{"as_of":"2026-08-09T23:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c4b08a855689ad3c76aac2ab45e497cd81596ebb94b6069db28381a421e50399","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T07:28:39.214015Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.05263/citation-record","integrity":"/paper/2607.05263/integrity","json":"/paper/2607.05263/citation-record.json","paper":"/paper/2607.05263"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:68dc20706a3102cad8c88ed9c0474dfa05620d10bd0be263538778b895225c33","observation_id":"653cef4f-346f-49ca-9eab-02845cf6c2d4","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:f49cf7909d0e9bf635a2fa1bf1e89b52d5256a8b771032893b14f88930ea9b23","observation_id":"8445702c-6e93-4db8-9b28-4451e62556bc","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF International Con- ference on Computer Vision","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:997c419ccbcdc8f155296c4e73802751ba366aed93b38d88a33a9ada09b5fe74","observation_id":"cc95590a-73f3-4cc9-8464-cfa4401c94d1","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Mathematics of computation87(314), 2563– 2609 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:ee002e4fb63da1ff0793d1b2c571db7e57498e181e6726e6a7d58fcb97d7368c","observation_id":"0df521bc-9414-45e8-a608-06cce77e0344","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:74c74922911e97cea9549c12a2a21b70442dfc792005d2ebf50f969a006d0426","observation_id":"068e9618-1011-401b-a831-e27910a50dd5","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Journal ofthe royalstatistical society: seriesB (method- ological)39(1), 1–22 (1977)","venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:8b8ba4089a2a510e512837ecac4f4e80ccb1c3a257b35234d57bc7df50c7758c","observation_id":"318ab334-db8f-4f00-9f1c-d3ae9fdf6209","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:4a35aba89fd9a5bd9736e808659f4a1910f5e0c1c2eff9a52652f7174c8e12c0","observation_id":"41948b1d-2805-4a73-a877-18792a768c0b","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:2cf92d59c10fe61d6d7a2dfda74403ccdebe186fdd5656483a66f2d7538800b9","observation_id":"5c03277a-0735-4649-b669-0cf24ea0a511","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:77cc790db3e772bf899a270dff1b808f7f9275c6eba532e939b0cd14bdb44dbf","observation_id":"6dd973ea-a201-4100-9897-c6bb1fc8f2c1","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:1b11a5d1a78596d0158c5a0b3455b1b3a459b723c33a0ed2cdb9f3ab5d385a65","observation_id":"cf617292-5430-46a8-8ff6-8b126f68d366","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-07-11T07:28:39.214015Z","title":"arXiv preprint arXiv:1412.6980 (2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:b45394bc2f35c96e2545285767b2d9095a43b64dc578c8ba256fc674c7b376e0","observation_id":"45ea3d6f-ca50-438e-a5cb-3197fec7b49e","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-07-11T07:28:39.214015Z","title":"arXiv preprint arXiv:1312.6114 (2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:3b3d7f1a0ebafa8fbad9a14c8559b8491093606979e9b0bd5e33bd8104c86727","observation_id":"4a4df356-cc53-458b-91f0-777d5a4dd4ff","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-07-11T07:28:39.214015Z","title":"arXiv preprint arXiv:1609.02907 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:ee1045b6de795b58fdd385c85ea5868171f353c29eb7b0b8114399c6439e5739","observation_id":"06ac8f96-8255-4abb-9c8c-ebbe59c7a713","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:2b517fb552ee0694335d05f17b8eb5512e44fcda950cae4e0eb788e8f63c24cd","observation_id":"a992af5c-05a9-460a-90e5-7b74095a1b98","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Naval research logistics quarterly2(1-2), 83–97 (1955)","venue":null,"work_id":null,"year":1955},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:e8568613b8a3ed4b97e3c2640d5d1293f2914b768cd01581553a297bd02639b2","observation_id":"42672d4b-0bb2-4a4b-a15c-6bbf03b93630","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:d06df0f31f5448051fe2552cdd739f6593ef2222248d479918a885b0ea614bfc","observation_id":"b6d51da2-8438-4133-adf3-d594ffc448c8","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:8bf2e8e067577ace3638207e57bd31006961bdbda582cb2945242b0494c1b046","observation_id":"9eeb31f6-fd08-47ce-85ae-6f5ff01c4b0b","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:153a1972083d4aeefdb697ea476eddd3236919c3fc91ebd818febdc56276dbdc","observation_id":"a04539b2-8ef9-4113-bcc3-65ffac8b77f5","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:713a14edb5def61bd26b594595d03c6cc9db9b69fc244d533a1390023acd8ab2","observation_id":"b50cdf40-c99f-4cce-a180-bca67367a225","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Advances in Neural Information Processing Systems36, 18046–18075 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:4333a30edca4248b52c8bbec3a5878a2ec3e7313484f6b6a9d57483e02f5136e","observation_id":"230bdcb1-43c7-40cd-a597-28de2b609a97","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00319","last_updated":"2019-08-27T00:31:41Z","snapshot_observed_at":"2026-08-09T11:57:33.841446Z","submitted_at":"2018-09-30T04:51:27Z","title":"Modeling Uncertainty with Hedged Instance Embedding","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00319","snapshot_observed_at":"2026-07-11T07:28:39.214015Z","title":"arXiv preprint arXiv:1810.00319 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"cited_paper":"/paper/1810.00319","citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:85e750b16e7583948b5e2949f67bfd82b693952bdf8b67f9dbe61a5e75585d79","observation_id":"aa382ef4-2908-4dc7-b646-ab7f862a2a2e","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: International conference on machine learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:68e858d659a5c351d01ba989d56ff7379752ff2a6a3d338d32caa2d54338b513","observation_id":"ffed587a-8243-4892-8b24-f9b10e89a24e","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Engineering