{"as_of":"2026-08-14T11:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9aabd0b2e556ee07b4fdc18900dfff901dc56834c91b8564d1d24a00acb3f7a9","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:33:34.490756Z","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":"2026-07-04T03:49:30.351567Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2304.10195","last_updated":"2023-04-20T10:12:09Z","snapshot_observed_at":"2026-08-13T11:58:52.450039Z","submitted_at":"2023-04-20T10:12:09Z","title":"CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval","version":1},"cited_work":{"arxiv_id":"2304.10195","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.10195","snapshot_observed_at":"2026-07-04T03:49:30.351567Z","title":"Cot-mote: Explor- ing contextual masked auto-encoder pre-training with mixture-of-textual-experts for passage retrieval","venue":null,"work_id":"76fee986-e721-40ee-a8b1-8df4aa6bb631","year":null},"citing_paper":{"arxiv_id":"2401.15947","last_updated":"2024-12-23T08:05:14Z","snapshot_observed_at":"2026-08-06T02:31:58.372974Z","submitted_at":"2024-01-29T08:13:40Z","title":"MoE-LLaVA: Mixture of Experts for Large Vision-Language Models","version":5},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T02:33:30.143907Z"},"links":{"cited_paper":"/paper/2304.10195","citing_paper":"/paper/2401.15947"},"observation_digest":"sha256:58cc19be9561afa9e84c2961f1a29b0556e5b0293cb9be09a9790cb75faaf70b","observation_id":"9b2a5077-c9fc-4527-8b39-d64ea1f774e7","resolution":{"observed_at":"2026-05-16T02:33:30.358585Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.10195","last_updated":"2023-04-20T10:12:09Z","snapshot_observed_at":"2026-08-13T11:58:52.450039Z","submitted_at":"2023-04-20T10:12:09Z","title":"CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.10195","snapshot_observed_at":"2026-08-11T14:33:34.490756Z","title":"arXiv preprint arXiv:2304.10195 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11864","last_updated":"2024-12-16T15:20:13Z","snapshot_observed_at":"2026-08-12T12:07:04.876950Z","submitted_at":"2024-12-16T15:20:13Z","title":"Investigating Mixture of Experts in Dense Retrieval","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:33:34.490756Z"},"links":{"cited_paper":"/paper/2304.10195","citing_paper":"/paper/2412.11864"},"observation_digest":"sha256:1526b63e55fab1edef71420c576d3ae015ff353114ef8cddbbedfd610ce4822b","observation_id":"bfee9b9e-d475-41bb-86c2-2e641606d66f","resolution":{"observed_at":"2026-08-11T14:33:34.490756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.10195","last_updated":"2023-04-20T10:12:09Z","snapshot_observed_at":"2026-08-13T11:58:52.450039Z","submitted_at":"2023-04-20T10:12:09Z","title":"CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval","version":1},"cited_work":{"arxiv_id":"2304.10195","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.10195","snapshot_observed_at":"2026-07-04T03:49:30.351567Z","title":"Cot-mote: Explor- ing contextual masked auto-encoder pre-training with mixture-of-textual-experts for passage retrieval","venue":null,"work_id":"76fee986-e721-40ee-a8b1-8df4aa6bb631","year":null},"citing_paper":{"arxiv_id":"2606.20970","last_updated":"2026-06-18T22:17:18Z","snapshot_observed_at":"2026-08-01T20:10:55.070907Z","submitted_at":"2026-06-18T22:17:18Z","title":"CogniRoute: Learning to Route Social Evidence in Omni-Modal Models","version":1},"reference_index":143,"source":"arxiv_source","source_observed_at":"2026-06-26T17:37:11.371892Z"},"links":{"cited_paper":"/paper/2304.10195","citing_paper":"/paper/2606.20970"},"observation_digest":"sha256:f2018449767864f462f0e43d0700a140523d2294737eced19d5989e2df423bd6","observation_id":"015825a2-5e1a-4333-849a-39ce25ff2433","resolution":{"observed_at":"2026-07-04T03:49:30.354096Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2304.10195/citation-record","integrity":"/paper/2304.10195/integrity","json":"/paper/2304.10195/citation-record.json","paper":"/paper/2304.10195"},"outbound":[],"paper":{"arxiv_id":"2304.10195","last_updated":"2023-04-20T10:12:09Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T11:58:52.450039Z","submitted_at":"2023-04-20T10:12:09Z","title":"CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2304.10195."}