{"as_of":"2026-08-20T20:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:41d8793d9adacc1cf97c5974317c643574223d001ae07d3e468574e8de133538","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:51:05.018037Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2506.16297/citation-record","integrity":"/paper/2506.16297/integrity","json":"/paper/2506.16297/citation-record.json","paper":"/paper/2506.16297"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:51:05.865546Z","title":"Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning Research, 20(184):1–25, 2019","venue":null,"work_id":"86c80800-d606-4c49-ad6b-457a82945762","year":2019},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:00.896161Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:59dc4c8c6b351ab338214dfdf8ced248871102c667de058bce064f75f0122803","observation_id":"7acbe35d-ba62-45bd-a396-e9607bcb6747","resolution":{"observed_at":"2026-08-06T23:51:05.871495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00451","last_updated":"2018-06-01T17:16:56Z","snapshot_observed_at":"2026-08-16T06:32:06.888652Z","submitted_at":"2018-06-01T17:16:56Z","title":"Do CIFAR-10 Classifiers Generalize to CIFAR-10?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00451","snapshot_observed_at":"2026-08-06T23:51:00.975217Z","title":"Do cifar-10 classifiers generalize to cifar-10? arXiv preprint arXiv:1806.00451, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:00.975217Z"},"links":{"cited_paper":"/paper/1806.00451","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:d511118856f51b6f59262a81017434966f413a19988f10469c3dd4204a7f6637","observation_id":"0c0f357e-972f-419c-a73f-0f57b7b8df68","resolution":{"observed_at":"2026-08-06T23:51:00.975217Z","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":"2026-08-06T23:51:05.848751Z","title":"Intriguing properties of neural networks","venue":null,"work_id":"b1c1bc96-d70b-4403-ba2d-dd095fd5b07f","year":2014},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.070316Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:46cf7d32b1af0f768e81851e5d2e123b6bc81584a867b87685d9a51d089737c8","observation_id":"23c95516-3f85-4284-a80d-0b8a86b860a6","resolution":{"observed_at":"2026-08-06T23:51:05.854193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-19T07:17:20.004918Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-06T23:51:01.136616Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.136616Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:8d9089b905fb26d3daa34b0b47bef9c15c62fc6741926de8f4f58d74f42fa73e","observation_id":"f73d6cdb-474b-45fc-a5ef-dea1f7dc4e47","resolution":{"observed_at":"2026-08-06T23:51:01.136616Z","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":"2026-08-06T23:51:05.830872Z","title":"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images","venue":null,"work_id":"fa1f9117-94b4-4151-991c-5747173646b8","year":2015},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.226131Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:6d2db3acd6d63a5a6f912df79228eacf383513f9d6a44d2bd4f9f3668d364763","observation_id":"a5de8276-b247-4feb-966d-55802467e5b1","resolution":{"observed_at":"2026-08-06T23:51:05.835814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.814035Z","title":"Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary","venue":null,"work_id":"72df23bc-330a-43ab-9d4b-e051dbc8fe88","year":2022},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.353358Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:779f10c35322e7c0a7ef97493203be3ee658ad03e57ddcb28d704f2738e8f5bd","observation_id":"74687e42-942d-44d4-aaf9-dbf7df7c013e","resolution":{"observed_at":"2026-08-06T23:51:05.819687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.796657Z","title":"Thermometer encoding: One hot way to resist adversarial examples","venue":null,"work_id":"e9e38408-f729-47cd-9ad8-0449d6285735","year":2018},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.446647Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:49094c55f9d6789d4567047f9e47f9a6d0736b43f0a364f6c4e0ccde9be9152c","observation_id":"92f01872-d175-4cf5-b10f-1658c2c74bad","resolution":{"observed_at":"2026-08-06T23:51:05.802401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.781484Z","title":"Adversarial risk and the dangers of evaluating against weak