{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DXWMR2H2C4VVFJ7H22FHMFZYSL","short_pith_number":"pith:DXWMR2H2","canonical_record":{"source":{"id":"2506.09258","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T21:40:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"abc9af68d1bf27a5c47062ad127b1e27bfb19ea15a4790fccab1f59ba6fd6d0b","abstract_canon_sha256":"6e0ef8186ff0e4d5f46c1a3dd108bb43dfb2fe288c3b269980b8c74efc71c018"},"schema_version":"1.0"},"canonical_sha256":"1decc8e8fa172b52a7e7d68a76173892ea6a1cf5f4cda6ca291136c8c79333c2","source":{"kind":"arxiv","id":"2506.09258","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09258","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09258v1","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09258","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_12","alias_value":"DXWMR2H2C4VV","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_16","alias_value":"DXWMR2H2C4VVFJ7H","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_8","alias_value":"DXWMR2H2","created_at":"2026-07-05T11:19:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DXWMR2H2C4VVFJ7H22FHMFZYSL","target":"record","payload":{"canonical_record":{"source":{"id":"2506.09258","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T21:40:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"abc9af68d1bf27a5c47062ad127b1e27bfb19ea15a4790fccab1f59ba6fd6d0b","abstract_canon_sha256":"6e0ef8186ff0e4d5f46c1a3dd108bb43dfb2fe288c3b269980b8c74efc71c018"},"schema_version":"1.0"},"canonical_sha256":"1decc8e8fa172b52a7e7d68a76173892ea6a1cf5f4cda6ca291136c8c79333c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:33.813715Z","signature_b64":"CV6VjhkwaITvTxNmdUmPCWsQMAIZ/zrPqkR2AQw5zQL4LfjIk3vLxWAErF6c+Yes7yO9z8Ta3vFE1N1O+HreAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1decc8e8fa172b52a7e7d68a76173892ea6a1cf5f4cda6ca291136c8c79333c2","last_reissued_at":"2026-07-05T11:19:33.813253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:33.813253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.09258","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":"2026-07-05T11:19:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZ4KONGoeI2seNV8w+ccKDYaVDlrEJM3+xKOdbuvFkWhBgjgTWVGyYrvF+tb2Qia70U1+fVFxyohlns1EF56DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:28:19.896890Z"},"content_sha256":"4fa0371ab0ff1717353fc21b2b4ec7412a277532acd806bcb87ccd23107bb0ce","schema_version":"1.0","event_id":"sha256:4fa0371ab0ff1717353fc21b2b4ec7412a277532acd806bcb87ccd23107bb0ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DXWMR2H2C4VVFJ7H22FHMFZYSL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CFMI: Flow Matching for Missing Data Imputation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Michael U. Gutmann, Vaidotas Simkus","submitted_at":"2025-06-10T21:40:36Z","abstract_excerpt":"We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide range of metrics. Applying the method to zero-shot imputation of time-series data, we find that it m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09258","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/2506.09258/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":"2026-07-05T11:19:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GzVLdtTRrCznugR26wp0ADpxFu+eX4GKZc7Pu6FGO7kYr1BIDxXmgKAGZzixBVeT+Wn8MXL/jZHFUOQfM0R5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T18:28:19.898665Z"},"content_sha256":"2740397ec48145831c09050bdb6dc42fbe487496f3459ac2cb8c6912c6aac3d2","schema_version":"1.0","event_id":"sha256:2740397ec48145831c09050bdb6dc42fbe487496f3459ac2cb8c6912c6aac3d2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/bundle.json","state_url":"https://pith.science/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/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":"2026-08-05T18:28:19Z","links":{"resolver":"https://pith.science/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL","bundle":"https://pith.science/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/bundle.json","state":"https://pith.science/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DXWMR2H2C4VVFJ7H22FHMFZYSL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DXWMR2H2C4VVFJ7H22FHMFZYSL","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":"6e0ef8186ff0e4d5f46c1a3dd108bb43dfb2fe288c3b269980b8c74efc71c018","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T21:40:36Z","title_canon_sha256":"abc9af68d1bf27a5c47062ad127b1e27bfb19ea15a4790fccab1f59ba6fd6d0b"},"schema_version":"1.0","source":{"id":"2506.09258","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09258","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09258v1","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09258","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_12","alias_value":"DXWMR2H2C4VV","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_16","alias_value":"DXWMR2H2C4VVFJ7H","created_at":"2026-07-05T11:19:33Z"},{"alias_kind":"pith_short_8","alias_value":"DXWMR2H2","created_at":"2026-07-05T11:19:33Z"}],"graph_snapshots":[{"event_id":"sha256:2740397ec48145831c09050bdb6dc42fbe487496f3459ac2cb8c6912c6aac3d2","target":"graph","created_at":"2026-07-05T11:19:33Z","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/2506.09258/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide range of metrics. Applying the method to zero-shot imputation of time-series data, we find that it m","authors_text":"Michael U. Gutmann, Vaidotas Simkus","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T21:40:36Z","title":"CFMI: Flow Matching for Missing Data Imputation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09258","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":"sha256:4fa0371ab0ff1717353fc21b2b4ec7412a277532acd806bcb87ccd23107bb0ce","target":"record","created_at":"2026-07-05T11:19:33Z","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":"6e0ef8186ff0e4d5f46c1a3dd108bb43dfb2fe288c3b269980b8c74efc71c018","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T21:40:36Z","title_canon_sha256":"abc9af68d1bf27a5c47062ad127b1e27bfb19ea15a4790fccab1f59ba6fd6d0b"},"schema_version":"1.0","source":{"id":"2506.09258","kind":"arxiv","version":1}},"canonical_sha256":"1decc8e8fa172b52a7e7d68a76173892ea6a1cf5f4cda6ca291136c8c79333c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1decc8e8fa172b52a7e7d68a76173892ea6a1cf5f4cda6ca291136c8c79333c2","first_computed_at":"2026-07-05T11:19:33.813253Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:33.813253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CV6VjhkwaITvTxNmdUmPCWsQMAIZ/zrPqkR2AQw5zQL4LfjIk3vLxWAErF6c+Yes7yO9z8Ta3vFE1N1O+HreAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:33.813715Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.09258","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fa0371ab0ff1717353fc21b2b4ec7412a277532acd806bcb87ccd23107bb0ce","sha256:2740397ec48145831c09050bdb6dc42fbe487496f3459ac2cb8c6912c6aac3d2"],"state_sha256":"16dfdb00c1edbc7405cebfb2ecdb890f4aa211e9ff4632c8574f8aeb3feeca2b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wDDp6ML2eXCyjXUEeDqsH7xyh/XNjwxnRt0f0ehboa5ZJ26PnE3qgBDLT05M8x3CdNpB56QI/WKJeVhY1VUmCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T18:28:19.924260Z","bundle_sha256":"3d1f5d4606bdb74e0424aa2d79ad6eb07245997e5f7659cdfe3997cf847b1e40"}}