{"as_of":"2026-08-16T04:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7329b4f4a52836abcdfb69c9aa023a2aac061f7867f17130c1f4eef9e5a3393a","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T06:02:12.252510Z","state":"measured"},{"denominator":83,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":83,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2502.08231/citation-record","integrity":"/paper/2502.08231/integrity","json":"/paper/2502.08231/citation-record.json","paper":"/paper/2502.08231"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T06:02:11.886432Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.886432Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:96dd0682b8bbea00961d863f583504b29facf64751e821d133d02d33a4c1ea8a","observation_id":"a828c851-d477-4920-89b3-695201c93e07","resolution":{"observed_at":"2026-08-08T06:02:11.886432Z","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":"2026-08-08T06:02:11.891648Z","title":"On the Equivalence between Herding and Conditional Gradient Algorithms","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.891648Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1d192dbc389751eb5de38389ceca439f83e62c491aeb3190a3cba4695e1b149a","observation_id":"a423670c-7056-440f-8b14-e8bb0ea5bf12","resolution":{"observed_at":"2026-08-08T06:02:11.891648Z","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":"2026-08-08T06:02:11.896511Z","title":"Neural machine translation by jointly learning to align and translate","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.896511Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:01f7ff644cd3507d9da0bca095a40a4db0b8382a40360677533503d6d2333d13","observation_id":"649bc30a-453b-4093-a933-e0cedd1a110d","resolution":{"observed_at":"2026-08-08T06:02:11.896511Z","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":"2026-08-08T06:04:14.080053Z","title":"Riemannian adaptive optimization methods","venue":null,"work_id":"d34f43ad-a9fd-422b-9aff-cd8e2f7e2604","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.901758Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:e781a67e47207269b6ff6b5c7b2ff1bf463806b7a8c357a93c70dfe7692575b9","observation_id":"e83ba7b4-034f-43be-842a-902f7b01cb8b","resolution":{"observed_at":"2026-08-08T06:04:14.085068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22104","last_updated":"2024-10-29T15:02:15Z","snapshot_observed_at":"2026-08-16T00:05:37.780317Z","submitted_at":"2024-10-29T15:02:15Z","title":"Positive definite singular kernels on two-point homogeneous spaces","version":1},"cited_work":{"arxiv_id":"2410.22104","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.22104","snapshot_observed_at":"2026-08-08T06:04:13.213348Z","title":"Positive definite singular kernels on two-point homogeneous spaces","venue":"math.CA","work_id":"3826f0c0-9620-4cb6-977e-1a9cc8a9aa74","year":2024},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.906389Z"},"links":{"cited_paper":"/paper/2410.22104","citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:30ab5d722079ba02ef808dffb5dba60549ec5f854cc07ef63bd31e00e663ea31","observation_id":"c92de2d9-204f-4c9b-bdf5-603499d74d77","resolution":{"observed_at":"2026-08-08T06:04:13.218596Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:14.064451Z","title":"Spherical S liced- W asserstein","venue":null,"work_id":"f6d3e2d1-fe3f-47f5-aa68-9dd133990284","year":2023},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.910768Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:24fe1d87c6e9840d6ce91c57e2cc4a69c6d7e5c57b7365e53a7fc1e6ff82cbc0","observation_id":"7bfc9445-c62a-4b60-a795-4a91c06abb3e","resolution":{"observed_at":"2026-08-08T06:04:14.069422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:14.047220Z","title":"Stochastic gradient descent on R iemannian manifolds","venue":null,"work_id":"9d93dc29-b117-4c61-92d8-2aca8471bb15","year":2013},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.915704Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1fc0206a3815c40453c7e50862817be626296cefe384a38252e6254dbd3a209b","observation_id":"b29d56ce-0158-4c7d-9373-0ff53e9a4d2d","resolution":{"observed_at":"2026-08-08T06:04:14.053194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:14.029155Z","title":"Convergence properties of the KMeans