{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GGBMUG2AWIM3F6RNFYKIDMJAIX","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":"4148eb9e5891ddc374b55419431bb02c5f0d4c8158664aac7ac9fb35c5355cb1","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T09:47:05Z","title_canon_sha256":"20194ed00be07879952963307028215f0a3d9c36c27511201c3c60c94b024d34"},"schema_version":"1.0","source":{"id":"2605.15789","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15789","created_at":"2026-05-20T00:01:18Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15789v1","created_at":"2026-05-20T00:01:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15789","created_at":"2026-05-20T00:01:18Z"},{"alias_kind":"pith_short_12","alias_value":"GGBMUG2AWIM3","created_at":"2026-05-20T00:01:18Z"},{"alias_kind":"pith_short_16","alias_value":"GGBMUG2AWIM3F6RN","created_at":"2026-05-20T00:01:18Z"},{"alias_kind":"pith_short_8","alias_value":"GGBMUG2A","created_at":"2026-05-20T00:01:18Z"}],"graph_snapshots":[{"event_id":"sha256:4ead6f6d8126524e56ea71b7a1e459137711d3986ea3485d4df643c22044ff56","target":"graph","created_at":"2026-05-20T00:01:18Z","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":"ai_meta_artifact","ran_at":"2026-05-19T17:33:48.745864Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.915007Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15789/integrity.json","findings":[],"snapshot_sha256":"b588f266d539550df2363b8bbb8185dc367789da4d0f54778242c261a47a4f48","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely on conservative bounds rather than point estimates. Predictor-level intervals (e.g., from quantile regression, conformal prediction, variance networks, or Bayesian models) generally do not compose: adding two per-variable intervals need not yield a valid interval for their sum or preserve coverage. In aviation, Gaussian overbounding replaces complex error distributions with a conservative Gaussian whose tails dominate the truth, so conservatis","authors_text":"Hui Ren, Ruirui Liu, Xuejie Hou, Yiping Jiang","cross_cats":["eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T09:47:05Z","title":"Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15789","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":"sha256:44e46c0e87349f0797977263a5c2dfec7bfdfcdaf7ae79b79048983fabec56e2","target":"record","created_at":"2026-05-20T00:01:18Z","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":"4148eb9e5891ddc374b55419431bb02c5f0d4c8158664aac7ac9fb35c5355cb1","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T09:47:05Z","title_canon_sha256":"20194ed00be07879952963307028215f0a3d9c36c27511201c3c60c94b024d34"},"schema_version":"1.0","source":{"id":"2605.15789","kind":"arxiv","version":1}},"canonical_sha256":"3182ca1b40b219b2fa2d2e1481b12045c4d62d346424d2eefa338a834982cb57","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3182ca1b40b219b2fa2d2e1481b12045c4d62d346424d2eefa338a834982cb57","first_computed_at":"2026-05-20T00:01:18.404228Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:18.404228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Kab1sqm0IT1uNIB4Gm+3nRVJSJxxHp7Wmqzpsh1F2pVpP69QhUPB03R7jtfrqGXqbzuUWL9kXfBWKwLiuTuMCw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:18.405200Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15789","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44e46c0e87349f0797977263a5c2dfec7bfdfcdaf7ae79b79048983fabec56e2","sha256:4ead6f6d8126524e56ea71b7a1e459137711d3986ea3485d4df643c22044ff56"],"state_sha256":"50c6a108b68effc851fd5cbe0fe03d3a803830fd0d74e9f57dd68c7ed186ce32"}