{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HHXHJWETH6IT2SKG6S5VGCSQGB","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":"3a764a182382865a87a7a5af9d4e373724305aa13e577c466def17654cf7e8d8","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T20:46:20Z","title_canon_sha256":"e0e5f5f78a2d476479cfd7a74c28b74eb76bbc8fe752ecf71cfbd02a3410f80c"},"schema_version":"1.0","source":{"id":"2605.17149","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17149","created_at":"2026-05-20T00:03:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17149v1","created_at":"2026-05-20T00:03:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17149","created_at":"2026-05-20T00:03:42Z"},{"alias_kind":"pith_short_12","alias_value":"HHXHJWETH6IT","created_at":"2026-05-20T00:03:42Z"},{"alias_kind":"pith_short_16","alias_value":"HHXHJWETH6IT2SKG","created_at":"2026-05-20T00:03:42Z"},{"alias_kind":"pith_short_8","alias_value":"HHXHJWET","created_at":"2026-05-20T00:03:42Z"}],"graph_snapshots":[{"event_id":"sha256:27e6d7ba227d64ae815785be4be335b6cca33113d5fa36aed912bf1a34ba3779","target":"graph","created_at":"2026-05-20T00:03:42Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T22:33:23.766862Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T22:01:58.000354Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.17149/integrity.json","findings":[],"snapshot_sha256":"9857cd6c3099e2e0bcb1b255bf4cdbccf11715a99f595310ddd58784c6c2f8a3","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a QPLEX Decision Process (QDP) as a model for dynamic control of queueing systems with non-stationary arrivals, general service distributions, and service-level chance constraints. QDPs integrate QPLEX, a computational modeling methodology for transient analysis of stochastic systems, into a nonlinear Markov decision framework. Since QPLEX approximations use nonlinear transition probabilities with orders-of-magnitude smaller state spaces, QDPs circumvent the curse of dimensionality associated with general service times. Via forward and backward iterative schemes, we can rapidly co","authors_text":"Antonius B. Dieker, Steven T. Hackman, Yunhao Yan, Zitong Wang","cross_cats":["math.PR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T20:46:20Z","title":"QPLEX Decision Processes: Formulation via Nonlinear Markov Chains and Optimization via Policy Gradients"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17149","kind":"arxiv","version":1},"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":"sha256:7c9f1f0f71ce7de9955ea4042e1159e74e736dd54cd6524f278576c56eacfbf2","target":"record","created_at":"2026-05-20T00:03:42Z","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":"3a764a182382865a87a7a5af9d4e373724305aa13e577c466def17654cf7e8d8","cross_cats_sorted":["math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T20:46:20Z","title_canon_sha256":"e0e5f5f78a2d476479cfd7a74c28b74eb76bbc8fe752ecf71cfbd02a3410f80c"},"schema_version":"1.0","source":{"id":"2605.17149","kind":"arxiv","version":1}},"canonical_sha256":"39ee74d8933f913d4946f4bb530a50306340e3c5ef96fbe415e0fbfd4bcf7c1c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39ee74d8933f913d4946f4bb530a50306340e3c5ef96fbe415e0fbfd4bcf7c1c","first_computed_at":"2026-05-20T00:03:42.222412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:42.222412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nTupJOgRWi9QIGjpKbY9I+2cLe4K79znuCEcTaeNWnlA8P1LEKkdkyYNSQowkHd2PXXfAUEPXDVOQInphBgCBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:42.223217Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17149","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c9f1f0f71ce7de9955ea4042e1159e74e736dd54cd6524f278576c56eacfbf2","sha256:27e6d7ba227d64ae815785be4be335b6cca33113d5fa36aed912bf1a34ba3779"],"state_sha256":"34e8e00071778985937b51348edaa92ceade67c372bacaec07991c6fc9181cb1"}