algorithm","venue":null,"work_id":"dbe61dc1-b6cf-4e7f-aab8-43b39a34b73a","year":1995},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.919693Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:8bd470746dd70dd400b54cc733637614fb1df0229f86c3bc3310e07f40e7ee39","observation_id":"a8cfe7cd-38f1-4eb4-b532-f93a80600451","resolution":{"observed_at":"2026-08-08T06:04:14.034192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.04581","last_updated":"2016-02-15T15:12:44Z","snapshot_observed_at":"2026-08-14T22:22:59.200391Z","submitted_at":"2015-11-14T17:18:47Z","title":"A Test of Relative Similarity For Model Selection in Generative Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.04581","snapshot_observed_at":"2026-08-08T06:02:11.923609Z","title":"Blaschko, Ioannis Antonoglou, and Arthur Gretton","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.923609Z"},"links":{"cited_paper":"/paper/1511.04581","citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:7b3bbc81beffc7b40db2c581aa1bcb23ae38cbdfd221883d2d43ffff93cb7a7c","observation_id":"83b96450-26e1-48fc-afdb-be9491b4ae67","resolution":{"observed_at":"2026-08-08T06:02:11.923609Z","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":"2026-08-08T06:04:14.012596Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"d1441460-072e-406c-8b61-0b5bee3a1509","year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.927989Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b55044e9db46b83c43144ca022522f5d76eadff53a161c7b42499e869f944400","observation_id":"eef202c7-83a0-4a94-b13a-bf637a7599f2","resolution":{"observed_at":"2026-08-08T06:04:14.018173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.996025Z","title":"Big self-supervised models are strong semi-supervised learners","venue":null,"work_id":"907c62d2-030b-4aa4-a47d-b9862ae8e57d","year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.932025Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:abf5c487c8cd0834602f4ad4d79fc719a6a9d696eff655b94d4f3cfb26019eaa","observation_id":"bddc84eb-3716-46e4-884f-997e77a1d3a6","resolution":{"observed_at":"2026-08-08T06:04:14.000801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.981586Z","title":"Table of spherical codes","venue":null,"work_id":"c84e04bf-5033-454e-bba9-336a01ae9729","year":2024},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.936419Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:d3d693a469dd325f08682a81899180cfb05d74217a26613612e5de560e1edae6","observation_id":"8b003327-be4d-44e0-8384-3784db2c854e","resolution":{"observed_at":"2026-08-08T06:04:13.985999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.965693Z","title":"Sphere-packings, lattices, and groups","venue":null,"work_id":"6aac19bd-c7e6-4a79-9cc7-28c7fc3472af","year":1999},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.940783Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:01cfefc28da9e262bb1118703a83938cff4c31d343e17ec432e1b6c456fab283","observation_id":"e709204e-7ed0-40b7-8400-88a70854b6b2","resolution":{"observed_at":"2026-08-08T06:04:13.970623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:11.945029Z","title":"Finite point-sets on S^2 with minimum distance as large as possible","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.945029Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:a1df1058a7973c0e3cb47aa0de5eda5ef8a2bb9e89ddd4019ebf3cdd33c87b37","observation_id":"826d2829-2fc0-44ba-8aba-d71bbe4d11f0","resolution":{"observed_at":"2026-08-08T06:02:11.945029Z","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":"2026-08-08T06:04:13.949369Z","title":"The power spherical distrbution","venue":null,"work_id":"5bce7079-afe2-4cb6-9bf4-632edaed9057","year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.949476Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:07d0fcc0f7c70aec680198ae72de1ee1b1d48262303fd32270acca61bc0d44bc","observation_id":"2bce692e-2437-4db0-9021-c6a85ba729ce","resolution":{"observed_at":"2026-08-08T06:04:13.954669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.932080Z","title":"Uber eine Absch\\","venue":null,"work_id":"4782d81f-20a3-4cc2-bb13-bd035a0172b8","year":1943},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.954071Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:6b572732b5ca5ed8435d46055be391ebaac87d60f6954ecb25668be3fcfe1e36","observation_id":"71b606db-95be-4574-9f73-e230b7cf968f","resolution":{"observed_at":"2026-08-08T06:04:13.937741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.914619Z","title":"Geodesic exponential