{"as_of":"2026-08-12T07:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:681e902b84fea71e630623d2742920793c814a779dcaf8af6d993d3f786b9ace","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:19:44.402023Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.05672/citation-record","integrity":"/paper/2506.05672/integrity","json":"/paper/2506.05672/citation-record.json","paper":"/paper/2506.05672"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.059635Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.059635Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:d58f0bd1cfea0071eeff0ae96c0c21a0c648c97f56f5bf8b6c0d76c9aa8a8736","observation_id":"4cd0a788-6118-4b4e-9079-1f34b155f583","resolution":{"observed_at":"2026-08-07T10:19:42.059635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.101352Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.101352Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:81b7012c68c31b3c0389daf6b051ad7ca2c7b0aba81205b804f806bcaf556fd7","observation_id":"315e0b3b-813e-49ea-b62f-0eaf4b577e9c","resolution":{"observed_at":"2026-08-07T10:19:42.101352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:46.640214Z","title":"Gaussian process prior variational autoencoders","venue":null,"work_id":"2e95e566-f3ea-4a9a-9b4e-ec57a169a67f","year":2018},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.171159Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:bf35589936f1531faa610cd7db5e15c1e9fb2b2b8c11b39edc03398c35715eeb","observation_id":"af4eed04-9725-4113-9820-5ed951393386","resolution":{"observed_at":"2026-08-07T10:19:46.698147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v37i6.25860","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Cf-vit: A general coarse-to-fine method for vision transformer","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"3bb82576-3572-477f-abd2-52481d4cfa61","year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.234358Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:e13687b1c2a653c50e024f79b34295a1f2318c6e794cf693225805b92df13310","observation_id":"0c231f50-5c09-4483-b3ac-a7fa40f4076b","resolution":{"observed_at":"2026-08-07T10:19:44.805924Z","resolver_source":"doi","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":"2101.00828","last_updated":"2021-07-08T17:18:13Z","snapshot_observed_at":"2026-08-03T13:29:59.113145Z","submitted_at":"2021-01-04T08:31:11Z","title":"Transformer-based Conditional Variational Autoencoder for Controllable Story Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00828","snapshot_observed_at":"2026-08-07T10:19:42.300336Z","title":"Transformer-based conditional variational autoencoder for controllable story generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.300336Z"},"links":{"cited_paper":"/paper/2101.00828","citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:25de7471434a71946a56cfab1f81b32d64c76d6290928fb19c1dd38072ae93f9","observation_id":"0aa99e17-2832-44c2-8d87-76eafffd1af2","resolution":{"observed_at":"2026-08-07T10:19:42.300336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.384946Z","title":"In-context learning creates task vectors","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.384946Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:1bed569e0d620b2996070001fd7622a0b6d63df7c1b1f3e06a4c1353171efdb2","observation_id":"4a235b5b-95bb-480b-9d5f-a66b206edb56","resolution":{"observed_at":"2026-08-07T10:19:42.384946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:46.514446Z","title":"A VAE for Transformers with Nonparametric Variational Information Bottleneck","venue":null,"work_id":"ccb2bd31-3244-45c4-8e9c-986096710b16","year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.450151Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:503ef85f475e5e0f40df489f29f58804c693bbe77fb01c0950ae8c44cd45a8b0","observation_id":"63781fba-02bf-41e1-b873-f9b2f1e0638d","resolution":{"observed_at":"2026-08-07T10:19:46.565318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.514370Z","title":"Kingma and Max Welling","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.514370Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:7a2cb8188afa8deffaefba775957eacd1559dbf9e06b7fa274173cf2eb4164b9","observation_id":"61c2aa74-0b6c-4bc4-a0ea-3a95bf0c4392","resolution":{"observed_at":"2026-08-07T10:19:42.514370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.588622Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.588622Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:85185f68ad84f1afd191a822787e1b51de8070153beeff834efabcd81f414f51","observation_id":"8d683374-2425-42bc-b83c-e74de2dbd4c3","resolution":{"observed_at":"2026-08-07T10:19:42.588622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:42.701848Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.701848Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:032ec7aabe63e33fc38871e7054c071e4099615452a05c42aa7fc71d22fdad1c","observation_id":"304cac0f-7a19-4efe-9831-368c5ee0e1e2","resolution":{"observed_at":"2026-08-07T10:19:42.701848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:46.337907Z","title":"Fast-slow