{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QGVTMAHYP3MXATSHC2E7D4YDU3","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":"64e126a60d59bc90f2eb3b0d1742435bb7d8822c6aaacbd5db63e972f7d05248","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-06-05T04:28:31Z","title_canon_sha256":"8987e3f34694f7fcdd782d4023394b833dfa11643444f73da9b9de15dda82ade"},"schema_version":"1.0","source":{"id":"2606.06896","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.06896","created_at":"2026-06-08T01:04:34Z"},{"alias_kind":"arxiv_version","alias_value":"2606.06896v1","created_at":"2026-06-08T01:04:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06896","created_at":"2026-06-08T01:04:34Z"},{"alias_kind":"pith_short_12","alias_value":"QGVTMAHYP3MX","created_at":"2026-06-08T01:04:34Z"},{"alias_kind":"pith_short_16","alias_value":"QGVTMAHYP3MXATSH","created_at":"2026-06-08T01:04:34Z"},{"alias_kind":"pith_short_8","alias_value":"QGVTMAHY","created_at":"2026-06-08T01:04:34Z"}],"graph_snapshots":[{"event_id":"sha256:dcdb10a0bf17c58e04b998c913851861336f1a9a4324081fdad012303043b4dd","target":"graph","created_at":"2026-06-08T01:04:34Z","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":[],"endpoint":"/pith/2606.06896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a successive convexification framework for trajectory optimization under continuous-time Signal Temporal Logic (CT-STL) specifications. The framework employs generalized mean-based robustness (GMSR), a smooth and exact parameterization of discrete-time STL, as a logical building block for constructing differentiable CT-STL constraints in optimal control. It is integrated with time-dilation for free-final-time problems, finite-dimensional control parameterization, multiple-shooting discretization of the dynamics, and a convergence-guaranteed sequential convex programming met","authors_text":"Beh\\c{c}et A\\c{c}{\\i}kme\\c{s}e, Samet Uzun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-06-05T04:28:31Z","title":"Successive Convexification for Trajectory Optimization with Continuous-time Satisfaction of Signal Temporal Logic Specifications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06896","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:d59fa0de36a6ce2ffac3056747347088879f94a2aeaeb79e86f647392eeb8324","target":"record","created_at":"2026-06-08T01:04:34Z","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":"64e126a60d59bc90f2eb3b0d1742435bb7d8822c6aaacbd5db63e972f7d05248","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-06-05T04:28:31Z","title_canon_sha256":"8987e3f34694f7fcdd782d4023394b833dfa11643444f73da9b9de15dda82ade"},"schema_version":"1.0","source":{"id":"2606.06896","kind":"arxiv","version":1}},"canonical_sha256":"81ab3600f87ed9704e471689f1f303a6e8e5dfc880cb3ecf0b58d4d7eb272189","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81ab3600f87ed9704e471689f1f303a6e8e5dfc880cb3ecf0b58d4d7eb272189","first_computed_at":"2026-06-08T01:04:34.474347Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:34.474347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FygoDMn9GzNP8jG5WVNTi7BTBe9vbe4E2LTMB5MVK3jfgtr/M9My1+rhQtMGv7tgWmwoUgRY4VHc1ap8eZfjCA==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:34.475182Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.06896","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d59fa0de36a6ce2ffac3056747347088879f94a2aeaeb79e86f647392eeb8324","sha256:dcdb10a0bf17c58e04b998c913851861336f1a9a4324081fdad012303043b4dd"],"state_sha256":"6646654827c9955a899cfbe35e3658b2d7bc6092fda9480d10fa2bb4eef7f62c"}