{"as_of":"2026-08-14T08:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:22fab0e38210b54d1c0bba4c52dcd217f4fefa924750854e3c0e330ca5a2067c","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:42:30.766350Z","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":"2026-07-02T23:07:26.960601Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-08-07T15:42:30.766350Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14088","last_updated":"2025-05-20T08:52:28Z","snapshot_observed_at":"2026-08-11T03:09:27.639558Z","submitted_at":"2025-05-20T08:52:28Z","title":"Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:30.766350Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2505.14088"},"observation_digest":"sha256:ada06d8143982f28930c7062d7cc9ae6629d9ad1fee1998392bec8aec62b7b5e","observation_id":"c8d54a78-966d-46ca-8c70-bf244f584773","resolution":{"observed_at":"2026-08-07T15:42:30.766350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":"2206.04046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-07-02T23:07:26.960601Z","title":"Sparse mixture-of-experts are domain generalizable learners","venue":null,"work_id":"8afeedb0-a7d3-455c-bf87-13b196fc300c","year":2022},"citing_paper":{"arxiv_id":"2507.00029","last_updated":"2026-05-13T05:28:39Z","snapshot_observed_at":"2026-08-11T06:15:13.326412Z","submitted_at":"2025-06-17T14:58:54Z","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-19T09:05:25.236355Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2507.00029"},"observation_digest":"sha256:0bf16d59746c3fdd4c012d0d9db7470e966ea5c8c93ddf4bc770db6efc674a5a","observation_id":"27b704ef-3681-48e2-a71e-cc93800ac751","resolution":{"observed_at":"2026-05-19T09:07:14.559981Z","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":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-08-04T00:27:27.235376Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.01192","last_updated":"2026-06-03T05:41:12Z","snapshot_observed_at":"2026-08-10T08:16:23.120671Z","submitted_at":"2025-11-03T03:36:48Z","title":"DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T00:27:27.235376Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2511.01192"},"observation_digest":"sha256:16430ab4dfcb656e29317d5f0462e10982e1a4b09f1051cc2bfc9f007314ff0c","observation_id":"f4efbd08-6641-42ad-8a2c-e6749753fc33","resolution":{"observed_at":"2026-08-04T00:27:27.235376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":"2206.04046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-07-02T23:07:26.960601Z","title":"Sparse mixture-of-experts are domain generalizable learners","venue":null,"work_id":"8afeedb0-a7d3-455c-bf87-13b196fc300c","year":2022},"citing_paper":{"arxiv_id":"2604.16892","last_updated":"2026-04-18T07:53:50Z","snapshot_observed_at":"2026-08-13T03:14:04.414916Z","submitted_at":"2026-04-18T07:53:50Z","title":"CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T07:48:03.572543Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2604.16892"},"observation_digest":"sha256:a800ebe54a7dec55bf09036ce7fb18daa3432dd07099f0db2787925623572a6f","observation_id":"78dc1451-c4bb-43a0-a4c2-f791f2d69b58","resolution":{"observed_at":"2026-05-10T07:52:13.790549Z","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":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":"2206.04046","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-07-02T23:07:26.960601Z","title":"Sparse mixture-of-experts are domain generalizable learners","venue":null,"work_id":"8afeedb0-a7d3-455c-bf87-13b196fc300c","year":2022},"citing_paper":{"arxiv_id":"2606.08814","last_updated":"2026-06-07T20:07:24Z","snapshot_observed_at":"2026-08-12T12:22:24.694247Z","submitted_at":"2026-06-07T20:07:24Z","title":"STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T18:28:35.162934Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2606.08814"},"observation_digest":"sha256:72bf83b0b153beee9e90e623e973399a9dbb13e182029049bc3d1a348211c16a","observation_id":"43ca8e98-75e2-494b-a84b-c1dffe64ded9","resolution":{"observed_at":"2026-07-02T23:07:26.962049Z","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":"2206.04046","last_updated":"2023-01-27T06:46:00Z","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04046","snapshot_observed_at":"2026-08-01T20:10:30.815191Z","title":"arXiv preprint arXiv:2206.04046 (2022) 2","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.16726","last_updated":"2026-07-18T09:24:39Z","snapshot_observed_at":"2026-08-11T03:09:42.016899Z","submitted_at":"2026-07-18T09:24:39Z","title":"Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T20:10:30.815191Z"},"links":{"cited_paper":"/paper/2206.04046","citing_paper":"/paper/2607.16726"},"observation_digest":"sha256:db4e0fc07e144829f1d3bfd62747aed2bb06a9c1ac0f45e8bd45e32b3c910866","observation_id":"3ddf15a1-dc8f-463b-9364-a29957b41ded","resolution":{"observed_at":"2026-08-01T20:10:30.815191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2206.04046/citation-record","integrity":"/paper/2206.04046/integrity","json":"/paper/2206.04046/citation-record.json","paper":"/paper/2206.04046"},"outbound":[],"paper":{"arxiv_id":"2206.04046","last_updated":"2023-01-27T06:46:00Z","latest_version":6,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T15:27:50.895879Z","submitted_at":"2022-06-08T17:59:57Z","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners"},"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 6 inbound Pith citation observations for arXiv:2206.04046."}