{"as_of":"2026-08-09T23:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1e3c57c3288c1d8eeaaee184112779ee5a3a497f222b338ff0525faf09ce3034","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:26:24.534330Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":25,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"1906.10124","last_updated":"2019-06-25T15:18:10Z","snapshot_observed_at":"2026-08-02T15:08:54.470275Z","submitted_at":"2019-06-25T15:18:10Z","title":"On Multi-Agent Learning in Team Sports Games","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T15:51:28.289206Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/1906.10124"},"observation_digest":"sha256:ac29f439c6c8037c816d5d3f857528c3e398124d285de7aa0d3e5e8d039095eb","observation_id":"a4097592-c063-455f-83ff-3191923f9e96","resolution":{"observed_at":"2026-05-25T15:56:01.077678Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"1910.07113","last_updated":"2019-10-16T00:59:05Z","snapshot_observed_at":"2026-08-02T15:37:37.200292Z","submitted_at":"2019-10-16T00:59:05Z","title":"Solving Rubik's Cube with a Robot Hand","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-15T09:38:28.621842Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/1910.07113"},"observation_digest":"sha256:eb04a5aebd7af12308c2c2914991a70ab993dfaf2cec0ad2298cd28e0d155254","observation_id":"8e3147c5-9ca3-42fa-aacc-1996faea6fb5","resolution":{"observed_at":"2026-05-15T09:38:28.907337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"1910.11215","last_updated":"2020-01-02T06:26:37Z","snapshot_observed_at":"2026-07-06T08:31:59.314616Z","submitted_at":"2019-10-24T15:20:03Z","title":"RoboNet: Large-Scale Multi-Robot Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-17T19:08:29.358286Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/1910.11215"},"observation_digest":"sha256:2e825813c54c6b7cf6aa1278c69adcf4b29bba530b7ed6c0cd047b46e21d7cde","observation_id":"348bc236-78e2-4b9f-93de-51c7271616b6","resolution":{"observed_at":"2026-05-17T19:08:29.432014Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2409.12917","last_updated":"2024-10-04T17:28:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-19T17:16:21Z","title":"Training Language Models to Self-Correct via Reinforcement Learning","version":2},"reference_index":212,"source":"arxiv_source","source_observed_at":"2026-05-17T12:04:10.210508Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2409.12917"},"observation_digest":"sha256:86606c97a675333c379bb7782c4a169b7a810801e909794182ba9f4db2d3d5c1","observation_id":"387c8a6e-6281-4672-b327-d12a6c14add3","resolution":{"observed_at":"2026-05-17T12:04:10.824877Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-06T15:26:24.534330Z","title":"Lynch, M","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2507.16139","last_updated":"2025-07-22T01:13:45Z","snapshot_observed_at":"2026-08-09T17:55:09.044078Z","submitted_at":"2025-07-22T01:13:45Z","title":"Equivariant Goal Conditioned Contrastive Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:26:24.534330Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2507.16139"},"observation_digest":"sha256:9dd4d130cf6b16caefadc904ac684aeec1c82d8617d9206951eb8299691c379f","observation_id":"6ec8fa24-2d93-41f8-8694-34bea6fd4bdc","resolution":{"observed_at":"2026-08-06T15:26:24.534330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T06:03:28.291986Z","title":"Lynch, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.04737","last_updated":"2025-09-05T01:31:07Z","snapshot_observed_at":"2026-08-09T17:22:01.031905Z","submitted_at":"2025-09-05T01:31:07Z","title":"Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T06:03:28.291986Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2509.04737"},"observation_digest":"sha256:6fb238a5c65d1db69f050adf8dbe9c8170ffa48ce49b25f10f5054c3f9c47d77","observation_id":"7792e52e-2bc5-4f19-9bdd-619d899ac62a","resolution":{"observed_at":"2026-08-05T06:03:28.291986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2510.01433","last_updated":"2026-04-15T19:41:35Z","snapshot_observed_at":"2026-07-29T19:10:23.857765Z","submitted_at":"2025-10-01T20:13:39Z","title":"AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-18T10:17:53.226866Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2510.01433"},"observation_digest":"sha256:c75b8f5b2066b5b7259588f3b616e5c010bce64859b0fe8e3f58b2f2817226b9","observation_id":"42fa750e-c398-4751-8eff-3e80550b873a","resolution":{"observed_at":"2026-05-18T10:21:15.248307Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2604.16903","last_updated":"2026-04-18T08:26:48Z","snapshot_observed_at":"2026-07-06T23:04:05.813982Z","submitted_at":"2026-04-18T08:26:48Z","title":"Leveraging