{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:DHTKYIC4KDJ2LWSGONOGUBQUUW","short_pith_number":"pith:DHTKYIC4","canonical_record":{"source":{"id":"2602.01629","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"12c979088a762b89d10ad00d7cd5e4ab3ca3983a15c3a5deb01f832554a3ca07","abstract_canon_sha256":"d820c87328a8b4a7f78b3a67516116579d372eaa0abfa28ce00247325bf55687"},"schema_version":"1.0"},"canonical_sha256":"19e6ac205c50d3a5da46735c6a0614a5b2eca169b7f25d77cdf324fea1e4662d","source":{"kind":"arxiv","id":"2602.01629","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.01629","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"arxiv_version","alias_value":"2602.01629v2","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01629","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"pith_short_12","alias_value":"DHTKYIC4KDJ2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"DHTKYIC4KDJ2LWSG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"DHTKYIC4","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:DHTKYIC4KDJ2LWSGONOGUBQUUW","target":"record","payload":{"canonical_record":{"source":{"id":"2602.01629","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35Z","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"title_canon_sha256":"12c979088a762b89d10ad00d7cd5e4ab3ca3983a15c3a5deb01f832554a3ca07","abstract_canon_sha256":"d820c87328a8b4a7f78b3a67516116579d372eaa0abfa28ce00247325bf55687"},"schema_version":"1.0"},"canonical_sha256":"19e6ac205c50d3a5da46735c6a0614a5b2eca169b7f25d77cdf324fea1e4662d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:31.710675Z","signature_b64":"v4i+6x9VHu9Qm9v0ehFW9LwmsuKGOPR3F09WGrJpBd63dRzyVsXolGWeTOzCR15dtVGDrQrbN4s1pVmCpZBcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19e6ac205c50d3a5da46735c6a0614a5b2eca169b7f25d77cdf324fea1e4662d","last_reissued_at":"2026-05-18T02:44:31.710013Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:31.710013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.01629","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:44:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/3gLdRVRvZ1S7cCavLvlugPYlPsefu+8EBCL8NWGst7rl0HCNbTrIL8LZmC3LWsrnwWOgMOgeBopblNQi7fpCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:15:01.399286Z"},"content_sha256":"01fdb0f1cc44f13592de2362bbe072b1850c35231b9a0843881eb315f6c22757","schema_version":"1.0","event_id":"sha256:01fdb0f1cc44f13592de2362bbe072b1850c35231b9a0843881eb315f6c22757"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:DHTKYIC4KDJ2LWSGONOGUBQUUW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage.","cross_cats":["cs.RO","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Aditya Singh, Rahul Mangharam, Renukanandan Tumu","submitted_at":"2026-02-02T04:41:35Z","abstract_excerpt":"Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient predictio"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The adaptive reweighting of nonconformity scores combined with the replay buffer preserves the marginal coverage guarantees of conformal prediction during online transitions under distribution shift.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e4f570b63a146021d9d8397e547ef5058051a927d6733e21e90003daa72051f2"},"source":{"id":"2602.01629","kind":"arxiv","version":2},"verdict":{"id":"b28bc6b9-f206-4263-8a90-c387270d618c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T08:04:01.606656Z","strongest_claim":"AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.","one_line_summary":"AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The adaptive reweighting of nonconformity scores combined with the replay buffer preserves the marginal coverage guarantees of conformal prediction during online transitions under distribution shift.","pith_extraction_headline":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage."},"references":{"count":16,"sample":[{"doi":"10.1016/j.jmva.2005","year":2005,"title":"doi: 10.1016/j.jmva.2005","work_id":"50ca57c1-e2c3-4818-927c-f0ec18a943bc","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Gao, C., Shan, L., Srinivas, V ., and Vijayaragha- van, A","work_id":"b757e598-257e-41a0-b920-95020b3b5023","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"URL https://openreview.net/forum? id=oNDhnGrD51&noteId=7kR09SC5BY. Gibbs, I. and Candes, E. Adaptive Conformal In- ference Under Distribution Shift. InAdvances in Neural Information Processing Systems","work_id":"92fc93d0-606a-4ced-b0c7-05b8f7bafa25","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1103/physreve.51","year":1991,"title":"URL http://jmlr.org/ papers/v25/22-1218.html","work_id":"f21483a7-5973-4621-8e28-7cf1941b8a6d","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.52202/079017-3158","year":1994,"title":"1103/PhysRevE.51.4282","work_id":"c854b72c-6d0d-4ddb-9324-26d799cc2ea3","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":16,"snapshot_sha256":"9dabc616d6ee04390e156fe906be6285f2329273c6c7c636f364edbeb5386dde","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"00f52e30f5c96ed755e84972232695e1a38d1d615ec4152fc4f263275f8334d2"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"b28bc6b9-f206-4263-8a90-c387270d618c"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:44:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wp6CChK0/99LvtY5llNJcclE80U7lb+iPbmjfoba1yQ0EIlcl9IZzzQ32PCpy305bSjHF7/MgbGqHCN+0ol2Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:15:01.399885Z"},"content_sha256":"3e1f6c7ab66eafd9d27ad5c0d6e8d6f9fc388ac97b8f1f3ab8117255a353493e","schema_version":"1.0","event_id":"sha256:3e1f6c7ab66eafd9d27ad5c0d6e8d6f9fc388ac97b8f1f3ab8117255a353493e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/bundle.json","state_url":"https://pith.science/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/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":"2026-05-31T22:15:01Z","links":{"resolver":"https://pith.science/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW","bundle":"https://pith.