026-07-23T01:25:03.582019Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-23T01:25:03.582019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bHocjK7Ni20DsR8ofgBygIKW0u0I+d6O3VKFm1aUH0phq6PAGca/4EI54c2xX0t2qmShJsO2SzgP3m46aIF0Dg==","signature_status":"signed_v1","signed_at":"2026-07-23T01:25:03.582877Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.20140","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4017e1cc9f07476e85e39a33bcd19b812e5c77ee3759ea596913c2a0a1cdcd29","sha256:d2dd71e7b4720b0a5ca1d7e8ba2a3b912e0ec43d98b55f6e8cf72e870e0768fc"],"state_sha256":"0958f364cf3d5c70c1a4a904a69d1bd38d07c14ffe97a3c08b4ccb4cc8b767c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FdHLPvSSYKx6q98WL22WdjK+1UTvLVSC4aKsx4EqjQO/A8AmXPu6C/t+JjmMZvsOEW2oQmLFAmZyhQalrIGwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T10:15:01.087109Z","bundle_sha256":"ba2bcca90c559f238d94b8ea6b2a825d8469a6d0fb864c98db2e314f06129dda"}}