kernels: When curvature and linearity conflict","venue":null,"work_id":"8ac0ae1d-2b71-4678-9092-86001ffc8eb4","year":2015},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.958718Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:ad63849595e74f86a0fdef7ad08a67b4ef50dc0568dd2bdc7c5e5d7e817d07e2","observation_id":"b01950ea-c8c4-4506-b1ba-6e41396f61c2","resolution":{"observed_at":"2026-08-08T06:04:13.920395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.897810Z","title":"Principal geodesic analysis for the study of nonlinear statistics of shape","venue":null,"work_id":"1c28a7be-fe42-42a7-82fc-4263962b7a51","year":2004},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.962975Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b5214d468aa596f7bb0443534c519f5efb09accae6bb4eefa3261f8e80ec6bf1","observation_id":"89e56b62-eeb9-4e88-bd70-75a21b382776","resolution":{"observed_at":"2026-08-08T06:04:13.902424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.881869Z","title":"Minimizing a differentiable function over a differential manifold","venue":null,"work_id":"31ec4f65-2c0d-4ea0-aab9-155b4591bccf","year":1982},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.967543Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:e721c603249b3872a63cf7ecd162ef1c5b2598fe91eea3fff872e349f8bf7055","observation_id":"0436b8dc-3646-4bb9-b2b3-9855ca852650","resolution":{"observed_at":"2026-08-08T06:04:13.887080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.864763Z","title":"Representation degeneration problem in training natural language generation models","venue":null,"work_id":"24b502fa-944e-498f-959c-5a30e4d8c811","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.972445Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:20617546ca7bb59b78d5dbd65a384fff1b455e800be6136962f8ee0456a372e5","observation_id":"739cf760-388d-4ee9-8479-1dc29b06aeda","resolution":{"observed_at":"2026-08-08T06:04:13.869830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.847518Z","title":"A novel approach to the spherical codes problem","venue":null,"work_id":"2c1eba8b-4b66-4544-b7d8-34322e292a54","year":2013},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.976814Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:abb2754df8e2e2b95c75d61cfa71fb2487ea6f4ca523507b48d370c09a42b917","observation_id":"c11c35ae-e7bd-4ce5-b53d-2c53ecdb39c1","resolution":{"observed_at":"2026-08-08T06:04:13.852794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.830481Z","title":"Frage: Frequency-agnostic word representation","venue":null,"work_id":"be6a8870-6ae2-481d-ad76-889ce410e10b","year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.981148Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:95044bf58fa8f06e7a5ac1c0f59178f61a8685cbd8f411b6d6734a12eec8b6b7","observation_id":"c8445b52-32e4-4402-ae2c-09ee5de27660","resolution":{"observed_at":"2026-08-08T06:04:13.835994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:11.985580Z","title":"Borgwardt, Malte J","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.985580Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:2e627525697c4b4ee03b6a4bc1b16300795c3ae4b7dac4ebb910c63c4ad064b9","observation_id":"3efc5af2-2612-49fd-8787-53f0ecf8d0e1","resolution":{"observed_at":"2026-08-08T06:02:11.985580Z","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":"2026-08-08T06:04:13.803826Z","title":"Inequalities","venue":null,"work_id":"99ac7b4a-91f8-4178-a725-f6090201ae05","year":1952},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.989876Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:5ee34e83dbf53f1c2c7c4331541600020d1341525755211001f1db636348f2f5","observation_id":"6fc27e87-b1d8-4fce-bf77-42903e529fe6","resolution":{"observed_at":"2026-08-08T06:04:13.808618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:11.994342Z","title":"Momentum contrast for unsupervised visual representation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.994342Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:cafa516652e399efa2ada862b0561b773f915e5607ac3408b0f9e30c27320997","observation_id":"13394abc-afae-4ffa-9d87-bbaa07c512d1","resolution":{"observed_at":"2026-08-08T06:02:11.994342Z","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":"2026-08-08T06:04:13.776016Z","title":"Learning deep representations by mutual information estimation and