VR Robot Games to Facilitate Data Collection for Embodied Intelligence Tasks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T06:40:25.738966Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2604.16903"},"observation_digest":"sha256:843735c46b6483e8092148596a195d73bcc909480ed36c31be83404a4b4f4b1a","observation_id":"7a524f8f-e0e7-4b9c-b4ec-20cc6e96e2d4","resolution":{"observed_at":"2026-05-10T06:41:36.442419Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2604.22551","last_updated":"2026-04-24T13:45:04Z","snapshot_observed_at":"2026-08-03T02:55:51.041095Z","submitted_at":"2026-04-24T13:45:04Z","title":"QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T11:22:38.704101Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2604.22551"},"observation_digest":"sha256:70460a3c62edda8d2f2097e4d687549321aab6f0ee45a8ca317d131a7e648d1d","observation_id":"ed61388d-e061-400d-933f-140103549c6d","resolution":{"observed_at":"2026-05-08T21:49:16.199039Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2605.09364","last_updated":"2026-05-10T06:27:20Z","snapshot_observed_at":"2026-07-31T06:28:20.350355Z","submitted_at":"2026-05-10T06:27:20Z","title":"Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-12T03:35:37.739085Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2605.09364"},"observation_digest":"sha256:1aa6053e6cc030aa809ef6565f54b8a39422d0ccf96cdaf3a486aa06e242fcc2","observation_id":"fca83540-fa73-4f09-b289-1475901db8bf","resolution":{"observed_at":"2026-05-12T07:16:26.090010Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2605.13918","last_updated":"2026-05-13T12:52:35Z","snapshot_observed_at":"2026-08-02T07:48:11.711356Z","submitted_at":"2026-05-13T12:52:35Z","title":"CA2: Code-Aware Agent for Automated Game Testing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T05:53:26.558042Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2605.13918"},"observation_digest":"sha256:0994cdb9730187b8a652d322555323db1da91fd846e65b2ec9668e24d7aa1189","observation_id":"2e91a951-bee8-4625-b952-1f132f12a03b","resolution":{"observed_at":"2026-05-15T05:55:05.015975Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play","version":2},"cited_work":{"arxiv_id":"1903.01973","doi":"10.48550/arxiv.1903.01973","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.01973","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning latent plans from play","venue":"arXiv (Cornell University)","work_id":"e8d5f0a4-16bc-4f57-9219-7110250ece87","year":2019},"citing_paper":{"arxiv_id":"2606.00229","last_updated":"2026-06-08T17:22:58Z","snapshot_observed_at":"2026-07-06T23:40:56.510371Z","submitted_at":"2026-05-29T18:02:09Z","title":"Continuous Reasoning for Vision-Language-Action","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-06-28T22:07:07.401335Z"},"links":{"cited_paper":"/paper/1903.01973","citing_paper":"/paper/2606.00229"},"observation_digest":"sha256:a2b0b8f56400bb855bc5d5e339b60b631bf99aba274301a8aa0cb9a0b753b2ba","observation_id":"a7df777e-7d43-4020-83c7-181734b064ae","resolution":{"observed_at":"2026-07-01T19:46:10.514393Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1903.01973/citation-record","integrity":"/paper/1903.01973/integrity","json":"/paper/1903.01973/citation-record.json","paper":"/paper/1903.01973"},"outbound":[],"paper":{"arxiv_id":"1903.01973","last_updated":"2019-12-20T05:03:10Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-08T18:38:25.614821Z","submitted_at":"2019-03-05T18:36:42Z","title":"Learning Latent Plans from Play"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1903.01973."}