{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JOIDNAFSVHBD7BADVRWH75TZIV","short_pith_number":"pith:JOIDNAFS","schema_version":"1.0","canonical_sha256":"4b903680b2a9c23f8403ac6c7ff6794559e924842a102d379196fcb4717b86c8","source":{"kind":"arxiv","id":"2605.09273","version":2},"attestation_state":"computed","paper":{"title":"Instance-Adaptive Online Multicalibration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A single efficient algorithm achieves online multicalibration with error rates that automatically adapt to the complexity of the data sequence.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aaron Roth, Claire Jie Zhang, Jamie Morgenstern, Zhiming Huang","submitted_at":"2026-05-10T02:45:59Z","abstract_excerpt":"We study online multicalibration beyond the worst-case. We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. Our analysis recovers the known $\\widetilde O(T^{2/3})$ worst-case-optimal rate for online multicalibration, while simultaneously automatically adapting to easier instances: in the marginal stochastic setting it obtains a rate of $\\widetilde O(\\sqrt T)$, and for piecewise-stationary means with $J$ segme"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.09273","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-10T02:45:59Z","cross_cats_sorted":[],"title_canon_sha256":"6978a7a0611978c68b9d78abd19c794f1e23e7d6b15b362a1b2392bf72371dcf","abstract_canon_sha256":"4b1fa55881a995e83a78a7369943902a1f06e2c5bc30f240d33b9c1a5fc768f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:05.799716Z","signature_b64":"vDFAHZMUSpWsFhOWYPWpn7KiW28xn2WYoJowY8fsp3gKeMM+TdX/GjgXz18I41zd9J/2qX6j06ByCM52kFWbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b903680b2a9c23f8403ac6c7ff6794559e924842a102d379196fcb4717b86c8","last_reissued_at":"2026-05-22T01:04:05.798767Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:05.798767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instance-Adaptive Online Multicalibration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A single efficient algorithm achieves online multicalibration with error rates that automatically adapt to the complexity of the data sequence.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aaron Roth, Claire Jie Zhang, Jamie Morgenstern, Zhiming Huang","submitted_at":"2026-05-10T02:45:59Z","abstract_excerpt":"We study online multicalibration beyond the worst-case. We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. Our analysis recovers the known $\\widetilde O(T^{2/3})$ worst-case-optimal rate for online multicalibration, while simultaneously automatically adapting to easier instances: in the marginal stochastic setting it obtains a rate of $\\widetilde O(\\sqrt T)$, and for piecewise-stationary means with $J$ segme"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. ... the rate depends on a threshold-complexity measure of the predictable mean process relative to the group family. We show that this dependence is tight up to logarithmic factors.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The analysis assumes that the threshold-complexity measure of the predictable mean process (relative to the given group family) is well-defined and that the algorithm can observe enough information to decide when to refine the dyadic grid without additional side information.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A single efficient algorithm achieves online multicalibration with error rates that automatically adapt to the complexity of the data sequence.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7e50624eeae355e9a114ab44314402bd943ed7a13d27665a83a4cce2e8c20e1d"},"source":{"id":"2605.09273","kind":"arxiv","version":2},"verdict":{"id":"0283a81d-f593-4251-8a3b-1706394b515e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T04:39:51.056389Z","strongest_claim":"We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. ... the rate depends on a threshold-complexity measure of the predictable mean process relative to the group family. We show that this dependence is tight up to logarithmic factors.","one_line_summary":"A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The analysis assumes that the threshold-complexity measure of the predictable mean process (relative to the given group family) is well-defined and that the algorithm can observe enough information to decide when to refine the dyadic grid without additional side information.","pith_extraction_headline":"A single efficient algorithm achieves online multicalibration with error rates that automatically adapt to the complexity of the data sequence."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.09273/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T08:02:08.914613Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T20:34:28.985734Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T13:31:17.808759Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T10:22:16.618251Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"63a0e1f4c96243ebbcc85ade7f4d5204a5985f254a6c502c15069f71c6371b0d"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.09273","created_at":"2026-05-22T01:04:05.798935+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.09273v2","created_at":"2026-05-22T01:04:05.798935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.09273","created_at":"2026-05-22T01:04:05.798935+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOIDNAFSVHBD","created_at":"2026-05-22T01:04:05.798935+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOIDNAFSVHBD7BAD","created_at":"2026-05-22T01:04:05.798935+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOIDNAFS","created_at":"2026-05-22T01:04:05.798935+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.11490","citing_title":"Adaptive Calibration in Non-Stationary Environments","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV","json":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV.json","graph_json":"https://pith.science/api/pith-number/JOIDNAFSVHBD7BADVRWH75TZIV/graph.json","events_json":"https://pith.science/api/pith-number/JOIDNAFSVHBD7BADVRWH75TZIV/events.json","paper":"https://pith.science/paper/JOIDNAFS"},"agent_actions":{"view_html":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV","download_json":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV.json","view_paper":"https://pith.science/paper/JOIDNAFS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.09273&json=true","fetch_graph":"https://pith.science/api/pith-number/JOIDNAFSVHBD7BADVRWH75TZIV/graph.json","fetch_events":"https://pith.science/api/pith-number/JOIDNAFSVHBD7BADVRWH75TZIV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV/action/storage_attestation","attest_author":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV/action/author_attestation","sign_citation":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV/action/citation_signature","submit_replication":"https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV/action/replication_record"}},"created_at":"2026-05-22T01:04:05.798935+00:00","updated_at":"2026-05-22T01:04:05.798935+00:00"}