maximization","venue":null,"work_id":"421108c6-dabc-4e92-940e-a4fcb936b3c2","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:11.998668Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:f130d2fb20128c8b58f74626b7bb76d326a59c6dd249a923ab51924e704861ff","observation_id":"4c1fe0c2-da46-4a0f-b9e6-784f8f0898da","resolution":{"observed_at":"2026-08-08T06:04:13.782013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.003019Z","title":"Probability inequalities for sums of bounded random variables","venue":null,"work_id":null,"year":1963},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.003019Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:296513b762ec88970c9ff0ac1d24d365a5feb83d3d3e92abe86da52bd542129e","observation_id":"9d69372b-e9d9-4f25-b300-98fd2e703a7b","resolution":{"observed_at":"2026-08-08T06:02:12.003019Z","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":"2026-08-08T06:04:13.758591Z","title":"Topics in circular statistics","venue":null,"work_id":"c5aadf9a-eb9a-46c5-9252-35f0ec6db985","year":2001},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.007541Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:7fd4aa657dd6453997baa86f716b28cf61d31d46a636e864fc9ce4e53f9302d3","observation_id":"3f0946d7-eb57-4bc0-afbb-a0af136f0181","resolution":{"observed_at":"2026-08-08T06:04:13.763844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.011983Z","title":"S entence P iece: A simple and language independent subword tokenizer and detokenizer for neural text processing","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.011983Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:ee4d35b2a9322203efbc10ac41f70d856761cf5dd2c1470a55d715e8d3aeb01d","observation_id":"03f4543c-3022-4e13-ad53-cef29a0a3f11","resolution":{"observed_at":"2026-08-08T06:02:12.011983Z","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":"2026-08-08T06:02:12.016575Z","title":"Determinantal point processes for machine learning","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.016575Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:8c1b80fdf63fcc840ee0884352fb17b5f62a2ea82f4f1b7e2873333056425926","observation_id":"d10d4d8b-b146-48da-b7a2-b2d3fe141598","resolution":{"observed_at":"2026-08-08T06:02:12.016575Z","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":"2026-08-08T06:04:13.739295Z","title":"von Mises-Fisher loss for training sequence to sequence models with continuous outputs","venue":null,"work_id":"195eeae1-34ed-41a9-ac8a-183223559f1b","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.021373Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:c7a7cc5ea5de355719f92c33b0054115d5271850d62185fbe4aff2f69b234aa4","observation_id":"a5155dc4-ddd3-4894-9e9a-fe05085eaecf","resolution":{"observed_at":"2026-08-08T06:04:13.744363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.026088Z","title":"Determinantal point process models and statistical inference","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.026088Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:31cf93cf790f44382804c79d2d422f742ad73ecab6dba76d40c08ad9e1eb1dda","observation_id":"15ff6b72-09de-4b11-ac90-27c7fdc0d2b8","resolution":{"observed_at":"2026-08-08T06:02:12.026088Z","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":"2026-08-08T06:04:13.723169Z","title":null,"venue":null,"work_id":"398da58d-5a2b-427d-9ba1-bd237011d43d","year":2015},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.030541Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:c33db136b4ab882dbc5ef16645ebdb1af53c4cd7dfeb10b387f19673067653bf","observation_id":"f53292c8-549a-4dbc-b438-4bd4f4a4dc7f","resolution":{"observed_at":"2026-08-08T06:04:13.728017Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jmva.2019.05.008","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Quantization and clustering on riemannian manifolds with an application to air traffic analysis","venue":"Journal of Multivariate Analysis","work_id":"5a57adfe-0888-450d-9651-17389d26fa69","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.035220Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:c0cb8e7c0e709d1d080fb7f60fcdf326a031012f4adc9f000b8264208f7dc563","observation_id":"b0923c39-d224-46cb-838c-4560d4b9d259","resolution":{"observed_at":"2026-08-08T06:02:12.387226Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.706507Z","title":"Sample estimate of the entropy of a random