attacks","venue":null,"work_id":"588f07b0-d0a1-4172-a4fa-415b02b6d33d","year":2018},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.515628Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:fcf1ff660932f071c40190fb4272dc98e5681522d82732fe4d6eb1989d53c87b","observation_id":"be2ff2d4-ae86-46eb-9090-f01274fdf0e8","resolution":{"observed_at":"2026-08-06T23:51:05.786575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-06T23:51:01.624137Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.624137Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:88edd54aca7e43313e712cf297c3238fa4d5ed75ed5ed708d8f915a74937368f","observation_id":"b0e48641-00a6-4319-8845-4cdc7483e250","resolution":{"observed_at":"2026-08-06T23:51:01.624137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01155","last_updated":"2017-12-05T23:45:08Z","snapshot_observed_at":"2026-08-19T19:49:58.259825Z","submitted_at":"2017-04-04T18:56:53Z","title":"Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01155","snapshot_observed_at":"2026-08-06T23:51:01.726015Z","title":"Feature squeezing: Detecting adversarial examples in deep neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.726015Z"},"links":{"cited_paper":"/paper/1704.01155","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:fc442a1251dd9dce003ad57b4f6bb451b97cfe3a127f6462fcd565f5007e21be","observation_id":"028f5258-0ba6-4f5b-8c41-6bf3ee0d64aa","resolution":{"observed_at":"2026-08-06T23:51:01.726015Z","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":"2026-08-06T23:51:05.765495Z","title":"Adversarial examples are not easily detected: Bypassing ten detection methods","venue":null,"work_id":"20648e9d-5f01-4ec8-afed-e85d8ca815c5","year":2017},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.831942Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:317ff0046e077a7b623d1683355580c7ca83f1d4fb0d39d24d4e55218e182e09","observation_id":"d0d05632-a983-436b-9924-53a1e905544b","resolution":{"observed_at":"2026-08-06T23:51:05.770961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.749728Z","title":"Continual general chunking problem and syncmap","venue":null,"work_id":"0590d9aa-909c-420e-b507-b19e2e5dcd48","year":2021},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:01.920680Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:c9ccb08e088fef110b6e84b4437932349b1823c228eabd878fbb5a47c6c491f0","observation_id":"24561953-333c-4f6d-bd5b-12a94764962a","resolution":{"observed_at":"2026-08-06T23:51:05.754568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.734258Z","title":"Unsupervised universal image segmentation","venue":null,"work_id":"352a5e3e-aba5-4c4f-972d-a2612e623258","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.013287Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:9790ed369d1c7640a710d2771ebb2ad702b6c95a118dd4d65fbb1c6cb35f71ab","observation_id":"b96ee7dd-8b5b-4ab1-8526-d7f1ad1cf056","resolution":{"observed_at":"2026-08-06T23:51:05.739160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.08414","last_updated":"2022-03-16T06:08:47Z","snapshot_observed_at":"2026-08-20T09:01:14.337738Z","submitted_at":"2022-03-16T06:08:47Z","title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.08414","snapshot_observed_at":"2026-08-06T23:51:02.100075Z","title":"Unsupervised semantic segmentation by distilling feature correspondences","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.100075Z"},"links":{"cited_paper":"/paper/2203.08414","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:59c07c5802b710710cb1d157cbe80ed41ffbbc7c10a4d16c4b91f48309d5325c","observation_id":"fd1b8b54-9614-40da-b040-47d36cf85f62","resolution":{"observed_at":"2026-08-06T23:51:02.100075Z","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":"2026-08-06T23:51:02.167939Z","title":"Comaniciu and P","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.167939Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:07ec61b70e0106c60869b87788acb185c524d84cdb8229cdab2ebb91e8527673","observation_id":"dd3971c2-f36c-485d-a6f5-70d3494c533c","resolution":{"observed_at":"2026-08-06T23:51:02.167939Z","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":"2026-08-06T23:51:02.265617Z","title":"Efficient graph-based image segmentation","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.265617Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:2fce3dbdd0092b647d1d1902c0f43f6fa5624e0f7ae16f04717f8bf780180626","observation_id":"1bdd33b2-d3f1-4502-bf20-b505f18df440","resolution":{"observed_at":"2026-08-06T23:51:02.265617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.08506","last_updated":"2017-11-22T21:06:13Z","snapshot_observed_at":"2026-08-17T19:30:41.628384Z","submitted_at":"2017-11-22T21:06:13Z","title":"W-Net: A Deep Model for Fully Unsupervised Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.08506","snapshot_observed_at":"2026-08-06T23:51:02.340446Z","title":"W-net: A deep model for fully unsupervised image segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.340446Z"},"links":{"cited_paper":"/paper/1711.08506","citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:759e57f2c4969775f7662fb6f79686f0633da052c2cb0751b3f0d5613d18baef","observation_id":"0361cb6b-b2a3-40a7-80e2-a45732bc3bc1","resolution":{"observed_at":"2026-08-06T23:51:02.340446Z","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":"2026-08-06T23:51:05.694133Z","title":"Unsupervised image segmentation by backpropagation","venue":null,"work_id":"97ff8b2f-1ce2-4e9d-a550-3b73e5e4105c","year":2018},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.423404Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:9b1ea7325e6ef417d85d21d3f877ae1fa7e4d9d4cae95a4f3bffd533e1fece46","observation_id":"1393cd05-ccc2-4367-ae97-f2029519505c","resolution":{"observed_at":"2026-08-06T23:51:05.699907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.675283Z","title":"Invariant information clustering for unsupervised image classification and segmentation","venue":null,"work_id":"13fa4a26-7ec1-4d6e-9a8e-f773c6f649c0","year":2019},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.471962Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:d459279b6ff45d9cd3cdaa8cd9b1234017db10ef2dbb3f619a89d8aef1cbacc9","observation_id":"a137f1ce-1036-41dd-ac85-6f37a0a2723b","resolution":{"observed_at":"2026-08-06T23:51:05.682023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.658716Z","title":"Unsupervised learning of image segmentation based on differentiable feature clustering","venue":null,"work_id":"7fa5b13e-3cb1-4a75-b949-f917eb05c441","year":2020},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.559215Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:2d073de95fd0980240769a99b59c918de229e28a9cbc1c54c50eb1d4b05dc652","observation_id":"048d0345-4137-4111-bc9f-b394c8ce103c","resolution":{"observed_at":"2026-08-06T23:51:05.663867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.641841Z","title":"Pixel-level clustering network for unsupervised image segmentation","venue":null,"work_id":"7899bf52-3dcc-4626-922e-5e8838b6163c","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.641870Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:4c162ad74c2ea0fa9a547a2afb3620debb52159c7e4ade29c65544caf0db2e0a","observation_id":"b20f6688-cfd7-47cf-9f5e-846609a3706e","resolution":{"observed_at":"2026-08-06T23:51:05.647203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.623563Z","title":"Two views on the cognitive brain","venue":null,"work_id":"c5b6fe21-044c-43ef-9814-768b7cbfa0d7","year":2021},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.770358Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:d952f971cacdcb30ebc850783bf72482fc4ee2918d9c74e25982f2fefbbc5964","observation_id":"083c7da4-d790-4a50-9d60-49af1dd433df","resolution":{"observed_at":"2026-08-06T23:51:05.628543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.606857Z","title":"Accurate estimation of neural population dynamics without spike sorting","venue":null,"work_id":"f25ad742-4d2c-4dbc-b547-9f54cbdb81f3","year":2019},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:02.885463Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:35ac6890f44381f00db6a008f2cb4c6352751363ada3d0f2c0dd7ad67a5d62c3","observation_id":"3575b037-b4d7-4cb2-8ca7-8ab220614b28","resolution":{"observed_at":"2026-08-06T23:51:05.612054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:03.046685Z","title":"The importance of mixed selectivity in complex cognitive tasks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.046685Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:a6fdf7cfa4fdc95d36a900db90b17ef5a3bf1bceef704a290034fc9ee6a94464","observation_id":"bf4894b3-b7c1-4e8c-a826-d86d672b0227","resolution":{"observed_at":"2026-08-06T23:51:03.046685Z","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":"2026-08-06T23:51:05.577959Z","title":"A survey on reservoir computing and its interdisciplinary applica- tions beyond traditional machine learning","venue":null,"work_id":"1d8f407e-3ebf-44b2-8884-60c81ee4edcf","year":2023},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.134527Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:0a325b0abd1149fc70f5fc797a985c9fb6903985c1ef19bd4d343ca44a0a3850","observation_id":"0a903ecb-ded0-4bec-a115-964a6038144f","resolution":{"observed_at":"2026-08-06T23:51:05.584303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.561918Z","title":"Smooseg: smoothness prior for