{"as_of":"2026-08-11T10:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3a423427a67e4988aa78b669e11421d1de3e862e098074bdf53557e088ba068e","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:35:15.925959Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2501.01311/citation-record","integrity":"/paper/2501.01311/integrity","json":"/paper/2501.01311/citation-record.json","paper":"/paper/2501.01311"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2005.00928","last_updated":"2020-05-31T16:59:40Z","snapshot_observed_at":"2026-08-10T11:20:26.295031Z","submitted_at":"2020-05-02T21:45:27Z","title":"Quantifying Attention Flow in Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00928","snapshot_observed_at":"2026-08-10T22:35:15.813632Z","title":"and Zuidema, W","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.813632Z"},"links":{"cited_paper":"/paper/2005.00928","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:fd19ef07755fe653fb16e92f65f4d29ca7e1f602e87887a3f5be61d72d48f53e","observation_id":"ef8a1ff5-13c1-4621-b198-1489892893c5","resolution":{"observed_at":"2026-08-10T22:35:15.813632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-10T22:35:15.839399Z","title":"Dovonon, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.839399Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:6891e5b5fb8901bbf733c935fbf65746dbc2509a174fba311610ea6c2724db95","observation_id":"039a9a99-d538-48b3-84ee-0716463b9f64","resolution":{"observed_at":"2026-08-10T22:35:15.839399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10186","last_updated":"2019-05-08T18:05:56Z","snapshot_observed_at":"2026-07-06T07:35:40.542174Z","submitted_at":"2019-02-26T19:59:15Z","title":"Attention is not Explanation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10186","snapshot_observed_at":"2026-08-10T22:35:15.854758Z","title":"Jiang, P.-T., Zhang, C.-B., Hou, Q., Cheng, M.-M., and Wei, Y","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.854758Z"},"links":{"cited_paper":"/paper/1902.10186","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:28526dc4911b3a91da00ba3c9eb86886ad83d9da7c75efc2ee32a8d20cd2d0b3","observation_id":"5da95ff5-5469-433f-8d45-fa0b7ebbd2b4","resolution":{"observed_at":"2026-08-10T22:35:15.854758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.08593","last_updated":"2019-09-11T16:26:37Z","snapshot_observed_at":"2026-08-05T14:31:02.514313Z","submitted_at":"2019-08-21T04:27:38Z","title":"Revealing the Dark Secrets of BERT","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.08593","snapshot_observed_at":"2026-08-10T22:35:15.863728Z","title":"Lee, C.-Y ., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.863728Z"},"links":{"cited_paper":"/paper/1908.08593","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:dcd4092ba983dce53a6ebffb05fe83c2cdd7c2551192b705c94f738b17f1bda2","observation_id":"29029c4b-889d-4cb1-8078-ea37f5efb5a2","resolution":{"observed_at":"2026-08-10T22:35:15.863728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07874","last_updated":"2017-11-25T03:53:32Z","snapshot_observed_at":"2026-07-06T05:43:42.722222Z","submitted_at":"2017-05-22T17:38:10Z","title":"A Unified Approach to Interpreting Model Predictions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07874","snapshot_observed_at":"2026-08-10T22:35:15.878800Z","title":"A unified approach to interpreting model pre- dictions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.878800Z"},"links":{"cited_paper":"/paper/1705.07874","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:ce94729554a34874822a7ddc62e2a8a074ecd0e4e720783b21a1cc2f79d064b8","observation_id":"67b32edf-9d44-4a64-983a-4e68ffe09c2e","resolution":{"observed_at":"2026-08-10T22:35:15.878800Z","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":"2026-08-10T22:35:16.335794Z","title":null,"venue":null,"work_id":"adc6014f-88ff-44e2-8114-2a574832c8cc","year":2020},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.888159Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:eb3e9109ead41fc151a53eb4bdec20754da1eb787dc52959a8aefec5822666df","observation_id":"e2ac3199-947c-4da1-99fa-2d7d269701b4","resolution":{"observed_at":"2026-08-10T22:35:16.340324Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.92066","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:35:16.107990Z","title":"2020.9206626","venue":null,"work_id":"fb50dc7e-a684-4bd9-8c45-591fee021cc6","year":2020},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.892142Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:5fc688d7b66bba906016c197a3cb0fb0f2e954bb70d0547b5ea2e915306bde03","observation_id":"a36ed37e-3057-4d86-adc5-60d34530d2dd","resolution":{"observed_at":"2026-08-10T22:35:16.121126Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06104","last_updated":"2021-03-12T15:25:47Z","snapshot_observed_at":"2026-07-06T10:48:39.433147Z","submitted_at":"2021-03-10T14:58:31Z","title":"U-Net Transformer: Self and Cross Attention for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06104","snapshot_observed_at":"2026-08-10T22:35:15.896781Z","title":"Petsiuk, V","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.896781Z"},"links":{"cited_paper":"/paper/2103.06104","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:8f1fa06e0108176af19ec01056bfae3f9fadac673950b48f31b715544a704ded","observation_id":"e3d2304d-9a1a-4af3-9231-522321603566","resolution":{"observed_at":"2026-08-10T22:35:15.896781Z","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":"2026-08-10T22:35:16.323100Z","title":"U-net: Con- volutional networks for biomedical image segmentation","venue":null,"work_id":"84f6cdc2-21e3-4da7-9a23-4d1c72799e13","year":2015},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.902458Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:7a837fb6846b467a5b7a4838377ec7c25b3236a79a4b0597be241e933c3d031d","observation_id":"a23d2f4e-fcbf-4c3c-8e43-88e8769e1f92","resolution":{"observed_at":"2026-08-10T22:35:16.327967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-10T22:35:15.912028Z","title":"Graph attention networks.arXiv preprint arXiv:1710.10903,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.912028Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:64dc9391cef90165c23ccbbb7bca6f30c3e131d9d5ffbee3375bd1412c7b87ee","observation_id":"6fb56f4b-4531-4f37-9331-2634f2dac1cd","resolution":{"observed_at":"2026-08-10T22:35:15.912028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.03064","last_updated":"2023-06-09T08:32:06Z","snapshot_observed_at":"2026-08-05T23:52:09.115246Z","submitted_at":"2022-11-06T09:06:16Z","title":"ViT-CX: Causal Explanation of Vision Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.03064","snapshot_observed_at":"2026-08-10T22:35:15.916304Z","title":"Yang, J., Shi, R., and Ni, B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.916304Z"},"links":{"cited_paper":"/paper/2211.03064","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:df9067af831059c7ee3cd0a87260c37f98bb2b3c15e9f5d8baf8e97c07995596","observation_id":"28d128f5-59cc-4ab4-b345-e2fb8e9028a2","resolution":{"observed_at":"2026-08-10T22:35:15.916304Z","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":"2026-08-10T22:35:16.309364Z","title":null,"venue":null,"work_id":"9019d89f-4c47-4559-8740-f17b5a380926","year":2018},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.921969Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:f6c3b3eb0b1f2b74e40709066c865068b6cfca142a588d75425a5d3614f585f5","observation_id":"73ef1581-4cbc-4cd5-bf6a-906968343f2d","resolution":{"observed_at":"2026-08-10T22:35:16.314086Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.5185","last_updated":"2014-09-25T05:03:06Z","snapshot_observed_at":"2026-08-02T05:45:44.371732Z","submitted_at":"2014-09-18T04:08:25Z","title":"Deeply-Supervised Nets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.5185","snapshot_observed_at":"2026-08-10T22:35:15.869144Z","title":"Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.869144Z"},"links":{"cited_paper":"/paper/1409.5185","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:c26dcce7b362688628d9df7606b7eab884d946bd0c04a989ee6361a25ba02a81","observation_id":"361c98b7-8f68-4fd8-843a-bd56045ad458","resolution":{"observed_at":"2026-08-10T22:35:15.869144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-10T22:35:15.848884Z","title":"Hu, J., Shen, L., and Sun, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.848884Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:095871cf451038fd30d14f2d3d3943a8755a633201a104d589204b90270da207","observation_id":"0eee5bfe-8f54-4919-95d9-c1969a1e00bb","resolution":{"observed_at":"2026-08-10T22:35:15.848884Z","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":"2026-08-10T22:35:16.294498Z","title":"Therefore, a key consideration is how to introduce residual links within these frameworks to seamlessly in- tegrate MHEX","venue":null,"work_id":"62e4d436-c31b-46d1-a7ba-114d49f8fe2c","year":2017},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.925959Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:e7314b864ee5c69072e60039ab196c8c57048680a3bceb6e38242cf9588f7cd5","observation_id":"3372a26c-f7da-4f3f-ab17-e5f518537cce","resolution":{"observed_at":"2026-08-10T22:35:16.299673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.02178","last_updated":"2022-03-04T17:17:31Z","snapshot_observed_at":"2026-07-06T11:54:38.041673Z","submitted_at":"2021-10-05T17:07:53Z","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.02178","snapshot_observed_at":"2026-08-10T22:35:15.883998Z","title":"and Rastegari, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.883998Z"},"links":{"cited_paper":"/paper/2110.02178","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:1f5b712a7b8f4d8c8dea36a88c521c2ea9e58651095cb0d7768cba555a6a2d3b","observation_id":"735fbb2e-1eb5-4626-bc0e-1cc0bea9ff6e","resolution":{"observed_at":"2026-08-10T22:35:15.883998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.10130","last_updated":"2018-02-25T13:43:49Z","snapshot_observed_at":"2026-07-06T06:20:59.047515Z","submitted_at":"2018-01-30T18:28:30Z","title":"Spherical