recurrent neural networks","venue":null,"work_id":"98c51d9d-ffad-4d86-becd-bc4632bf358f","year":2017},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.801505Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:f99d552e89731240ec589f8834698c6fd57b6bc1f9c4766251f2d6015a8aba95","observation_id":"4948b7ce-2e7c-48ea-96ea-011431fa813a","resolution":{"observed_at":"2026-08-07T10:19:46.422935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:46.185955Z","title":"Hypertuning: Toward adapting large language models without back-propagation","venue":null,"work_id":"6bc28b41-f198-4145-94c0-e8c977baa892","year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:42.927595Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:84529ea1f92769deb6f5c10797200b5bb6eb65bcf3caaa028a2159fca43a4d10","observation_id":"349c7ef4-ffc0-4c96-b956-142df9a2cf37","resolution":{"observed_at":"2026-08-07T10:19:46.253514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:43.007540Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.007540Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:215c968a4979f6b9c9fc4a9ffaa9cc534ef35a3ae794e14a36c82302f855ccb6","observation_id":"a5c6c0be-921d-4672-bf6d-c66b3b54ff37","resolution":{"observed_at":"2026-08-07T10:19:43.007540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:43.093303Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.093303Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:8aa89f2d1ae7aadaeac386bcdf857b4187efe76b6bd0de2730f7c853732e3519","observation_id":"8f7d56fa-5a80-4f47-b82a-5b79dbb8fe8b","resolution":{"observed_at":"2026-08-07T10:19:43.093303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.958520Z","title":"Orthogonal projection loss","venue":null,"work_id":"a8b42761-90e1-4e9b-9866-fb480a084cd9","year":2021},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.157688Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:450fe65fd2341dcf177295f8ee518a0a3406cf530551adb6e37fc48028f4aa87","observation_id":"dcaac2e5-b1f4-4c07-92e4-a59c167248eb","resolution":{"observed_at":"2026-08-07T10:19:46.036814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.769857Z","title":"Dynamicvit: Efficient vision transformers with dynamic token sparsification","venue":null,"work_id":"e1e2046f-853c-4fc4-8032-81bed6d99999","year":2021},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.250797Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:d3263dcbb4c3bf9eafe4cb8e50bc9764f906608492f146784e112cf2aaf22f57","observation_id":"61d9ea04-d2a0-4072-b98f-25022b328446","resolution":{"observed_at":"2026-08-07T10:19:45.843013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:43.347372Z","title":"Learning to control fast-weight memories: An alternative to dynamic recurrent networks","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.347372Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:6f49ef217c5049d52863aeebb09d4cc0ad4a303504c421782cb94a217b9d6993","observation_id":"53123e4b-86ad-428b-9da5-97705aeff4f0","resolution":{"observed_at":"2026-08-07T10:19:43.347372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.07909","last_updated":"2016-06-10T14:45:08Z","snapshot_observed_at":"2026-07-06T04:28:16.222296Z","submitted_at":"2015-08-31T16:37:31Z","title":"Neural Machine Translation of Rare Words with Subword Units","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.07909","snapshot_observed_at":"2026-08-07T10:19:43.432024Z","title":"Neural machine translation of rare words with subword units","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.432024Z"},"links":{"cited_paper":"/paper/1508.07909","citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:e40a582c479cf1d4eb0cda017983fa16897fe61e9d4b35daf1fe69bde6fef1e4","observation_id":"66d2743d-54f1-4c0a-a61a-4f0f7957d839","resolution":{"observed_at":"2026-08-07T10:19:43.432024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:43.516435Z","title":"Patch slimming for efficient vision transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.516435Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:eea05a4526304096775d811e01964713c85b8bf8465ca5782ad60c11c8be0c8b","observation_id":"76330345-cced-4c1b-9473-972397aa19d9","resolution":{"observed_at":"2026-08-07T10:19:43.516435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:43.621411Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.621411Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:3c88561ed9c4980ae126b74557d3e222d93c895278ce508c1442f80e6c368724","observation_id":"defa3c62-148b-4a2e-81dd-e3d486a3cd09","resolution":{"observed_at":"2026-08-07T10:19:43.621411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.594616Z","title":"Transformers