{"as_of":"2026-08-07T18:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3c48676d9f203d059a8843f8d1d7ff93b3c0f41ccef3bea5a3ed7004b9768264","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T15:43:21.902600Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T20:47:22.766708Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-07T15:43:21.902600Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2505.14021","last_updated":"2025-05-20T07:22:21Z","snapshot_observed_at":"2026-08-07T15:40:05.766158Z","submitted_at":"2025-05-20T07:22:21Z","title":"Adversarial Training from Mean Field Perspective","version":1},"reference_index":103,"source":"pdf_text","source_observed_at":"2026-08-07T15:43:21.902600Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2505.14021"},"observation_digest":"sha256:3f64a9a0f07ef55b3ab8c33e3c0be8751ea47a2aa0e26d0f48478615ca09ba1b","observation_id":"d0170ffb-f7aa-446a-a3f4-7fdff9103954","resolution":{"observed_at":"2026-08-07T15:43:21.902600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-07T13:57:42.783867Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2505.20553","last_updated":"2025-07-31T11:11:42Z","snapshot_observed_at":"2026-08-07T15:32:31.594466Z","submitted_at":"2025-05-26T22:26:55Z","title":"A ZeNN architecture to avoid the Gaussian trap","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:57:42.783867Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2505.20553"},"observation_digest":"sha256:8c02a5c94d01c557fbed1d634cd3793d4de651ff9d3aa4ff284a01e2742c70ba","observation_id":"cfb6856f-6604-481a-8abb-1ba2473e29a7","resolution":{"observed_at":"2026-08-07T13:57:42.783867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-07T13:50:00.918705Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2505.21119","last_updated":"2025-06-02T16:01:02Z","snapshot_observed_at":"2026-08-07T13:32:53.353279Z","submitted_at":"2025-05-27T12:38:19Z","title":"Universal Value-Function Uncertainties","version":2},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T13:50:00.918705Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2505.21119"},"observation_digest":"sha256:f9d889bdeab822335645d83f773cc5af96508c8f839470095affacb2bcdb3dbd","observation_id":"dcbc0990-6900-49d8-a00e-16ef11868caf","resolution":{"observed_at":"2026-08-07T13:50:00.918705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-06T22:50:15.749239Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.20629","last_updated":"2025-06-25T17:25:02Z","snapshot_observed_at":"2026-08-06T22:41:48.313168Z","submitted_at":"2025-06-25T17:25:02Z","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:50:15.749239Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2506.20629"},"observation_digest":"sha256:32ab82a39eab961a2437e0ea133d9c768d8fa723ee137f3e692e0ab43cc9edeb","observation_id":"182b4bf2-086e-478b-a7e5-4997fd1f11a4","resolution":{"observed_at":"2026-08-06T22:50:15.749239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2508.03810","last_updated":"2026-05-14T20:07:53Z","snapshot_observed_at":"2026-08-07T13:59:00.885099Z","submitted_at":"2025-08-05T18:00:31Z","title":"Viability of perturbative expansion for quantum field theories on neurons","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-22T00:12:10.492652Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2508.03810"},"observation_digest":"sha256:7d7e7b5d6258fa71cd683fcf5cae84536246934dcce8d39cecf9c0bab0bbb407","observation_id":"3bee39d4-ab59-4dbb-b45a-f55c0cb72b80","resolution":{"observed_at":"2026-05-22T00:14:28.124241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2605.05113","last_updated":"2026-05-06T16:44:44Z","snapshot_observed_at":"2026-08-06T00:17:33.848277Z","submitted_at":"2026-05-06T16:44:44Z","title":"How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-08T17:52:21.272304Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2605.05113"},"observation_digest":"sha256:aafbf5652da26d2e121aaed2f844e7004cd52f81726c623c54ffd83b8e7b659e","observation_id":"5146954e-0f9f-4fb7-b635-82feeb61d8af","resolution":{"observed_at":"2026-05-11T17:06:06.144949Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2605.17968","last_updated":"2026-05-18T07:25:14Z","snapshot_observed_at":"2026-08-04T20:09:38.098970Z","submitted_at":"2026-05-18T07:25:14Z","title":"Function