{"as_of":"2026-08-16T20:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12b2cffe014fcd358129cae5be8330fd58c38cf0a5d4b869ef88b5e25f846242","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:22:07.575429Z","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":234,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-06T16:12:00.930771Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14459","last_updated":"2025-07-19T03:09:30Z","snapshot_observed_at":"2026-08-06T23:47:46.595834Z","submitted_at":"2025-07-19T03:09:30Z","title":"VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:12:00.930771Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2507.14459"},"observation_digest":"sha256:02686b30ff9081f614af95e31d30878dee44a0c2cbb5031a694ded1bd8d6f012","observation_id":"4f49c304-66b8-4ae4-9a2e-9fb9b696a759","resolution":{"observed_at":"2026-08-06T16:12:00.930771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2604.11993","last_updated":"2026-04-13T19:34:23Z","snapshot_observed_at":"2026-08-14T13:55:26.193838Z","submitted_at":"2026-04-13T19:34:23Z","title":"Ultra-low-light computer vision using trained photon correlations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T16:12:10.810592Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2604.11993"},"observation_digest":"sha256:0fb076fa0c63bb55f720c7b08185332d9624cb34dc44ab47459d0b88e4fedb2a","observation_id":"73854845-dfa1-4356-9969-f2c870cd5937","resolution":{"observed_at":"2026-05-11T09:11:00.422978Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2604.12894","last_updated":"2026-04-14T15:42:48Z","snapshot_observed_at":"2026-08-11T06:22:14.380565Z","submitted_at":"2026-04-14T15:42:48Z","title":"Representing 3D Faces with Learnable B-Spline Volumes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T15:36:02.269466Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2604.12894"},"observation_digest":"sha256:e791d6dbd5f38c6b10c2113c858bf4343601bdcb88a130e84bba6e1a7704071f","observation_id":"6dee614f-c14b-45cf-9173-e20036347590","resolution":{"observed_at":"2026-05-11T10:11:03.976805Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2604.25065","last_updated":"2026-04-27T23:42:52Z","snapshot_observed_at":"2026-08-15T16:35:38.832551Z","submitted_at":"2026-04-27T23:42:52Z","title":"ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-08T04:13:28.233483Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2604.25065"},"observation_digest":"sha256:5a54cb6744c69e3189a08c865edc8b2f5e9ed32f29c22602d3f3cd3e21f095ce","observation_id":"ee89778c-1346-499d-96ee-f4131f3bb937","resolution":{"observed_at":"2026-05-11T21:51:11.428456Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2605.09727","last_updated":"2026-05-10T19:52:29Z","snapshot_observed_at":"2026-08-16T18:57:20.494739Z","submitted_at":"2026-05-10T19:52:29Z","title":"One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-12T03:46:21.786972Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2605.09727"},"observation_digest":"sha256:ac78027392826689554d7a45f831dd20aa75a6dfb813da20b4ae8ee37bc0fa10","observation_id":"a3154c72-b3d4-4da1-9ce0-cbbd342dd8ec","resolution":{"observed_at":"2026-05-12T06:56:32.095806Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2605.11007","last_updated":"2026-08-02T03:28:25Z","snapshot_observed_at":"2026-08-16T09:34:01.750363Z","submitted_at":"2026-05-10T08:14:14Z","title":"The Transformer as a Polar State Estimator","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-13T00:58:28.483037Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2605.11007"},"observation_digest":"sha256:ffd6bfa05c4f1a218d307dd19a89cfdcad24bf670fdee92428d6925059059d04","observation_id":"b733a52c-1048-4009-9687-0cee3096d628","resolution":{"observed_at":"2026-05-13T02:07:09.236969Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":"2106.09681","doi":"10.48550/arxiv.2106.09681","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Xcit: Cross-covariance image transformers","venue":"arXiv (Cornell University)","work_id":"905f2cd4-52d0-43a8-98b5-bc26f144e4e4","year":2021},"citing_paper":{"arxiv_id":"2605.22098","last_updated":"2026-05-21T07:36:00Z","snapshot_observed_at":"2026-08-12T18:54:37.715720Z","submitted_at":"2026-05-21T07:36:00Z","title":"TextTeacher: What Can Language Teach About Images?","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-22T07:26:03.594414Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2605.22098"},"observation_digest":"sha256:351a5ffb557d9b95ff8a000ceada5317738abecf1e30646870f82f5aa0a4a114","observation_id":"569c5f7f-f17e-45e0-ac4b-18c65f0688a0","resolution":{"observed_at":"2026-05-22T07:26:12.533647Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-22T13:22:34.184109+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-14T04:15:48.342140Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.10288","last_updated":"2026-08-10T22:45:22Z","snapshot_observed_at":"2026-08-16T18:06:38.184117Z","submitted_at":"2026-08-10T22:45:22Z","title":"Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T04:15:48.342140Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2608.10288"},"observation_digest":"sha256:c4a39f1b3c76a2a235c409616871dafd0f35af562511b7a20c8c3a560314120a","observation_id":"2e41a57e-1fdc-4751-88d3-0f2d8a6f42ff","resolution":{"observed_at":"2026-08-14T04:15:48.342140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09681","snapshot_observed_at":"2026-08-16T00:22:07.575429Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.12084","last_updated":"2026-08-12T14:07:09Z","snapshot_observed_at":"2026-08-16T00:50:45.268717Z","submitted_at":"2026-08-12T14:07:09Z","title":"NAE: Normalizing AutoEncoder","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-16T00:22:07.575429Z"},"links":{"cited_paper":"/paper/2106.09681","citing_paper":"/paper/2608.12084"},"observation_digest":"sha256:eec17ea39f245ea9f1265c8d05737e82baa0fc3783d45e4ebe24cc78600b72b8","observation_id":"137895e7-81ac-49ae-8f0d-e1c9ca5035a5","resolution":{"observed_at":"2026-08-16T00:22:07.575429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.09681/citation-record","integrity":"/paper/2106.09681/integrity","json":"/paper/2106.09681/citation-record.json","paper":"/paper/2106.09681"},"outbound":[],"paper":{"arxiv_id":"2106.09681","last_updated":"2021-06-18T15:33:31Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T18:16:19.424312Z","submitted_at":"2021-06-17T17:33:35Z","title":"XCiT: Cross-Covariance Image Transformers"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2106.09681."}