{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MPRZQYZXJNKLLLSCAKGHMMSUY3","short_pith_number":"pith:MPRZQYZX","canonical_record":{"source":{"id":"2605.15745","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"aadff434765507267b497901f79fea1a3d354232818fcb2bac45ea58dccea8ea","abstract_canon_sha256":"570070d40ed5ca67abea3b10917d803db061a8971f7c96cc33ff538de15f1b6d"},"schema_version":"1.0"},"canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","source":{"kind":"arxiv","id":"2605.15745","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15745","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15745v1","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15745","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_12","alias_value":"MPRZQYZXJNKL","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_16","alias_value":"MPRZQYZXJNKLLLSC","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_8","alias_value":"MPRZQYZX","created_at":"2026-05-20T00:01:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MPRZQYZXJNKLLLSCAKGHMMSUY3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15745","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","cross_cats_sorted":["cs.CC"],"title_canon_sha256":"aadff434765507267b497901f79fea1a3d354232818fcb2bac45ea58dccea8ea","abstract_canon_sha256":"570070d40ed5ca67abea3b10917d803db061a8971f7c96cc33ff538de15f1b6d"},"schema_version":"1.0"},"canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:15.973730Z","signature_b64":"HbDxTjE9wzYDeuq6fFZMpxe1tNG+9aI7rd3GvnJlo7Eu/17m2i9AGPRoXnulnNVPjyeCbZb1unEXXull/Ej9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","last_reissued_at":"2026-05-20T00:01:15.972915Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:15.972915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15745","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5rBrZWPyv97udn2zNQHvb3bbZS7Ok2aHNorKm+ZGyM6ETZy7NbncwPY4zTJE2A+R7RWNrPK/WfUick6XMbP4Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T20:26:19.866402Z"},"content_sha256":"67fee78bbf8129ec0959377dede5201a10794f11e126affa3c7cc2975edf4eec","schema_version":"1.0","event_id":"sha256:67fee78bbf8129ec0959377dede5201a10794f11e126affa3c7cc2975edf4eec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MPRZQYZXJNKLLLSCAKGHMMSUY3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Robotaxi Placement Problem: Minimizing Expected ETA for Stochastic Demand","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","cross_cats":["cs.CC"],"primary_cat":"cs.DS","authors_text":"Aaron Schild, Ali Kemal Sinop, Ioannis Caragiannis, Kostas Kollias, Mohammad Roghani","submitted_at":"2026-05-15T08:54:32Z","abstract_excerpt":"Autonomous ride-hailing platforms must strategically position idle robotaxis to minimize the wait times of prospective riders. We formalize this as the \\emph{robotaxi placement problem} ($k$-RP). Given a finite metric space and a demand distribution over its points, the goal is to position $k$ robotaxis to minimize the expected total distance in a perfect matching between the robotaxis and $k$ random riders. We present several theoretical results for this stochastic optimization problem. First, we observe that sampling robotaxi locations independently according to the demand distribution yield"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2119b5e9acbddf25b4b6df4910a7654a79e460fd81cc08b98ae105574c254af3"},"source":{"id":"2605.15745","kind":"arxiv","version":1},"verdict":{"id":"02651422-421f-4ae2-8b4c-dcf52e083aa6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:30:33.624106Z","strongest_claim":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem.","one_line_summary":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation).","pith_extraction_headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15745/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.192404Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:40:58.375059Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.890640Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.975839Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ab0b039cd300c7472366ebd291ec8ae05a55373d678123324fcc2898d7b563ac"},"references":{"count":29,"sample":[{"doi":"","year":1984,"title":"Optimal matchings of random points.Combi- natorica, 4(4):259–264, 1984","work_id":"89ff4134-811c-45dc-9114-4013051ed5d8","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.Proceedings of the National Academy of Sciences, 114(3):462–467, 2017","work_id":"fdd137b1-6496-40d4-adc4-61ed705ba36e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1988,"title":"Bertsimas.Probabilistic combinatorial optimization problems","work_id":"1095217e-12ad-4792-a080-33f2c4d56525","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1990,"title":"Bertsimas, Patrick Jaillet, and Amedeo R","work_id":"f7c63399-415d-4d2f-82b9-24dc2d181f99","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Empty-car routing in ridesharing systems.Operations Research, 67(5):1437–1452, 2019","work_id":"5c0f40f6-5653-43d8-8a1a-d02508e0202b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":29,"snapshot_sha256":"1d1c509886e47006274ce556bb74fc4f28c6064dea11f403547ebfb58e8b7d90","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d6f994c907d988ae98ed9b237fd488418df78e45733ed225ba53eb7249fc1ac4"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"02651422-421f-4ae2-8b4c-dcf52e083aa6"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"naKyxvBP1k0FcFb3k4GqqUJa1qBdW7U218TGGW3DMIkDgp+QXiO5h8pcvYNWUd+GoDInyE4YaY+HgBrv1YtQBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T20:26:19.867153Z"},"content_sha256":"6d19e563787f4f598b4ec10ea0b5c4dd3ac330c2fa9dd50592f3386fa6569c14","schema_version":"1.0","event_id":"sha256:6d19e563787f4f598b4ec10ea0b5c4dd3ac330c2fa9dd50592f3386fa6569c14"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/bundle.json","state_url":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-23T20:26:19Z","links":{"resolver":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3","bundle":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/bundle.json","state":"https://pith.science/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MPRZQYZXJNKLLLSCAKGHMMSUY3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MPRZQYZXJNKLLLSCAKGHMMSUY3","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":"570070d40ed5ca67abea3b10917d803