vector","venue":null,"work_id":"aa11aee4-c87a-4e41-8a7d-483b2cbd4fb6","year":1987},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.039796Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:f369de0fa7d049654652a84ff7c425ba0bc63f284e7f2f06952134d4314fbe3d","observation_id":"b334b0e7-b085-4952-8f63-0bcd3027adf3","resolution":{"observed_at":"2026-08-08T06:04:13.712345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.690905Z","title":"Minimisation des fonctionnelles d \\'e finies sur une vari \\'e t \\'e par la methode du gradi \\\"e nt conjugu \\'e","venue":null,"work_id":"3d47c2a3-d6c5-4257-ae31-edf619292a1f","year":1979},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.044459Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:a7dc88fb05f8ded9e63106c669f32a8877670e9b013ed94a65550d1cc8b2dee4","observation_id":"bc01c7b8-d4cd-40dc-9ee4-efad461b4767","resolution":{"observed_at":"2026-08-08T06:04:13.695812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.674560Z","title":"Riemannian stein variational gradient descent for bayesian inference","venue":null,"work_id":"ae9b8dee-b17a-4e82-a6ab-05392b38f07c","year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.049057Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:0257e971c0bee2325e0916b528387c0ad6ff2a1d3317d659c79311db77dd777a","observation_id":"fb6a51b4-0ab7-4635-969e-349e8f8bd28c","resolution":{"observed_at":"2026-08-08T06:04:13.679091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.053543Z","title":"Stein variational gradient descent: A general purpose bayesian inference algorithm","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.053543Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:66663c3476c8c9a405d677cdb47f2104cadb3b41ff8acb673d443fbbd5913f4f","observation_id":"d79f61ad-6c00-4190-b9fe-eb5deb15835e","resolution":{"observed_at":"2026-08-08T06:02:12.053543Z","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":"2026-08-08T06:04:13.648570Z","title":"Sphereface: Deep hypersphere embedding for face recognition","venue":null,"work_id":"05e0092e-5acb-44aa-bca3-1bca67b3ae4a","year":2017},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.058162Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:86adfd166e5be3cf0d59a94ceb6536854847e1a560f5a60b0b749ad2fe2e4955","observation_id":"8f431ad3-dd96-472d-933b-7e7f59464b8d","resolution":{"observed_at":"2026-08-08T06:04:13.653263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.631791Z","title":"Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song","venue":null,"work_id":"7001093f-cbfb-4add-9c7e-f8cb5d3e9a80","year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.062624Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b43a084036cc929e0044b25758cfa7d58d9c6b6c517a4a4faf7f004779e49127","observation_id":"9553a634-ed52-4dc2-b7db-a878765603cd","resolution":{"observed_at":"2026-08-08T06:04:13.636797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.615944Z","title":"Learning with hyperspherical uniformity","venue":null,"work_id":"1d2695a8-c614-4396-a207-99447ff407f3","year":2021},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.067286Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:784340c10720917dbf8387ae354c777c7722ac8002aa6fdc768f9a855b9dcb36","observation_id":"1c0a6eab-215b-4ddf-bd03-5ab8d78b464e","resolution":{"observed_at":"2026-08-08T06:04:13.620324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.072099Z","title":"Multilingual denoising pre-training for neural machine translation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.072099Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:56c918a5c598d8593d904285b3b3c5d667eecb1154470f312225cccb8fa3e4d4","observation_id":"35d3745f-c00e-4a7a-bfb5-bd3386824b23","resolution":{"observed_at":"2026-08-08T06:02:12.072099Z","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":"2026-08-08T06:02:12.076979Z","title":"Least squares quantization in PCM","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.076979Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:7b588487c770449971bdc1f0a77e0613272bfda455fa7dc54d5784e9133522b8","observation_id":"f0c2cbcf-f661-474c-bbb6-f1d82bf22eb0","resolution":{"observed_at":"2026-08-08T06:02:12.076979Z","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":"stable/2629156","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T06:03:27.986329Z","title":"Luenberger","venue":null,"work_id":"19bf9fc3-33b6-43bb-a8a6-e36070ee1aef","year":1972},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.081653Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:d4c48d30b8281abf7ba8cebcb3524ebb2cc36238ef60c0ad45eadfe81c32a4ba","observation_id":"7888512a-142d-45be-aeb4-b1a80de8edc8","resolution":{"observed_at":"2026-08-08T06:03:28.016060Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.600923Z","title":"Reliable