{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PKRNPKWHZZQ2Y25SAYTH5KCVL2","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":"330de83e9a1c47bf5b2ddf17bb72174f18ea10f6ce6e7aec62c659f0a594d531","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T16:31:54Z","title_canon_sha256":"3087569cf8d095cd28f830a41cfd6cc067c84caf4c33e7902f798800df9e2ca5"},"schema_version":"1.0","source":{"id":"2606.05073","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.05073","created_at":"2026-06-04T01:10:05Z"},{"alias_kind":"arxiv_version","alias_value":"2606.05073v1","created_at":"2026-06-04T01:10:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05073","created_at":"2026-06-04T01:10:05Z"},{"alias_kind":"pith_short_12","alias_value":"PKRNPKWHZZQ2","created_at":"2026-06-04T01:10:05Z"},{"alias_kind":"pith_short_16","alias_value":"PKRNPKWHZZQ2Y25S","created_at":"2026-06-04T01:10:05Z"},{"alias_kind":"pith_short_8","alias_value":"PKRNPKWH","created_at":"2026-06-04T01:10:05Z"}],"graph_snapshots":[{"event_id":"sha256:3d8554497471f45bba7fcee7874d32ea0451429d277726c4e5f173a9eb702b3e","target":"graph","created_at":"2026-06-04T01:10:05Z","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/2606.05073/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed. We formalize this distinction as a selective imputation problem, where the goal is to jointly infer which missing entries should be preserved and which should be recovered. To add","authors_text":"Guang Cheng, Lixing Zhang, Liyan Xie, Shixiang Zhu, Weifu Li, Yidong Ouyang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T16:31:54Z","title":"Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05073","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:ae62bd0fa7f54ec48d8a18eecc9acc8c8512fde3bcba19b5988a77693f86e7b3","target":"record","created_at":"2026-06-04T01:10:05Z","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":"330de83e9a1c47bf5b2ddf17bb72174f18ea10f6ce6e7aec62c659f0a594d531","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T16:31:54Z","title_canon_sha256":"3087569cf8d095cd28f830a41cfd6cc067c84caf4c33e7902f798800df9e2ca5"},"schema_version":"1.0","source":{"id":"2606.05073","kind":"arxiv","version":1}},"canonical_sha256":"7aa2d7aac7ce61ac6bb206267ea8555e85167d157b7b7229566861f81dbf98cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7aa2d7aac7ce61ac6bb206267ea8555e85167d157b7b7229566861f81dbf98cb","first_computed_at":"2026-06-04T01:10:05.118309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:10:05.118309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FItbOi1Jx3DZ+o+OFOoK/St2Z06BD1RrouxUrh0wgljDKLzkl1cip3ShTgfJ1C7Eqi1x28z+JVYA1sUGsy98BQ==","signature_status":"signed_v1","signed_at":"2026-06-04T01:10:05.119100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.05073","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae62bd0fa7f54ec48d8a18eecc9acc8c8512fde3bcba19b5988a77693f86e7b3","sha256:3d8554497471f45bba7fcee7874d32ea0451429d277726c4e5f173a9eb702b3e"],"state_sha256":"2b7c41a2d2ea80ad076bbeecbf9a84463dd8ed09b682f13695ee278b6895ee1a"}