{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TTB3YLVAY4ZSSDGTS46KEL5UDF","merge_version":"pith-open-graph-merge-v1","event_count":10,"valid_event_count":10,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9387d2d3227c92dbad32028026b6792e5f00341c754fc002a3a8a66aa7774aeb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T12:37:27Z","title_canon_sha256":"fd9e659bbbeb522ffa1d407f8a2c329e4e67b39792678f0a5354fb23e34728ce"},"schema_version":"1.0","source":{"id":"2605.13442","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13442","created_at":"2026-05-18T02:44:42Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13442v1","created_at":"2026-05-18T02:44:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13442","created_at":"2026-05-18T02:44:42Z"},{"alias_kind":"pith_short_12","alias_value":"TTB3YLVAY4ZS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"TTB3YLVAY4ZSSDGT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"TTB3YLVA","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:c8909968e678f4b49b8588cbb5348a0901ca8d38f3c3d45d19d3a494990c2f1c","target":"graph","created_at":"2026-05-18T02:44:42Z","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 show that this approach preserves ergodic coverage guarantees in ambient flows and enables effective exploration in under-actuated, and even open-loop planning settings by integrating environment dynamics."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the flow fields and domain evolution are known or accurately modeled in advance so the adaptive MMD objective can be computed and optimized without feedback."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A flow-adaptive MMD formulation preserves ergodic coverage guarantees in time-varying domains and supports under-actuated open-loop robot planning."}],"snapshot_sha256":"d05397cf3d54e45f893aa4155a8416b1efc21dfa14bbd54412b3f391465baa7a"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Autonomous robotic exploration in remote and extreme environments allows scientists to model complex transport phenomena and collective behaviors described by continuously deforming flow fields. Although these environments are naturally modeled as time-varying domains, most adaptive exploration methods assume static environments and fail to provide adequate coverage or satisfy any formal guarantees. This is especially the case in oceanography where autonomous underwater systems (UxS) have highly restrictive compute and payload requirements that necessitate path planning methods that yield robu","authors_text":"Christian Hughes, Darrick Lee, Fabio Ramos, Houston Warren, Ian Abraham, Julia Engdahl, Travis Miles, Yanis Lahrach, Yilang Liu","cross_cats":[],"headline":"A flow-adaptive MMD formulation preserves ergodic coverage guarantees in time-varying domains and supports under-actuated open-loop robot planning.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T12:37:27Z","title":"Asymptotically Optimal Ergodic Coverage on Generalized Motion Fields"},"references":{"count":49,"internal_anchors":0,"resolved_work":49,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Ian Abraham, Ahalya Prabhakar, and Todd D. Mur- phey. Active area coverage from equilibrium. In Marco Morales, Lydia Tapia, Gildardo S ´anchez-Ante, and Seth Hutchinson, editors,Algorithmic Foundation","work_id":"3a22ee43-aa83-46b6-9ef1-a63ab79ac12e","year":2020},{"cited_arxiv_id":"","doi":"10.1016/j.robot.2009.11","is_internal_anchor":false,"ref_index":2,"title":"An information-based exploration strategy for environ- ment mapping with mobile robots.Robotics and Autonomous Systems, 58(5):684–699, 2010","work_id":"325cc899-6ed3-4351-a7e1-276414a024e4","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"URL https://www.sciencedirect.com/science/article/ pii/S0921889009002024","work_id":"d1716ccb-3ceb-46a9-90d8-e9f67cf54d3d","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Information based adaptive robotic exploration","work_id":"9e6163d9-ec16-49d7-a43e-d61aa8df0407","year":2002},{"cited_arxiv_id":"","doi":"10.1007/978-981-15-9460-1","is_internal_anchor":false,"ref_index":5,"title":"Rik B ¨ahnemann, Nicholas Lawrance, Jen Jen Chung, Michael Pantic, Roland Siegwart, and Juan Nieto.Re- visiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem, page 2","work_id":"c7ee1f72-8d20-41b4-ba22-04a6c297e22c","year":2021}],"snapshot_sha256":"6019c33ef766ad321d9928ad0d602328e6360b91071de193bb43bdcdc43fc226"},"source":{"id":"2605.13442","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T19:07:01.809147Z","id":"3dd3080b-4e82-4d2f-8a47-64fe9b2b2f5b","model_set":{"reader":"grok-4.3"},"one_line_summary":"A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A flow-adaptive MMD formulation preserves ergodic coverage guarantees in time-varying domains and supports under-actuated open-loop robot planning.","strongest_claim":"We show that this approach preserves ergodic coverage guarantees in ambient flows and enables effective exploration in under-actuated, and even open-loop planning settings by integrating environment dynamics.","weakest_assumption":"That the flow fields and domain evolution are known or accurately modeled in advance so the adaptive MMD objective can be computed and optimized without feedback."}},"verdict_id":"3dd3080b-4e82-4d2f-8a47-64fe9b2b2f5b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4c471aa1ee921c360d250ba83ba0ef3269c7e1c9bb51e7d748e0422a1939a946","target":"record","created_at":"2026-05-18T02:44:42Z","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":"9387d2d3227c92dbad32028026b6792e5f00341c754fc002a3a8a66aa7774aeb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-13T12:37:27Z","title_canon_sha256":"fd9e659bbbeb522ffa1d407f8a2c329e4e67b39792678f0a5354fb23e34728ce"},"schema_version":"1.0","source":{"id":"2605.13442","kind":"arxiv","version":1}},"canonical_sha256":"9cc3bc2ea0c733290cd3973ca22fb41943256940ea4ded70789d4d3c8277fa88","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9cc3bc2ea0c733290cd3973ca22fb41943256940ea4ded70789d4d3c8277fa88","first_computed_at":"2026-05-18T02:44:42.077435Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:42.077435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8xpQkOqSigeBez4WEeTuHqVRsdL1AfFe9olyHgrSaMPu03NKshVaQHOhHbWX2HKNBWFAV7r1emqEr2hPK4HfAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:42.077894Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13442","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:0389838e9b355a935b0fa8f83986b11f8e766f7cb73e011f9c7ee7a15beaaab1","sha256:1b4d3a7d0fe31c3c820fb271492131cf1f0ae9aa0c39b29ad0d806992b9c757c","sha256:2c1365f3c08a21274bb2f936e373bbfe345bb5bfd136cba97afb6fdcdcd4744c","sha256:79666b97c33e7d10aee5f46c3ddaa8bdf4f127e529452075f2e579b0a75e9768","sha256:87daff768e714f31a35de498367f5d83ce19173f96e5394fa8ed623eeab366bf","sha256:d0da03898bad88fb0db7e99143c9e1b8b8a8d16ce525d3d3839b10ddbcc649d7","sha256:e3704a01add237b38b238c0d887daacc09afd7dc464382875f915c67feb4f1ba","sha256:f991ee3f2ee43c68d2a8870f225b9ce4764301339c8f31b2d07a1e16bc55e154"]}],"invalid_events":[],"applied_event_ids":["sha256:4c471aa1ee921c360d250ba83ba0ef3269c7e1c9bb51e7d748e0422a1939a946","sha256:c8909968e678f4b49b8588cbb5348a0901ca8d38f3c3d45d19d3a494990c2f1c"],"state_sha256":"7c70d7b1d8668d6db48ec56c87119e6bd46294686a0f6084f09ae7ea6c062a21"}