science/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/bundle.json","state":"https://pith.science/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DHTKYIC4KDJ2LWSGONOGUBQUUW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DHTKYIC4KDJ2LWSGONOGUBQUUW","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":"d820c87328a8b4a7f78b3a67516116579d372eaa0abfa28ce00247325bf55687","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35Z","title_canon_sha256":"12c979088a762b89d10ad00d7cd5e4ab3ca3983a15c3a5deb01f832554a3ca07"},"schema_version":"1.0","source":{"id":"2602.01629","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.01629","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"arxiv_version","alias_value":"2602.01629v2","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01629","created_at":"2026-05-18T02:44:31Z"},{"alias_kind":"pith_short_12","alias_value":"DHTKYIC4KDJ2","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"DHTKYIC4KDJ2LWSG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"DHTKYIC4","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:3e1f6c7ab66eafd9d27ad5c0d6e8d6f9fc388ac97b8f1f3ab8117255a353493e","target":"graph","created_at":"2026-05-18T02:44:31Z","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":"AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The adaptive reweighting of nonconformity scores combined with the replay buffer preserves the marginal coverage guarantees of conformal prediction during online transitions under distribution shift."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage."}],"snapshot_sha256":"e4f570b63a146021d9d8397e547ef5058051a927d6733e21e90003daa72051f2"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"00f52e30f5c96ed755e84972232695e1a38d1d615ec4152fc4f263275f8334d2"},"paper":{"abstract_excerpt":"Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient predictio","authors_text":"Aditya Singh, Rahul Mangharam, Renukanandan Tumu","cross_cats":["cs.RO","cs.SY","eess.SY"],"headline":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35Z","title":"AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift"},"references":{"count":16,"internal_anchors":0,"resolved_work":16,"sample":[{"cited_arxiv_id":"","doi":"10.1016/j.jmva.2005","is_internal_anchor":false,"ref_index":1,"title":"doi: 10.1016/j.jmva.2005","work_id":"50ca57c1-e2c3-4818-927c-f0ec18a943bc","year":2005},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Gao, C., Shan, L., Srinivas, V ., and Vijayaragha- van, A","work_id":"b757e598-257e-41a0-b920-95020b3b5023","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"URL https://openreview.net/forum? id=oNDhnGrD51&noteId=7kR09SC5BY. Gibbs, I. and Candes, E. Adaptive Conformal In- ference Under Distribution Shift. InAdvances in Neural Information Processing Systems","work_id":"92fc93d0-606a-4ced-b0c7-05b8f7bafa25","year":2021},{"cited_arxiv_id":"","doi":"10.1103/physreve.51","is_internal_anchor":false,"ref_index":4,"title":"URL http://jmlr.org/ papers/v25/22-1218.html","work_id":"f21483a7-5973-4621-8e28-7cf1941b8a6d","year":1991},{"cited_arxiv_id":"","doi":"10.52202/079017-3158","is_internal_anchor":false,"ref_index":5,"title":"1103/PhysRevE.51.4282","work_id":"c854b72c-6d0d-4ddb-9324-26d799cc2ea3","year":1994}],"snapshot_sha256":"9dabc616d6ee04390e156fe906be6285f2329273c6c7c636f364edbeb5386dde"},"source":{"id":"2602.01629","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T08:04:01.606656Z","id":"b28bc6b9-f206-4263-8a90-c387270d618c","model_set":{"reader":"grok-4.3"},"one_line_summary":"AdaptNC jointly adapts nonconformity scores and thresholds in conformal prediction to shrink prediction region volumes under distribution shifts while preserving target coverage.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AdaptNC adapts both nonconformity scores and thresholds online to shrink prediction regions under distribution shifts while preserving coverage.","strongest_claim":"AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.","weakest_assumption":"The adaptive reweighting of nonconformity scores combined with the replay buffer preserves the marginal coverage guarantees of conformal prediction during online transitions under distribution shift."}},"verdict_id":"b28bc6b9-f206-4263-8a90-c387270d618c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:01fdb0f1cc44f13592de2362bbe072b1850c35231b9a0843881eb315f6c22757","target":"record","created_at":"2026-05-18T02:44:31Z","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":"d820c87328a8b4a7f78b3a67516116579d372eaa0abfa28ce00247325bf55687","cross_cats_sorted":["cs.RO","cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T04:41:35Z","title_canon_sha256":"12c979088a762b89d10ad00d7cd5e4ab3ca3983a15c3a5deb01f832554a3ca07"},"schema_version":"1.0","source":{"id":"2602.01629","kind":"arxiv","version":2}},"canonical_sha256":"19e6ac205c50d3a5da46735c6a0614a5b2eca169b7f25d77cdf324fea1e4662d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"19e6ac205c50d3a5da46735c6a0614a5b2eca169b7f25d77cdf324fea1e4662d","first_computed_at":"2026-05-18T02:44:31.710013Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:31.710013Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v4i+6x9VHu9Qm9v0ehFW9LwmsuKGOPR3F09WGrJpBd63dRzyVsXolGWeTOzCR15dtVGDrQrbN4s1pVmCpZBcBw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:31.710675Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.01629","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01fdb0f1cc44f13592de2362bbe072b1850c35231b9a0843881eb315f6c22757","sha256:3e1f6c7ab66eafd9d27ad5c0d6e8d6f9fc388ac97b8f1f3ab8117255a353493e"],"state_sha256":"05f5431ed3082774118cc84e3b2138f439182efb1fb358274144085be366099d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xlsEtCLcpn7mga2qnFR3ZpxzOBo6AT5Iipv65YcyXqccSWNpuztLWE3MsTl5YzFVayNs8AcXdtuQIbUmlCzeDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:15:01.402425Z","bundle_sha256":"d56349acb98bb12f10cd00d19aca18987c6b7dc3fca0d81461f79144e0f0ce7f"}}