CNNs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10130","snapshot_observed_at":"2026-08-10T22:35:15.828911Z","title":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.828911Z"},"links":{"cited_paper":"/paper/1801.10130","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:f337a17682431c04825407f4aba8f89eaae54eccee534c6a3b153d0ce9bdc8ef","observation_id":"0b738811-83a7-4f0a-b36d-90a4cba1e021","resolution":{"observed_at":"2026-08-10T22:35:15.828911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04341","last_updated":"2019-06-11T01:31:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-06-11T01:31:41Z","title":"What Does BERT Look At? An Analysis of BERT's Attention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.04341","snapshot_observed_at":"2026-08-10T22:35:15.823846Z","title":"Cohen, T","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.823846Z"},"links":{"cited_paper":"/paper/1906.04341","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:2038377b83fce7586547ce0ba5111309a3a9b98b768debb3ff3334a1f17fa37c","observation_id":"1891327e-1e39-4cf5-a3eb-3cdbf56d11ef","resolution":{"observed_at":"2026-08-10T22:35:15.823846Z","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":"2026-08-10T22:35:16.348845Z","title":null,"venue":null,"work_id":"892611f1-04e5-421d-984a-cca39ebe68d4","year":2018},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.819775Z"},"links":{"citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:1ee94b3f58bb4b4359cdfaa91a40e21215bc4fdd629113d6a930d8c5b32d6c76","observation_id":"fb30a7a3-9159-4bea-8b61-d3ee91a9544d","resolution":{"observed_at":"2026-08-10T22:35:16.352645Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-10T22:35:15.859447Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.859447Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:45846e2c3dd20d4cbb45fb4faff2753fb4fe8b8e22ceb891daa888a1242b9bf5","observation_id":"9ede84f7-ac27-437d-af5a-0d57c57591d5","resolution":{"observed_at":"2026-08-10T22:35:15.859447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.02376","last_updated":"2022-07-06T00:56:06Z","snapshot_observed_at":"2026-08-09T03:41:24.668385Z","submitted_at":"2022-07-06T00:56:06Z","title":"A Comprehensive Review on Deep Supervision: Theories and Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.02376","snapshot_observed_at":"2026-08-10T22:35:15.874330Z","title":"Liu, Z., Mao, H., Wu, C.-Y ., Feichtenhofer, C., Darrell, T., and Xie, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.874330Z"},"links":{"cited_paper":"/paper/2207.02376","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:4795088ad3df13bd8bce2b5ca717312289c034153d74c2abc6f06e0e612b8484","observation_id":"9b6f45f6-6ae1-4c99-b7e5-3acd1c70cb5f","resolution":{"observed_at":"2026-08-10T22:35:15.874330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6806","last_updated":"2015-04-13T07:58:17Z","snapshot_observed_at":"2026-08-08T13:18:11.819437Z","submitted_at":"2014-12-21T16:16:37Z","title":"Striving for Simplicity: The All Convolutional Net","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6806","snapshot_observed_at":"2026-08-10T22:35:15.907715Z","title":"T., Dosovitskiy, A., Brox, T., and Ried- miller, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.907715Z"},"links":{"cited_paper":"/paper/1412.6806","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:955c274ebb13db629666207bcebf7fcb6b09034b4c48d8dc00c1a9b70c449926","observation_id":"597d01d7-6596-4cba-814c-57b40b247721","resolution":{"observed_at":"2026-08-10T22:35:15.907715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04301","last_updated":"2024-07-27T07:28:03Z","snapshot_observed_at":"2026-08-09T01:08:04.784978Z","submitted_at":"2024-01-09T01:19:03Z","title":"Setting the Record Straight on Transformer Oversmoothing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04301","snapshot_observed_at":"2026-08-10T22:35:15.843954Z","title":"He, K., Zhang, X., Ren, S., and Sun, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-10T22:35:15.843954Z"},"links":{"cited_paper":"/paper/2401.04301","citing_paper":"/paper/2501.01311"},"observation_digest":"sha256:ffa41b54e4352e3ac9846460d8cdc737c3ac3d41c3cd51c65dd3ce285f1de621","observation_id":"a4ac5581-2b2d-4c4d-a9d7-8794ee848302","resolution":{"observed_at":"2026-08-10T22:35:15.843954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.01311","last_updated":"2025-01-13T12:42:14Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T07:42:07.068906Z","submitted_at":"2025-01-02T15:47:56Z","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":23},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2501.01311."}