measures of spread in high dimensional latent spaces","venue":null,"work_id":"2eb64849-4c2a-4a31-b422-7f7298586108","year":2023},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.085623Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:4d455bc930712df49b2d35a429616410730d83ca9d13f2cc67addb1d55c67d27","observation_id":"f4088dde-1359-4de7-88f5-a1ff1df58bc7","resolution":{"observed_at":"2026-08-08T06:04:13.605935Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.089527Z","title":"Mardia and P","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.089527Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:8a778a2ccba50157fb07cf9d4b54c3943f3581711319aae74d2e18988c9e3e08","observation_id":"e37ed280-4ae5-45f6-b894-99c5589d9dc3","resolution":{"observed_at":"2026-08-08T06:02:12.089527Z","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":"2026-08-08T06:04:13.585174Z","title":"Statistics of directional data","venue":null,"work_id":"6c7dc5d2-a50f-4b74-8774-c4b7662bda3a","year":1975},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.093515Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:87e5b76013c639bc80a0a9fadd11f8c6ff59b63f2532b5fa1d6eff20496c5e89","observation_id":"08410f47-2dbe-4e3d-a50b-1f2e8ca89e15","resolution":{"observed_at":"2026-08-08T06:04:13.590071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.569332Z","title":"Spherical text embedding","venue":null,"work_id":"d39b1979-3c4f-40a7-8a25-182a80744b6c","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.097379Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:00e61e85ec841db5d9bb8c499596b8015c097adc5dfe996c14c9ca6835d30508","observation_id":"9b587b1f-e104-435f-aebf-a31a31586a92","resolution":{"observed_at":"2026-08-08T06:04:13.574209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.552449Z","title":"Hyperspherical prototype networks","venue":null,"work_id":"188e92ff-16ef-4716-a21e-10899a5ef8c2","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.101105Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1e9327bedc0fee4a3b111e46a3fb104f6a6c016467b28770802ec90526209497","observation_id":"94b160e4-b7a1-47f4-a2bd-244913df4029","resolution":{"observed_at":"2026-08-08T06:04:13.557441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2649197","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T06:03:12.813926Z","title":"Determinantal point process models on the sphere","venue":null,"work_id":"6baf3dc7-4538-4ca7-b328-058f2ee9f0a5","year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.105127Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b225af047d7ecfc06cbbf3a5e3fd256248648774166cae240c86246bc52f9180","observation_id":"60881bc6-0510-40fb-8674-0a75973681ac","resolution":{"observed_at":"2026-08-08T06:03:12.827508Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00454-011-9392-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:03:40.895421Z","title":"Musin and Alexey S","venue":"Discrete & Computational Geometry","work_id":"26082eb2-6848-4967-9708-c7672896c9fa","year":2012},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.109070Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:0ce2b3f1f0d1f54c7909d77d86d2b49594f6cb701075def6ffeea2d2c5bba26c","observation_id":"42920733-a974-450f-9b64-c65017cd9dfd","resolution":{"observed_at":"2026-08-08T06:02:12.352382Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.113396Z","title":"Musin and Alexey S","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.113396Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:7813966066f58b2f2307cc1c62b4b72409bd44b8eda7c7ff65422b394686a8af","observation_id":"bf0b7df5-00d7-486c-b0a8-a45f51f0d88d","resolution":{"observed_at":"2026-08-08T06:02:12.113396Z","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":"2007.89953","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T06:02:42.605543Z","title":"Bastiaan