{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:W4NNGH3BGGLGMQBMSHONPDEJLB","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":"3796f48416faa9985343c960f44e0733108aee031c35bc1701edaba6bc1cfd1e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T19:01:45Z","title_canon_sha256":"a707ded989e7a8c2fa9bf5707925d0e167dfeb5e4d5104123b56ca9d69519068"},"schema_version":"1.0","source":{"id":"2402.08733","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.08733","created_at":"2026-07-05T08:24:01Z"},{"alias_kind":"arxiv_version","alias_value":"2402.08733v2","created_at":"2026-07-05T08:24:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08733","created_at":"2026-07-05T08:24:01Z"},{"alias_kind":"pith_short_12","alias_value":"W4NNGH3BGGLG","created_at":"2026-07-05T08:24:01Z"},{"alias_kind":"pith_short_16","alias_value":"W4NNGH3BGGLGMQBM","created_at":"2026-07-05T08:24:01Z"},{"alias_kind":"pith_short_8","alias_value":"W4NNGH3B","created_at":"2026-07-05T08:24:01Z"}],"graph_snapshots":[{"event_id":"sha256:c534eb1b31c636cf03fa042129220bdab7dfb889a60a9a84463f30428255dc57","target":"graph","created_at":"2026-07-05T08:24:01Z","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/2402.08733/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Identifying how much a model ${\\widehat{p}}_{\\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or \"hallucinated\" answers or taking unsafe actions. But this is difficult for generative models because probabilistic predictions do not distinguish between per-response noise (aleatoric uncertainty) and lack of knowledge about the process (epistemic uncertainty), and existing epistemic uncertainty quantification techniques tend to be overconfident when the model underfits. We propose a general strategy for teac","authors_text":"Chris J. Maddison, Daniel D. Johnson, Daniel Tarlow, David Duvenaud","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T19:01:45Z","title":"Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08733","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":"sha256:4445021e6a7c995f53482dbfad99620d908c830aa6b0259a2f01125e8bf3f73a","target":"record","created_at":"2026-07-05T08:24:01Z","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":"3796f48416faa9985343c960f44e0733108aee031c35bc1701edaba6bc1cfd1e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-13T19:01:45Z","title_canon_sha256":"a707ded989e7a8c2fa9bf5707925d0e167dfeb5e4d5104123b56ca9d69519068"},"schema_version":"1.0","source":{"id":"2402.08733","kind":"arxiv","version":2}},"canonical_sha256":"b71ad31f61319666402c91dcd78c89585420110893c5304834abe2de4378494f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b71ad31f61319666402c91dcd78c89585420110893c5304834abe2de4378494f","first_computed_at":"2026-07-05T08:24:01.546868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:24:01.546868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fLZyT+GHxapPKMC9/pQ7KRlYoYf9glM4k8E2mPS1HO3Bcnl/f9PLS/VJJpSPjWImz8B6aFj2G++sfC+WNMQjCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:24:01.547403Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.08733","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4445021e6a7c995f53482dbfad99620d908c830aa6b0259a2f01125e8bf3f73a","sha256:c534eb1b31c636cf03fa042129220bdab7dfb889a60a9a84463f30428255dc57"],"state_sha256":"e0d6bd6ea4507ac6cdd3ef9be56f9aa2c4ae9712758937f7bf6839aaacb27ce4"}