{"as_of":"2026-08-12T16:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c652eeda29dd4a5ac75f4027b0d9935a67273d9337d1dc08b9160342251ea145","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":"2026-08-12T06:34:41.77262+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":"2026-08-12T14:24:00.396667Z","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":"2026-07-02T15:17:07.182288Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":"2110.04260","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-07-02T15:17:07.182288Z","title":"arXiv preprint arXiv:2110.04260 , year=","venue":null,"work_id":"f85ba524-6177-423a-aba7-288d0b57ac47","year":2021},"citing_paper":{"arxiv_id":"2309.14509","last_updated":"2023-10-04T16:51:13Z","snapshot_observed_at":"2026-08-04T19:27:31.715261Z","submitted_at":"2023-09-25T20:15:57Z","title":"DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models","version":2},"reference_index":167,"source":"arxiv_source","source_observed_at":"2026-05-13T01:07:22.166595Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2309.14509"},"observation_digest":"sha256:55dfceef7575a02f36fa8ecddf9a1e54e1fdf656e7bd0938f0580b5c43306d5d","observation_id":"202a855d-5903-44e1-bd12-8d1df84630b8","resolution":{"observed_at":"2026-05-13T01:07:22.292061Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-08-12T14:24:00.396667Z","title":"Taming sparsely activated transformer with stochastic experts,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15419","last_updated":"2024-11-23T02:41:34Z","snapshot_observed_at":"2026-08-12T14:17:23.867496Z","submitted_at":"2024-11-23T02:41:34Z","title":"Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T14:24:00.396667Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2411.15419"},"observation_digest":"sha256:47d39759170edb3a8b9c44835816c2b97baf73346b92bbc1bc170734f44a5b2c","observation_id":"1b998076-e7ee-4d05-b0e6-200ffa91e923","resolution":{"observed_at":"2026-08-12T14:24:00.396667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-08-11T12:03:43.387092Z","title":"Taming sparsely activated transformer with stochastic experts","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.14711","last_updated":"2025-02-27T16:33:09Z","snapshot_observed_at":"2026-08-11T20:31:43.301384Z","submitted_at":"2024-12-19T10:21:20Z","title":"ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T12:03:43.387092Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2412.14711"},"observation_digest":"sha256:4fbafca95c5065f4c2af74f1be6f2ab7492688ca3b4685385716df4206281c03","observation_id":"f3528265-2cb6-43d4-8e75-46fc1d5bf4d8","resolution":{"observed_at":"2026-08-11T12:03:43.387092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-08-10T20:54:41.906572Z","title":"Taming sparsely activated transformer with stochastic experts","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.06884","last_updated":"2025-01-12T17:41:23Z","snapshot_observed_at":"2026-08-12T04:17:24.860310Z","submitted_at":"2025-01-12T17:41:23Z","title":"Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T20:54:41.906572Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2501.06884"},"observation_digest":"sha256:1f13df9ce7b029e97a5ba5d0a9887626cdd6ec7ce7878818296837df212d7487","observation_id":"b59191aa-fddc-4ba4-b15e-aea3f8979729","resolution":{"observed_at":"2026-08-10T20:54:41.906572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-08-10T11:47:24.923354Z","title":"Taming sparsely activated transformer with stochastic experts","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16650","last_updated":"2025-01-28T02:32:49Z","snapshot_observed_at":"2026-08-10T22:38:25.204754Z","submitted_at":"2025-01-28T02:32:49Z","title":"DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T11:47:24.923354Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2501.16650"},"observation_digest":"sha256:d8510a5db1826305697aa3a8c49afe933bfe7052d418b757ee2ec28274067dc6","observation_id":"82837663-8add-443f-a6c0-3baf4e2fdac8","resolution":{"observed_at":"2026-08-10T11:47:24.923354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts","version":3},"cited_work":{"arxiv_id":"2110.04260","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.04260","snapshot_observed_at":"2026-07-02T15:17:07.182288Z","title":"arXiv preprint arXiv:2110.04260 , year=","venue":null,"work_id":"f85ba524-6177-423a-aba7-288d0b57ac47","year":2021},"citing_paper":{"arxiv_id":"2607.00371","last_updated":"2026-07-01T03:11:21Z","snapshot_observed_at":"2026-08-02T15:32:07.000409Z","submitted_at":"2026-07-01T03:11:21Z","title":"MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-02T15:14:36.946247Z"},"links":{"cited_paper":"/paper/2110.04260","citing_paper":"/paper/2607.00371"},"observation_digest":"sha256:af9212d809a1b6df0be6f1452bb2f046901fe54465b8ff49c9732d1434dc0d7b","observation_id":"543a92b1-cf1d-4faf-8ffe-6e152c431736","resolution":{"observed_at":"2026-07-02T15:17:07.183793Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2110.04260/citation-record","integrity":"/paper/2110.04260/integrity","json":"/paper/2110.04260/citation-record.json","paper":"/paper/2110.04260"},"outbound":[],"paper":{"arxiv_id":"2110.04260","last_updated":"2022-02-03T21:26:25Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T11:55:57.117755Z","submitted_at":"2021-10-08T17:15:47Z","title":"Taming Sparsely Activated Transformer with Stochastic Experts"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2110.04260."}