{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TDN3ASGWEAEV4DRHKPLG7JW36H","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":"b86cc1617bf7f2dc2a31a1a614b162d674c0bfbc553e5a26f0394c3958235a40","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-05T06:25:52Z","title_canon_sha256":"adf1c18d83ed7be9b921b162b48cac2988fe566873e4061c4c45289134cd20e9"},"schema_version":"1.0","source":{"id":"2605.03406","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.03406","created_at":"2026-05-20T00:01:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.03406v2","created_at":"2026-05-20T00:01:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.03406","created_at":"2026-05-20T00:01:42Z"},{"alias_kind":"pith_short_12","alias_value":"TDN3ASGWEAEV","created_at":"2026-05-20T00:01:42Z"},{"alias_kind":"pith_short_16","alias_value":"TDN3ASGWEAEV4DRH","created_at":"2026-05-20T00:01:42Z"},{"alias_kind":"pith_short_8","alias_value":"TDN3ASGW","created_at":"2026-05-20T00:01:42Z"}],"graph_snapshots":[{"event_id":"sha256:5556595de932c34b6dba94c91c96ad618814aa6632ac817c60adb7d6c1c27f18","target":"graph","created_at":"2026-05-20T00:01:42Z","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":"We use a sample average approximation combined with mixed integer linear programming (S-MILP) approach for this problem and show how our S-MILP approach dominates classical GST procedures such as Lan-DeMets, Pocock, and O'Brien-Fleming methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The sample average approximation provides a sufficiently accurate representation of the true type-1 and type-2 error probabilities for the optimized boundaries to maintain the desired error control in practice."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Mixed-integer linear programming finds optimal rejection boundaries for group sequential tests that allow earlier stopping than standard methods."}],"snapshot_sha256":"ca32b4c015ddb2a98f96cfdca2e3c1693765024ee73bc7113de1b1910f918073"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8e80ffc10d579b63c9887bd008bbc7bfb9a518b4059c4fc1a9a9922d100544d3"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T15:26:23.807489Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.03406/integrity.json","findings":[],"snapshot_sha256":"ab30930f3262d499199f9a07bb6fb13240d953934136ec317b67c2097cc3e9a5","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sequential hypothesis tests are widely adopted as a principled way to perform multiple tests on data that arrives over time. In particular, researchers frequently utilize group sequential hypothesis tests (GST) to test the same hypotheses at K times or \"groups\" while data arrives sequentially. In this setting, many methods have been proposed to allow researchers to uniformly control type-1 error across K checks (often known as various alpha-spending budgets). Although these methods are all successfully valid in controlling uniform type-1 error, it is not clear which of these methods are optima","authors_text":"Dae Woong Ham, Stefanus Jasin, Xuejun Zhao","cross_cats":[],"headline":"Mixed-integer linear programming finds optimal rejection boundaries for group sequential tests that allow earlier stopping than standard methods.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-05T06:25:52Z","title":"A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming"},"references":{"count":179,"internal_anchors":1,"resolved_work":179,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Eales, J. D. and Jennison, C. , title =. Biometrika , volume =. 1992 , doi =","work_id":"ad1d664f-b0b6-458b-ac37-9771d493fdae","year":1992},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Hampson, L. V. and Jennison, C. , title =. Journal of the Royal Statistical Society, Series B , volume =. 2013 , doi =","work_id":"47a09662-fbaa-41da-9c27-e7327e91173b","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Lectures on stochastic programming: modeling and theory , author=. 2021 , publisher=","work_id":"e4ef80c4-907f-415d-952c-42ead03a2c08","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Introduction to sample size determination and power analysis for clinical trials. , author=. Controlled clinical trials , year=","work_id":"8c71a3f8-6cab-4459-a2ae-15078ab942b7","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Cohen, J. , biburl =","work_id":"3850d5e9-63b9-4f25-9838-0e46793c1502","year":null}],"snapshot_sha256":"2a8d99a912b3e4d642b1abb65706975e7efcc563de7e5eec3ad8525d2b6d94e0"},"source":{"id":"2605.03406","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-19T18:12:45.296775Z","id":"e7410fb2-c40e-4bba-9545-49fe7e082953","model_set":{"reader":"grok-4.3"},"one_line_summary":"The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Mixed-integer linear programming finds optimal rejection boundaries for group sequential tests that allow earlier stopping than standard methods.","strongest_claim":"We use a sample average approximation combined with mixed integer linear programming (S-MILP) approach for this problem and show how our S-MILP approach dominates classical GST procedures such as Lan-DeMets, Pocock, and O'Brien-Fleming methods.","weakest_assumption":"The sample average approximation provides a sufficiently accurate representation of the true type-1 and type-2 error probabilities for the optimized boundaries to maintain the desired error control in practice."}},"verdict_id":"e7410fb2-c40e-4bba-9545-49fe7e082953"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:28e95cbaf02d9beebdabf18bf1281316db6ab3abd8ff47369c3214783dbefebe","target":"record","created_at":"2026-05-20T00:01:42Z","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":"b86cc1617bf7f2dc2a31a1a614b162d674c0bfbc553e5a26f0394c3958235a40","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-05T06:25:52Z","title_canon_sha256":"adf1c18d83ed7be9b921b162b48cac2988fe566873e4061c4c45289134cd20e9"},"schema_version":"1.0","source":{"id":"2605.03406","kind":"arxiv","version":2}},"canonical_sha256":"98dbb048d620095e0e2753d66fa6dbf1c1692ec6eae16f072930b031cad75422","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"98dbb048d620095e0e2753d66fa6dbf1c1692ec6eae16f072930b031cad75422","first_computed_at":"2026-05-20T00:01:42.613524Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:42.613524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DRVouWLYjUnCRfuKFJ7KgycuETgdPezozo138qZWY9iAHpxIFB+KXZXTwmXXmRbKGiH0iKaRRaRqh2KRrmmFCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:42.614127Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.03406","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28e95cbaf02d9beebdabf18bf1281316db6ab3abd8ff47369c3214783dbefebe","sha256:5556595de932c34b6dba94c91c96ad618814aa6632ac817c60adb7d6c1c27f18"],"state_sha256":"9c5c5d9f83ea9fbd332dd78130b4c26c11a036896f28032215b2c369010e6c46"}