Kleijn","venue":null,"work_id":"bdc6feb7-4fe7-4ad8-b79c-bf7cc8cb55a3","year":2007},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.117450Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:423434696ff0c0c404b94100e05f28db4a95d85bf6bee56a1f5cf9f01ad2a798","observation_id":"1201fabd-4ebc-42cf-8db9-9d0b5e998795","resolution":{"observed_at":"2026-08-08T06:02:42.614192Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.122088Z","title":"fairseq: A fast, extensible toolkit for sequence modeling","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.122088Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:e68aded7186a7ca64306e615a2f9f1cd82f4dc41ae117ef9f067732082e2d1a6","observation_id":"66faff56-2802-467d-bc1c-f380d728b003","resolution":{"observed_at":"2026-08-08T06:02:12.122088Z","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":"2026-08-08T06:02:12.126443Z","title":"B leu: a method for automatic evaluation of machine translation","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.126443Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:237ffce9ca5753d900f266ae435c12798bb8d7567a140a583b2daae068e7ec70","observation_id":"e57c7781-3312-4d56-a362-57a7ee81832f","resolution":{"observed_at":"2026-08-08T06:02:12.126443Z","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":"2026-08-08T06:02:12.130875Z","title":"Intrinsic statistics on R iemannian manifolds: Basic tools for geometric measurements","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.130875Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:4d802448fcdd7a83315eda079266e0d8a9600e1bbd56d01b544d67f01cb0b918","observation_id":"acce373e-95d5-437a-bfac-05a5916a4751","resolution":{"observed_at":"2026-08-08T06:02:12.130875Z","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":"2026-08-08T06:02:12.135559Z","title":"Computational optimal transport: With applications to data science","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.135559Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:e642329dc1d160700628cce925589e6f5d6084499f6f4e7f98cdc9ed579de9bb","observation_id":"e0c2450a-ac1b-4da8-ab4a-b7cfbb4f5d4d","resolution":{"observed_at":"2026-08-08T06:02:12.135559Z","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":"2026-08-08T06:02:12.140079Z","title":"A call for clarity in reporting BLEU scores","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.140079Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:dcd3cf5b543bec83d1cf2da1bf598370237efb6643d27ba11cb7f542ae0a1a73","observation_id":"4c90124a-fa3d-49f4-94a1-f26178162cfc","resolution":{"observed_at":"2026-08-08T06:02:12.140079Z","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":"2026-08-08T06:04:13.503892Z","title":"Wasserstein barycenter and its application to texture mixing","venue":null,"work_id":"7a847cc6-1137-4838-8025-110a0df08444","year":2012},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.144399Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:5ede317167123b184162d8519aa3103eb6449d1fb8e540e8c7d226f7f47ba952","observation_id":"de3abaed-1bd6-41df-b8bf-3718b9bb11df","resolution":{"observed_at":"2026-08-08T06:04:13.509357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.149071Z","title":"COMET : A neural framework for MT evaluation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.149071Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:16a8ff5d866c61d30a2e1bdc2a85eace5e5bbb57a8afcb8d10b65307a91e3712","observation_id":"baa37a6d-4e79-4445-b9bb-e45372f0dc80","resolution":{"observed_at":"2026-08-08T06:02:12.149071Z","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":"2026-08-08T06:04:13.488000Z","title":"Arrangement of 24 points on a sphere","venue":null,"work_id":"a94a9458-292a-442a-92c3-f4e7dd053642","year":1961},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.153785Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:ebfd7df9e27eae9b9eae33095b8cdeff1ef947386279a12acdc7fe2bd1a531e4","observation_id":"914e9dff-f32b-41ab-8f15-2b1a726678ee","resolution":{"observed_at":"2026-08-08T06:04:13.492491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.472444Z","title":"Orthogonal estimation of wasserstein distances","venue":null,"work_id":"1ce98f2b-b20e-47d5-9f0a-f9f6f61e419f","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.158502Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b991908b2dca085f683863258e7caebecf74246b16be04a70a11a5f9e8461d3a","observation_id":"9fd12550-151a-4cbd-b6d5-87c8b176ec8c","resolution":{"observed_at":"2026-08-08T06:04:13.477613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.456797Z","title":"Spreading vectors