unsupervised semantic segmentation","venue":null,"work_id":"7dde1942-e502-4cb2-b485-a726601cb5ab","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.243465Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:3194bf27003afb0ba16effa70e6c3673cec26e90f32747956b0040176beeb0b8","observation_id":"eb577673-6dfa-49b4-b56b-08d03644ae4b","resolution":{"observed_at":"2026-08-06T23:51:05.566794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.542656Z","title":"Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion","venue":null,"work_id":"5d24aeee-ec0d-4597-a0df-4c1d681d4de5","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.418150Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:f1296cebe078d29f6dee136ade1ccdcc934336560950ba2a41c3b521389e69b3","observation_id":"6b53f1fb-5931-48ad-9ef3-849204c50888","resolution":{"observed_at":"2026-08-06T23:51:05.550127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.523901Z","title":"Unsupervised semantic segmentation through depth-guided feature correlation and sampling","venue":null,"work_id":"3af5a593-c1ad-487d-ae53-4a5c737b1dec","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.480836Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:879128c4af4c2f63727f7771710b6fed223d7c5198eba512974c6609ce4cefa8","observation_id":"e68a8a73-1084-4cf9-8435-8412e9ff0192","resolution":{"observed_at":"2026-08-06T23:51:05.529940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.506007Z","title":"Dbscan revisited, revisited: why and how you should (still) use dbscan","venue":null,"work_id":"6f06726e-97a4-4cdd-ad75-d92df2709fde","year":2017},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.561626Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:ab97247eece2332a399c3fa582ea3d14bb25153f32f4e1521bf4e08185a1dca0","observation_id":"5d8da29f-60a8-4fd3-bcaf-45e3e6b9e3d1","resolution":{"observed_at":"2026-08-06T23:51:05.511974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.487885Z","title":"Algorithms for hierarchical clustering: an overview","venue":null,"work_id":"db18f619-2d80-461c-8323-7f1045e532f6","year":2012},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.690099Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:8769f6e9f32d1436c6bb070eb5014aa9b43395f51e4029c6b846cb9803874178","observation_id":"f8c7ccd2-9687-4ec3-a637-2c1f0f5ada06","resolution":{"observed_at":"2026-08-06T23:51:05.493563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.471452Z","title":"Symmetrical syncmap for imbalanced general chunking problems","venue":null,"work_id":"dc7172d8-67df-41ec-b069-edb011ccd8de","year":2023},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.815074Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:ad6776439d71c44090d2dea46769e6b45fa803704f70ed70df59abae5d1be2e5","observation_id":"637fa7e9-8ee2-4f18-a4a2-83175f93f741","resolution":{"observed_at":"2026-08-06T23:51:05.476614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.456652Z","title":"Gestalt psychology","venue":null,"work_id":"359f587a-9901-4629-95a9-2762021293d4","year":1938},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.901039Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:c83c41013b6d13c6c4bc257c34b261ac29861403c7a079bcad364d461ac5f3e3","observation_id":"1a41351e-8f6f-4c1e-a490-02fdafc1b6c0","resolution":{"observed_at":"2026-08-06T23:51:05.461344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:03.998351Z","title":"Principles of neural science, volume 4","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:03.998351Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:6fc45e3ac5552da19d0a1a2314d9386db0045fffe9f9857ca4fc323800181112","observation_id":"05fdf92a-3362-4b35-abd4-7accc0ab4329","resolution":{"observed_at":"2026-08-06T23:51:03.998351Z","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":"2026-08-06T23:51:04.118879Z","title":"echo state","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.118879Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:bad5409355e6ca9d1084443e3cea764a879f6490d4034445531e9abf5595fc29","observation_id":"3d42fa3e-a226-426a-af8d-86d4bbf93c27","resolution":{"observed_at":"2026-08-06T23:51:04.118879Z","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":"2026-08-06T23:51:05.420088Z","title":"Computing and visualizing dynamic time warping alignments in r: the dtw package","venue":null,"work_id":"5875eff0-4c6e-4ef5-84bb-11541f29638b","year":2009},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.169450Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:424ad713340271e5e53402aec869bd99647af1c3fca2c03183ef40bb40997af0","observation_id":"9c7a8957-fbdd-4889-b78b-39dc7c5d4a29","resolution":{"observed_at":"2026-08-06T23:51:05.425315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.404162Z","title":"The