db061a8971f7c96cc33ff538de15f1b6d","cross_cats_sorted":["cs.CC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","title_canon_sha256":"aadff434765507267b497901f79fea1a3d354232818fcb2bac45ea58dccea8ea"},"schema_version":"1.0","source":{"id":"2605.15745","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15745","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15745v1","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15745","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_12","alias_value":"MPRZQYZXJNKL","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_16","alias_value":"MPRZQYZXJNKLLLSC","created_at":"2026-05-20T00:01:15Z"},{"alias_kind":"pith_short_8","alias_value":"MPRZQYZX","created_at":"2026-05-20T00:01:15Z"}],"graph_snapshots":[{"event_id":"sha256:6d19e563787f4f598b4ec10ea0b5c4dd3ac330c2fa9dd50592f3386fa6569c14","target":"graph","created_at":"2026-05-20T00:01:15Z","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":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times."}],"snapshot_sha256":"2119b5e9acbddf25b4b6df4910a7654a79e460fd81cc08b98ae105574c254af3"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d6f994c907d988ae98ed9b237fd488418df78e45733ed225ba53eb7249fc1ac4"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:01:19.192404Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T19:40:58.375059Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.890640Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:21:55.975839Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15745/integrity.json","findings":[],"snapshot_sha256":"ab0b039cd300c7472366ebd291ec8ae05a55373d678123324fcc2898d7b563ac","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Autonomous ride-hailing platforms must strategically position idle robotaxis to minimize the wait times of prospective riders. We formalize this as the \\emph{robotaxi placement problem} ($k$-RP). Given a finite metric space and a demand distribution over its points, the goal is to position $k$ robotaxis to minimize the expected total distance in a perfect matching between the robotaxis and $k$ random riders. We present several theoretical results for this stochastic optimization problem. First, we observe that sampling robotaxi locations independently according to the demand distribution yield","authors_text":"Aaron Schild, Ali Kemal Sinop, Ioannis Caragiannis, Kostas Kollias, Mohammad Roghani","cross_cats":["cs.CC"],"headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","title":"The Robotaxi Placement Problem: Minimizing Expected ETA for Stochastic Demand"},"references":{"count":29,"internal_anchors":0,"resolved_work":29,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Optimal matchings of random points.Combi- natorica, 4(4):259–264, 1984","work_id":"89ff4134-811c-45dc-9114-4013051ed5d8","year":1984},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment.Proceedings of the National Academy of Sciences, 114(3):462–467, 2017","work_id":"fdd137b1-6496-40d4-adc4-61ed705ba36e","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Bertsimas.Probabilistic combinatorial optimization problems","work_id":"1095217e-12ad-4792-a080-33f2c4d56525","year":1988},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Bertsimas, Patrick Jaillet, and Amedeo R","work_id":"f7c63399-415d-4d2f-82b9-24dc2d181f99","year":1990},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Empty-car routing in ridesharing systems.Operations Research, 67(5):1437–1452, 2019","work_id":"5c0f40f6-5653-43d8-8a1a-d02508e0202b","year":2019}],"snapshot_sha256":"1d1c509886e47006274ce556bb74fc4f28c6064dea11f403547ebfb58e8b7d90"},"source":{"id":"2605.15745","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T19:30:33.624106Z","id":"02651422-421f-4ae2-8b4c-dcf52e083aa6","model_set":{"reader":"grok-4.3"},"one_line_summary":"Introduces the k-robotaxi placement problem on metric spaces, gives a randomized 2-approximation by independent sampling from demand, proves inapproximability via max-coverage reduction, provides exact DP on trees, and shows variance-reduced random placement works well empirically.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Sampling robotaxi locations from the demand distribution provides a randomized 2-approximation for minimizing expected rider wait times.","strongest_claim":"Sampling robotaxi locations independently according to the demand distribution yields a randomized 2-approximation algorithm for the robotaxi placement problem.","weakest_assumption":"The demand distribution over rider locations is known in advance and can be sampled from independently for each of the k riders (section on randomized algorithm and empirical evaluation)."}},"verdict_id":"02651422-421f-4ae2-8b4c-dcf52e083aa6"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:67fee78bbf8129ec0959377dede5201a10794f11e126affa3c7cc2975edf4eec","target":"record","created_at":"2026-05-20T00:01:15Z","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":"570070d40ed5ca67abea3b10917d803db061a8971f7c96cc33ff538de15f1b6d","cross_cats_sorted":["cs.CC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2026-05-15T08:54:32Z","title_canon_sha256":"aadff434765507267b497901f79fea1a3d354232818fcb2bac45ea58dccea8ea"},"schema_version":"1.0","source":{"id":"2605.15745","kind":"arxiv","version":1}},"canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63e39863374b54b5ae42028c763254c6e10e5f594d78f5949614c4b724b2b4f8","first_computed_at":"2026-05-20T00:01:15.972915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:15.972915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HbDxTjE9wzYDeuq6fFZMpxe1tNG+9aI7rd3GvnJlo7Eu/17m2i9AGPRoXnulnNVPjyeCbZb1unEXXull/Ej9Dg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:15.973730Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15745","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67fee78bbf8129ec0959377dede5201a10794f11e126affa3c7cc2975edf4eec","sha256:6d19e563787f4f598b4ec10ea0b5c4dd3ac330c2fa9dd50592f3386fa6569c14"],"state_sha256":"ae14e66aeb5bc12c303b7a6abf5546376338d2405c5c882275df72916998ff0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vZY/BtXcSNemm63OMlHQ4cMAKVc/WolPdG8CynU91xtDvhX+VXQirPz1dWJYs90DWf5ygKi7EphPzv5SOw8MAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T20:26:19.870821Z","bundle_sha256":"c19106daa93beac0ebcb497079b7eaa53572a15c439f298a3d141a00ca4d4d1d"}}