{"as_of":"2026-08-08T09:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8d27de3d9856f26cccb19fde995c00c54ca683ae07e9f8836ee7a138a2f02669","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:51:48.152786Z","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":"2026-08-06T15:40:04.389632Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.01279","snapshot_observed_at":"2026-08-07T11:51:48.152786Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.01266","last_updated":"2025-06-02T02:36:32Z","snapshot_observed_at":"2026-08-07T11:44:11.259086Z","submitted_at":"2025-06-02T02:36:32Z","title":"Detoxification of Large Language Models through Output-layer Fusion with a Calibration Model","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T11:51:48.152786Z"},"links":{"cited_paper":"/paper/2105.01279","citing_paper":"/paper/2506.01266"},"observation_digest":"sha256:976e929fee28cd26ea6068765afc9f14f2a07b3c7618ab62c55dd600cc35830c","observation_id":"9edaf56a-78b6-45dd-aba5-94c27b9fddc4","resolution":{"observed_at":"2026-08-07T11:51:48.152786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.01279","snapshot_observed_at":"2026-08-07T05:48:39.638931Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07086","last_updated":"2025-06-08T11:15:57Z","snapshot_observed_at":"2026-08-07T05:40:25.615197Z","submitted_at":"2025-06-08T11:15:57Z","title":"Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T05:48:39.638931Z"},"links":{"cited_paper":"/paper/2105.01279","citing_paper":"/paper/2506.07086"},"observation_digest":"sha256:44346b0f3e9f7a3232a07b0348388d42e8d3adc8f122411bb5e8a41b95ee1c5a","observation_id":"ded80348-f21f-4b0b-8509-68fbce618ca5","resolution":{"observed_at":"2026-08-07T05:48:39.638931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.01279","snapshot_observed_at":"2026-08-06T19:54:41.909167Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.04333","last_updated":"2025-07-06T10:37:16Z","snapshot_observed_at":"2026-08-07T18:26:49.746805Z","submitted_at":"2025-07-06T10:37:16Z","title":"Computed Tomography Visual Question Answering with Cross-modal Feature Graphing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:54:41.909167Z"},"links":{"cited_paper":"/paper/2105.01279","citing_paper":"/paper/2507.04333"},"observation_digest":"sha256:2d18b0f4aafed94013d59b14b7d0d32d86e09965ab73fc9ab1e5641b3c9b1a2b","observation_id":"488efb1a-27f0-4e35-ae39-7e62ebcce14d","resolution":{"observed_at":"2026-08-06T19:54:41.909167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.01279","snapshot_observed_at":"2026-08-06T17:59:27.822979Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09485","last_updated":"2025-07-13T04:07:07Z","snapshot_observed_at":"2026-08-06T17:52:24.595603Z","submitted_at":"2025-07-13T04:07:07Z","title":"Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.822979Z"},"links":{"cited_paper":"/paper/2105.01279","citing_paper":"/paper/2507.09485"},"observation_digest":"sha256:e3b55d3a6b72498626dbed7e75679d32229d50225b14beab7dfea709adb9e80e","observation_id":"58318cd6-8cf7-4776-a5c2-1d86b2646c2d","resolution":{"observed_at":"2026-08-06T17:59:27.822979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","version":1},"cited_work":{"arxiv_id":"2105.01279","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.01279","snapshot_observed_at":"2026-08-06T15:40:04.389632Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders","venue":"cs.CL","work_id":"c2e25855-f206-4285-970e-b847614445ff","year":2021},"citing_paper":{"arxiv_id":"2507.15275","last_updated":"2025-07-21T06:23:16Z","snapshot_observed_at":"2026-08-06T15:33:46.203127Z","submitted_at":"2025-07-21T06:23:16Z","title":"ChiMed 2.0: Advancing Chinese Medical Dataset in Facilitating Large Language Modeling","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T15:40:02.214823Z"},"links":{"cited_paper":"/paper/2105.01279","citing_paper":"/paper/2507.15275"},"observation_digest":"sha256:10ac301da0d7c342598d072b6d27d99e3e99dac884bf5c6ef8a22cccbe9c6d4e","observation_id":"e44c7fd8-c623-493a-80f5-241f86610299","resolution":{"observed_at":"2026-08-06T15:40:04.451408Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2105.01279/citation-record","integrity":"/paper/2105.01279/integrity","json":"/paper/2105.01279/citation-record.json","paper":"/paper/2105.01279"},"outbound":[],"paper":{"arxiv_id":"2105.01279","last_updated":"2021-05-04T04:08:58Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T19:01:56.567591Z","submitted_at":"2021-05-04T04:08:58Z","title":"ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2105.01279."}