graph transformers universally approximate operators between function spaces","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-20T13:08:22.786638Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2605.17968"},"observation_digest":"sha256:34249aeb1e60196f71eb2bfa1fc64b0cf1db20e2369353a2288a40f0c1fd6d6e","observation_id":"87a800be-6b47-4831-aa3f-250587306aa6","resolution":{"observed_at":"2026-05-20T13:13:18.758972Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2606.04426","last_updated":"2026-06-03T04:17:06Z","snapshot_observed_at":"2026-08-06T15:15:34.132980Z","submitted_at":"2026-06-03T04:17:06Z","title":"Discrete signaling mediates chaotic regularization in recurrent neural networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T03:34:30.037433Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2606.04426"},"observation_digest":"sha256:8ed9808af25f911c0c1b25211f86e13c400d2bf34c21f18685939247c2f953f4","observation_id":"00e10959-add0-413e-819f-94013d8e3b66","resolution":{"observed_at":"2026-07-02T11:26:54.925731Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2606.08218","last_updated":"2026-06-06T15:12:43Z","snapshot_observed_at":"2026-08-02T22:12:21.860387Z","submitted_at":"2026-06-06T15:12:43Z","title":"How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T20:11:41.317769Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2606.08218"},"observation_digest":"sha256:244dbc131c72cdae032d9d53b356f6add89d2c27a772a6bf06d02f3bf569a519","observation_id":"27e907ed-961d-4bcc-a5b3-b46ea4c08225","resolution":{"observed_at":"2026-07-02T20:47:22.768393Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":"1902.04760","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-02T20:47:22.766708Z","title":"Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":"48200a8a-f326-4f91-81c8-94989f02522c","year":1902},"citing_paper":{"arxiv_id":"2606.30831","last_updated":"2026-07-23T18:53:12Z","snapshot_observed_at":"2026-08-04T02:09:09.481541Z","submitted_at":"2026-06-29T19:01:43Z","title":"Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-01T01:42:14.145227Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2606.30831"},"observation_digest":"sha256:247baa186f5e21f149e18bf89e7c859804ba2b91336d9c4d477fb4832d93c6a3","observation_id":"9593607b-0a96-4c5c-a24e-4d649575b7a9","resolution":{"observed_at":"2026-07-01T12:55:43.897049Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-08-02T09:36:50.560914Z","title":"Yang.Scaling limits of wide neural networks with weight sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.30831","last_updated":"2026-07-23T18:53:12Z","snapshot_observed_at":"2026-08-04T02:09:09.481541Z","submitted_at":"2026-06-29T19:01:43Z","title":"Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T09:36:50.560914Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2606.30831"},"observation_digest":"sha256:684f25fa95c24f09a867cd0e377cd6302d4a6ec0efb3d037ca5417ed49aa34b6","observation_id":"6d71a0b9-d414-4bd7-b5a7-da9014bd65f1","resolution":{"observed_at":"2026-08-02T09:36:50.560914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04760","snapshot_observed_at":"2026-07-14T13:34:45.196896Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2607.10200","last_updated":"2026-07-11T08:25:26Z","snapshot_observed_at":"2026-07-16T23:18:14.574885Z","submitted_at":"2026-07-11T08:25:26Z","title":"The Differential Neural Tangent Kernel and Its Positivity","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T13:34:45.196896Z"},"links":{"cited_paper":"/paper/1902.04760","citing_paper":"/paper/2607.10200"},"observation_digest":"sha256:29daad68977371366322a8c78fb1595dc5f49794ec0684b5a83ba85265ee1dc1","observation_id":"d3589f3d-9464-44e0-b342-9bc4cc55629c","resolution":{"observed_at":"2026-07-14T13:34:45.196896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1902.04760/citation-record","integrity":"/paper/1902.04760/integrity","json":"/paper/1902.04760/citation-record.json","paper":"/paper/1902.04760"},"outbound":[],"paper":{"arxiv_id":"1902.04760","last_updated":"2020-04-04T22:53:19Z","latest_version":3,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-07T11:15:57.038619Z","submitted_at":"2019-02-13T06:09:18Z","title":"Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1902.04760."}