pascal visual object classes challenge: A retrospective","venue":null,"work_id":"ba96e170-ba24-4b1a-8bca-5b1676dae0b2","year":2015},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.304769Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:805ac008157d3d95b73fbf2712471366f85b6e445b39d9c628b37adb5d0ef898","observation_id":"a5552702-cd72-4aaf-8b1f-bf563d0884ae","resolution":{"observed_at":"2026-08-06T23:51:05.409427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.386898Z","title":"Contour detection and hierarchical image segmentation","venue":null,"work_id":"0cdf0eb0-38b9-4c27-bf18-8e231d9dec66","year":2010},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.412418Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:de6d5f6b0d6576e49374170f8b3a2fe0a2d4c346243a283d557204c9ac45b597","observation_id":"942a011c-7fc4-4e54-a42a-863892ea861d","resolution":{"observed_at":"2026-08-06T23:51:05.391770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.370655Z","title":"Dic: deep image clustering for unsupervised image segmentation","venue":null,"work_id":"4e3730b5-0d6a-4159-8a67-05b6c2510cbd","year":2020},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.481866Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:4b0916c4ae257eb9655230ff3c535bf1c0f390b46c535a1059becc1d42102b06","observation_id":"c8374169-b1c6-4d45-8b49-2bbd399d8457","resolution":{"observed_at":"2026-08-06T23:51:05.375521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.354964Z","title":"Segmentation using superpixels: A bipartite graph partitioning approach","venue":null,"work_id":"07a1abb4-27f8-40bf-9ebe-90ebed516323","year":2012},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.595855Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:1758fd86d2576bee3f5539c18f7e4e2f89ecd5c7e6bdbcf17179e6a7a040e13b","observation_id":"d03a7fcd-af36-4263-a026-26471f41bf9d","resolution":{"observed_at":"2026-08-06T23:51:05.360473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.339202Z","title":"Benchmarking neural network robustness to common corruptions and perturbations","venue":null,"work_id":"05b95627-040c-4e5e-ad38-c3d2997e9636","year":2019},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.682362Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:e0556ecf631db438fe03b3fe97c595889f97aa7355bc4a91795fee689f74b15b","observation_id":"eab5a732-409e-425a-93cf-bbec2d6c360d","resolution":{"observed_at":"2026-08-06T23:51:05.344449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:05.322980Z","title":"Dynaseg: A deep dynamic fusion method for unsuper- vised image segmentation incorporating feature similarity and spatial continuity","venue":null,"work_id":"9fc9f5d1-9968-4a86-884f-ab4f41e7d9e6","year":2024},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.792840Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:d8ea4f947a4adefc20c2bf738dc5f4fbc0ef80c655436cf8c5b7edbebaa66826","observation_id":"3740664c-282e-4220-9be1-5cadde9c57a3","resolution":{"observed_at":"2026-08-06T23:51:05.328022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T23:51:04.877665Z","title":"A practical guide to applying echo state networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:04.877665Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:e029937ab22e204917a44f34d1c8b6344c04a60a71a4192dc497423cd487a2ad","observation_id":"f9a90208-64e5-4cda-9e03-a5c2e9a771cf","resolution":{"observed_at":"2026-08-06T23:51:04.877665Z","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":"2026-08-06T23:51:05.185577Z","title":"Reservoir computing approaches to recurrent neural network training","venue":null,"work_id":"c62d745e-760e-4a4a-861e-5e24b46abaef","year":2009},"citing_paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:05.018037Z"},"links":{"citing_paper":"/paper/2506.16297"},"observation_digest":"sha256:71ec1abdc83ee102386444a6802a078cb9f7a54c6b7aff4e2acdf4cb2ffc8ef4","observation_id":"0bc84008-eb22-434a-9d88-f4d290712d84","resolution":{"observed_at":"2026-08-06T23:51:05.296062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.16297","last_updated":"2025-07-24T09:52:06Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T06:33:03.637249Z","submitted_at":"2025-06-19T13:17:30Z","title":"SyncMapV2: Robust and Adaptive Unsupervised Segmentation"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":43},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.16297."}