learn in-context by gradient descent","venue":null,"work_id":"7611af27-dd02-412f-9b83-7d7b368a595a","year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.699600Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:8f21940e2aec29eb95052c4ed7df981f3956e321732bf95f0cbc5ac721d017cf","observation_id":"2b166800-9dac-4a9f-9cf5-5c1b318bf6e0","resolution":{"observed_at":"2026-08-07T10:19:45.700864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.410858Z","title":"Uncovering mesa-optimization algorithms in transformers, 2023","venue":null,"work_id":"9c103876-b874-4837-bfc5-27fd55506410","year":2023},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.764629Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:408779889711f07385b1670a53ecc510e2f027ec0812456a7064d1a687ecc851","observation_id":"796d962b-de31-47ab-a092-82eb208d5999","resolution":{"observed_at":"2026-08-07T10:19:45.483993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":"10.24963/ijcai.2019/727","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"T-cvae: Transformer-based conditioned variational autoencoder for story completion","venue":null,"work_id":"3605f78e-1181-4ef9-be79-800f6b878d38","year":2019},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.839591Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:58010aaf6e0cbee4765f09f628e5ce06db27226a39e76a7156ff30d38c430173","observation_id":"a9d8e91f-2d80-474d-80f4-9815f17db886","resolution":{"observed_at":"2026-08-07T10:19:44.551962Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.253987Z","title":"Wikimedia downloads","venue":null,"work_id":"f18df89a-f4c5-459a-986c-066dc3e2c701","year":null},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:43.936979Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:496764148fab870a205793d85baa0040e860ec3e4a7cc7e04dcd6b4e7bac3e2d","observation_id":"0b416d09-26f5-45aa-9a5b-315ccaeb6bef","resolution":{"observed_at":"2026-08-07T10:19:45.318134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:44.047795Z","title":"Evo-vit: Slow-fast token evolution for dynamic vision transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:44.047795Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:939fe60f92d6173e6b915c55df99b77b62ba69d570c1ec589a0ac2d353c06357","observation_id":"f9241a03-e948-4009-be8f-e8ffbd6972cf","resolution":{"observed_at":"2026-08-07T10:19:44.047795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:45.090777Z","title":"Chi, Jason Wei, Jeff Dean, Liam B","venue":null,"work_id":"580d7b64-eecc-40c3-9d60-646d836fadea","year":2022},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:44.105247Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:92ee6838b584ab4720d93e39d608ccd348f544c03a8be4c4eeda5433a1a91385","observation_id":"00bb35db-33b8-4ae0-a233-f7de58936549","resolution":{"observed_at":"2026-08-07T10:19:45.153800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:44.218665Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:44.218665Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:e7ef7fec717e433312c1fb8e0c72d35748a17705e24a50b8678e616d06a81fa1","observation_id":"7349ae91-9851-462d-9a0d-61d3a535d0ee","resolution":{"observed_at":"2026-08-07T10:19:44.218665Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:44.305848Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:44.305848Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:0b24166c9fe5d703447d092264a49da11477f174ce121f61ed4d618eb2fcc3db","observation_id":"4326401b-1090-4cd9-9d83-335cfef331e4","resolution":{"observed_at":"2026-08-07T10:19:44.305848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:44.402023Z","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T10:19:44.402023Z"},"links":{"citing_paper":"/paper/2506.05672"},"observation_digest":"sha256:40cec89acf17b0797f3135960ee4505a8f64b35fd70ad55f7072ff3577eb3271","observation_id":"1f140c7b-c20a-4b1f-82e7-c6cf0bc4e87e","resolution":{"observed_at":"2026-08-07T10:19:44.402023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.05672","last_updated":"2025-06-06T01:34:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T22:35:00.343142Z","submitted_at":"2025-06-06T01:34:39Z","title":"Contextually Guided Transformers via Low-Rank Adaptation"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":2,"verified_fuzzy":10},"total_outbound_references":29},"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 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.05672."}