{"as_of":"2026-08-10T06:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44c53f886b2a6063c47f376f755006fe65613d0fb182d9f9a4c9842fb10c8f55","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:50:55.871315Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-11T01:17:44.903225Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-08-07T00:50:55.871315Z","title":"arXiv preprint arXiv:2004.13922 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.12574","last_updated":"2025-06-14T17:06:04Z","snapshot_observed_at":"2026-08-08T16:47:38.362371Z","submitted_at":"2025-06-14T17:06:04Z","title":"Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T00:50:55.871315Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2506.12574"},"observation_digest":"sha256:22100e3d7db0f14609143a33a01fdd286e4c97b84b4cba9430c93e48cce1e63a","observation_id":"1c3e85ec-c72b-4e76-ada5-f6755be7db4c","resolution":{"observed_at":"2026-08-07T00:50:55.871315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-08-06T19:16:55.671389Z","title":"arXiv preprint arXiv:2004.13922 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.06071","last_updated":"2025-08-14T07:56:46Z","snapshot_observed_at":"2026-08-08T00:26:40.379475Z","submitted_at":"2025-07-08T15:14:27Z","title":"MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding","version":4},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T19:16:55.671389Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2507.06071"},"observation_digest":"sha256:294adc563cfd8dc2a6e5042618c4b2dd06d0730f5be72c0dd8b6fcce5bcc3ae4","observation_id":"2edecf0f-efbb-404b-a97a-58dd16533bd6","resolution":{"observed_at":"2026-08-06T19:16:55.671389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-08-06T16:51:24.716997Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.12337","last_updated":"2025-07-16T15:29:56Z","snapshot_observed_at":"2026-08-09T09:57:12.198204Z","submitted_at":"2025-07-16T15:29:56Z","title":"MExplore: an entity-based visual analytics approach for medical expertise acquisition","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T16:51:24.716997Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2507.12337"},"observation_digest":"sha256:da6d8c56bfd3aeabd3a3661e640ad4aa4004a89b777d8011cd6949fbd1369ab4","observation_id":"26383776-8008-46da-963e-def695930ee5","resolution":{"observed_at":"2026-08-06T16:51:24.716997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-08-05T13:22:08.315484Z","title":"Revisiting pre-trained models for chinese natural language processing","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.00731","last_updated":"2025-08-31T07:51:22Z","snapshot_observed_at":"2026-08-10T01:34:53.644280Z","submitted_at":"2025-08-31T07:51:22Z","title":"LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T13:22:08.315484Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2509.00731"},"observation_digest":"sha256:0c17e2d40973dd6b5a0db0b077e00df9c77ddd0608123b7d16f1e4e4f0961c90","observation_id":"8c314798-c44e-4736-b64a-6735a67e2237","resolution":{"observed_at":"2026-08-05T13:22:08.315484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":"2004.13922","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-07-11T01:17:44.903225Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","venue":"cs.CL","work_id":"45911704-9ead-409d-bb15-77e030341dbe","year":2020},"citing_paper":{"arxiv_id":"2605.25474","last_updated":"2026-05-25T06:26:46Z","snapshot_observed_at":"2026-08-05T06:19:01.106812Z","submitted_at":"2026-05-25T06:26:46Z","title":"TypedCSIP: Typed Counterfactual Pretraining for Chinese Legislative Conflict Classification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T22:06:28.247167Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2605.25474"},"observation_digest":"sha256:f37e5b901f2c960f3e43622bc694921aaa4ce756e1679acfcf1204bfd069869e","observation_id":"7d6d7c11-4482-4240-80ca-f389b5dce3e2","resolution":{"observed_at":"2026-06-29T22:14:00.217855Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":"2004.13922","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-07-11T01:17:44.903225Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","venue":"cs.CL","work_id":"45911704-9ead-409d-bb15-77e030341dbe","year":2020},"citing_paper":{"arxiv_id":"2606.25325","last_updated":"2026-07-20T13:20:39Z","snapshot_observed_at":"2026-08-02T10:17:41.360200Z","submitted_at":"2026-06-24T02:43:26Z","title":"Omni-Perception Policy Optimization for Multimodal Emotion Reasoning","version":1},"reference_index":117,"source":"arxiv_source","source_observed_at":"2026-06-25T21:31:38.450382Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2606.25325"},"observation_digest":"sha256:c4d6f5d74cfdaa6fa0616210898922dfebfd331b1b6112bc7685b6b00512a0bb","observation_id":"c0c8df08-464d-4d29-8db3-70172fe2a2f3","resolution":{"observed_at":"2026-07-04T19:20:06.348294Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":"2004.13922","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-07-11T01:17:44.903225Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","venue":"cs.CL","work_id":"45911704-9ead-409d-bb15-77e030341dbe","year":2020},"citing_paper":{"arxiv_id":"2607.06149","last_updated":"2026-07-08T15:51:29Z","snapshot_observed_at":"2026-08-07T18:41:11.539772Z","submitted_at":"2026-07-07T11:20:25Z","title":"BlossomPsy: A User-Centric AI System for Adaptive and Engaging MBTI Personality Assessments","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-08T15:15:53.050341Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2607.06149"},"observation_digest":"sha256:a0b9b9d8e70c3b7332d0b2ae5de3ab562d4956fff8e04d5d8dec2e6acde47687","observation_id":"af7880d8-e8bc-4d90-9160-850aa9bc877e","resolution":{"observed_at":"2026-07-08T15:25:03.104055Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","version":2},"cited_work":{"arxiv_id":"2004.13922","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.13922","snapshot_observed_at":"2026-07-11T01:17:44.903225Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing","venue":"cs.CL","work_id":"45911704-9ead-409d-bb15-77e030341dbe","year":2020},"citing_paper":{"arxiv_id":"2607.06149","last_updated":"2026-07-08T15:51:29Z","snapshot_observed_at":"2026-08-07T18:41:11.539772Z","submitted_at":"2026-07-07T11:20:25Z","title":"BlossomPsy: A User-Centric AI System for Adaptive and Engaging MBTI Personality Assessments","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-11T01:14:29.490794Z"},"links":{"cited_paper":"/paper/2004.13922","citing_paper":"/paper/2607.06149"},"observation_digest":"sha256:393ed541aea12d82f74e5ac1f9acbf8c63944e034c068c2f9cb0252a97e3fa8b","observation_id":"cb77be69-faac-4c92-9e25-cbafe85707c2","resolution":{"observed_at":"2026-07-11T01:17:44.924985Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2004.13922/citation-record","integrity":"/paper/2004.13922/integrity","json":"/paper/2004.13922/citation-record.json","paper":"/paper/2004.13922"},"outbound":[],"paper":{"arxiv_id":"2004.13922","last_updated":"2020-11-02T06:27:52Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T03:08:53.449102Z","submitted_at":"2020-04-29T02:08:30Z","title":"Revisiting Pre-Trained Models for Chinese Natural Language Processing"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2004.13922."}