Applications of Artificial Intelli- gence148(2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:00affdb1328829b388b194a7cac7ee1504ce137a59e4b099a635b9ed8a64979f","observation_id":"4629c125-389f-4470-8826-52d628d8f551","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:5a2661106e2480f7bc5c079a49cec832ec942ad04de50220b7402f79661bd080","observation_id":"4ee10301-2dbd-425a-9c58-cb0efb2c5c94","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:b6fd8f25c59dbae82447fe0b5871e991287ae3af825eef8a84fe2a97bfb13212","observation_id":"3b81a572-f004-4c5f-97d2-1bedfa083fc6","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF international conference on computer vision","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:b634d4630122d14910d430b70fabcd97544c854f9287d7383aa6adc3dc543536","observation_id":"0915f93b-596a-42e4-8ffa-f215f136b741","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the AAAI Conference on Artificial Intelligence","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:e351429d10384ceec2af72f8d1d93497ef45db703e0fb5184bdb783d726a4045","observation_id":"dbc194e9-768b-41bb-ae10-720c4f7a5c73","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:95511ecbe9d9feb4df7cea568d1c3e82687468a65de0c3a3a394311b1a6e5d25","observation_id":"2d826ac1-e0cb-4a66-9733-c44584a0a805","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: European Conference on Computer Vision","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:c1fd6c60a0a0d0dfabbc3d7a16536a380c3e477d574bc0db1eabb80bd18ee6c2","observation_id":"189623b4-40ed-4682-a109-a7faba792ed7","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: 2021 IEEE International Conference on Image Processing (ICIP)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:ce144f6171a4af6c6b38890cd8c33888507e6d50f6ea27cc5e0c26c12908c5c0","observation_id":"e235ccec-37ad-4825-8a3e-de6bc4cc527c","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Notes of Course at University of Cambridge3(2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:83bc13419ec3d5a178de55e1329540fb0db6ce42c1e8805dd983e5e4e483ed0b","observation_id":"c693bf03-09d3-47c2-9e14-d0c4eb200f5d","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:00653aa3240254a245d91b3645350697c752da5ec22e5797b690c186ab93fbe2","observation_id":"6dac41e1-51e4-4a6c-ba27-d4b538b0557a","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"Advances in neural information pro- cessing systems30(2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:14baa9775a9b0895d1c58b30e3086e1419d5de5b2a841fa79792e4949672a816","observation_id":"a36a8543-0e8a-4c5f-961a-4ee3b040e1fb","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:26f4e882ff34e176ac1d12b43964bb9626bf3a91011ad9205d554fa6aec4c51e","observation_id":"975f4874-f1f2-4dad-8f72-b5af7d6ddbe4","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6623","last_updated":"2015-05-01T10:14:58Z","snapshot_observed_at":"2026-07-06T04:04:18.621124Z","submitted_at":"2014-12-20T07:42:40Z","title":"Word Representations via Gaussian Embedding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6623","snapshot_observed_at":"2026-07-11T07:28:39.214015Z","title":"arXiv preprint arXiv:1412.6623 (2014) 18 S","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"cited_paper":"/paper/1412.6623","citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:3ab87f908ed3960aa479b7a122f772fefd66ed270d2ae4a7798e789676eea4ed","observation_id":"51fc13e7-cd63-42db-845c-90ea77e85b3d","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Pro- ceedings of the IEEE international conference on computer vision","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:df4c7509da3fff9d039eda39444ff2b8bd184e09c42fdf1f1b11e9f440faa475","observation_id":"883bd7b8-b6ce-41e4-bd93-554e5194dbbe","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the European conference on computer vision (ECCV)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:b527e92cff0d896d3563a8017cfc6330b6849496f7e499b96ff3318e0129191e","observation_id":"97b8386e-2eca-4c94-8e3f-00b4021613fa","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:6f0a0e225f384184e23172a9fdc7f26b535c9cf96b57ef202b0d98575d16ff74","observation_id":"7d1a184c-dde9-4f85-8c55-84b4cb535bce","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"watching","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:740ebf6d39514be2e337f483ecb7367cf9df501deec58756f050ff26bf505cba","observation_id":"b22b5487-4308-4e37-b919-e1f4b27cdcd0","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: European Conference on Computer Vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:e8dc5b943c8cb051aa3f807f7e77bfc97788b22e17658e7225f6879d99d1e697","observation_id":"aab2edbd-9621-46ba-9346-b35ad55a5ba6","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","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":"2026-07-11T07:28:39.214015Z","title":"In: Proceedings of the IEEE/CVF international conference on computer vision","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-11T07:28:39.214015Z"},"links":{"citing_paper":"/paper/2607.05263"},"observation_digest":"sha256:074a95a826c390b32c3e79bccb38a1b04ace4b6e5f7b236589cb49df87cd190c","observation_id":"18afc0ae-6955-4333-aeab-3cfe774a21c5","resolution":{"observed_at":"2026-07-11T07:28:39.214015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.05263","last_updated":"2026-07-06T16:10:25Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T11:57:46.076203Z","submitted_at":"2026-07-06T16:10:25Z","title":"Learning Probabilistic Embeddings for Unsupervised Action Segmentation"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":41},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2607.05263."}