{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CDI5UTGMP76PZ5VCXWSLZCOS45","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":"104d0b6723736efeb5d3c6e9b562f2a89326931c4abc1106790ec16b41e3e42e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-12T19:18:43Z","title_canon_sha256":"83cf5893574120c7c672b5ece5c463edfdd1d2a0a2f4b739b1cfa1db0cecd25d"},"schema_version":"1.0","source":{"id":"2605.12668","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12668","created_at":"2026-05-18T03:09:50Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12668v1","created_at":"2026-05-18T03:09:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12668","created_at":"2026-05-18T03:09:50Z"},{"alias_kind":"pith_short_12","alias_value":"CDI5UTGMP76P","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"CDI5UTGMP76PZ5VC","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"CDI5UTGM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:543dfd7a3765901b0b8516e8e7011e85486e29884a1ea50164731cc6fd4c4002","target":"graph","created_at":"2026-05-18T03:09:50Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the online optimization framework with small regret directly enforces both the coverage guarantees and the strict nestedness of prediction sets across levels without post-hoc adjustments or loss of efficiency."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Online conformal prediction methods produce nested sets across coverage levels by using low-regret online optimization to control quantile errors."}],"snapshot_sha256":"68e8542d14e8cdb142872e31ae14d98d81dffd16ac6b513d6ec2683fb93fad42"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has extended conformal prediction to online and sequential settings, existing methods typically focus on a single coverage level and do not ensure consistency across multiple confidence levels. In many real-world applications, such as weather forecasting, macroeconomic prediction, and risk management, different users operate under heterogeneous risk tolerances and require calibrated uncertainty estimates across a range of coverage levels. In such setting","authors_text":"Ambuj Tewari, Eduardo Ochoa Rivera","cross_cats":["cs.LG"],"headline":"Online conformal prediction methods produce nested sets across coverage levels by using low-regret online optimization to control quantile errors.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-12T19:18:43Z","title":"Online Conformal Prediction: Enforcing monotonicity via Online Optimization"},"references":{"count":29,"internal_anchors":1,"resolved_work":29,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=","work_id":"afcf6681-617d-4e87-9635-ff5e4552cb84","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"European conference on machine learning , pages=","work_id":"f691f1cf-e556-4ba0-818d-31916634d675","year":2002},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Algorithmic learning in a random world , author=. 2005 , publisher=","work_id":"31470f85-2f7f-4117-905b-da888e9ae129","year":2005},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A Tutorial on Conformal Prediction. , author=. Journal of Machine Learning Research , volume=","work_id":"0f562caf-c2b9-4167-b0f9-9533b9395e5e","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"The Annals of Statistics , volume=","work_id":"d51fa001-ea90-430d-9501-526dbd09809e","year":2023}],"snapshot_sha256":"5a05573b473c336bf51def3d951a7cf599592b436e043fa2321fd9a0e83f6752"},"source":{"id":"2605.12668","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:02:01.287080Z","id":"6a6d4e71-3afd-4cac-beaf-9e72131e8370","model_set":{"reader":"grok-4.3"},"one_line_summary":"Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Online conformal prediction methods produce nested sets across coverage levels by using low-regret online optimization to control quantile errors.","strongest_claim":"Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets.","weakest_assumption":"That the online optimization framework with small regret directly enforces both the coverage guarantees and the strict nestedness of prediction sets across levels without post-hoc adjustments or loss of efficiency."}},"verdict_id":"6a6d4e71-3afd-4cac-beaf-9e72131e8370"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:185dca26f929691b48f9c1ad353311d8c26a49ac2bf61b3e893c8dc8afa2675e","target":"record","created_at":"2026-05-18T03:09:50Z","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":"104d0b6723736efeb5d3c6e9b562f2a89326931c4abc1106790ec16b41e3e42e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-12T19:18:43Z","title_canon_sha256":"83cf5893574120c7c672b5ece5c463edfdd1d2a0a2f4b739b1cfa1db0cecd25d"},"schema_version":"1.0","source":{"id":"2605.12668","kind":"arxiv","version":1}},"canonical_sha256":"10d1da4ccc7ffcfcf6a2bda4bc89d2e76466e56bd53747cacc6e96cdcc69a8b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10d1da4ccc7ffcfcf6a2bda4bc89d2e76466e56bd53747cacc6e96cdcc69a8b9","first_computed_at":"2026-05-18T03:09:50.290047Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:50.290047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cO/tUOubzENo+uPH7v4Kuy8/BtQC+Crd3kn629Z9gzOlJn6Zq9/E4VPjHCSDuCYo7lH2Ji4h9ZiaNW9e6QtoBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:50.290964Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12668","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:185dca26f929691b48f9c1ad353311d8c26a49ac2bf61b3e893c8dc8afa2675e","sha256:543dfd7a3765901b0b8516e8e7011e85486e29884a1ea50164731cc6fd4c4002"],"state_sha256":"bb0fc338a3e27ac84cc5ff211e21e69949618bf5df34cf31fed7f61470ddfa4b"}