for similarity search","venue":null,"work_id":"1c307bb0-d2d0-4f5f-9d77-86905e471d0c","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.163034Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:bcf13f9b51bb8542d6d77b38b1bce40b77cb52c0cb494931ccab038a0e1b587e","observation_id":"0f476a89-68cf-4c72-8f89-8abe464aaae3","resolution":{"observed_at":"2026-08-08T06:04:13.461574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.441207Z","title":"Soft-max and Soft-argmax , 2024","venue":null,"work_id":"f8478c34-4f38-40e5-8ef4-b931cc4dd006","year":2024},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.167425Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:781acf4f7c7ee2662f08115cdba7942a97c7fd3e51514510054e5baaaed63c5f","observation_id":"d8ddd29f-d2a6-4093-a491-e06ccbd1be43","resolution":{"observed_at":"2026-08-08T06:04:13.445751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.425988Z","title":"Web-scale K -means clustering","venue":null,"work_id":"9e5d68e4-6d99-4da5-8553-df10d8fdf049","year":2010},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.172004Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1c3da065ce89c661ca94a23f2faccab198401cfb83106c2c3d10f7dc5a43e49f","observation_id":"4ca13867-7cba-4fe9-a516-5f46532674aa","resolution":{"observed_at":"2026-08-08T06:04:13.430681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.176122Z","title":"Approximation theorems of mathematical statistics","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.176122Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:9a11de4748bfa30ddc82a6c501739949fd20af16c24ff0677c7a5904bd4e2d08","observation_id":"ded8faa9-82f3-4ecb-a7ff-d428640c3f74","resolution":{"observed_at":"2026-08-08T06:02:12.176122Z","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":"2026-08-08T06:04:13.401366Z","title":"A user's guide to sampling strategies for sliced optimal transport","venue":null,"work_id":"a4e141b2-b8e6-430f-b675-18bfdeed1069","year":2025},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.180646Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:5752020fd47f0989d5ee5763791eb3977a6541099dda335182522d94a7263d0b","observation_id":"af88dfe1-3527-4930-aa5d-fa49ef0bffa0","resolution":{"observed_at":"2026-08-08T06:04:13.405849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.385758Z","title":"Distribution of points in a cube and approximate evaluation of integrals","venue":null,"work_id":"4686875c-725e-407c-9f2f-6d1a97459b84","year":1967},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.185403Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:014fb1383c1a54cedd8c2b4b0934ae8d4e5f5779b83f8eb407e9345549ddfce1","observation_id":"a4009d11-1331-4223-b552-40ca0b782215","resolution":{"observed_at":"2026-08-08T06:04:13.391410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02104","last_updated":"2022-11-15T06:18:46Z","snapshot_observed_at":"2026-08-16T01:19:19.312460Z","submitted_at":"2019-06-05T16:26:46Z","title":"Unbiased estimators for the variance of MMD estimators","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02104","snapshot_observed_at":"2026-08-08T06:02:12.190014Z","title":"Sutherland","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.190014Z"},"links":{"cited_paper":"/paper/1906.02104","citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:24e8ad316f962d88fe1de8f070fc7ca710ccc0651779cef88e00b0481619572c","observation_id":"11c25e4b-06f0-46ff-ab13-1be15c06ef0d","resolution":{"observed_at":"2026-08-08T06:02:12.190014Z","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":"2026-08-08T06:04:13.370338Z","title":"On the origin of number and arrangement of the places of exit on the surface of pollen-grains","venue":null,"work_id":"ccdcdb3a-29d0-4648-ac3b-be26902f39d5","year":1930},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.194856Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:a1921b58a64f8c6135efbd8c4ebd0f8a80dd165c68b591eea92ecbc5aa626ae6","observation_id":"b994eb0a-f0f4-4ae6-8234-350941dd8975","resolution":{"observed_at":"2026-08-08T06:04:13.375672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.199192Z","title":null,"venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.199192Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1062c18b6beb70a1841bbe985d89c4e75937366c2bc905daeff1a2cc615e4afc","observation_id":"134a2893-d0ca-42c7-aa4a-b5970629a61d","resolution":{"observed_at":"2026-08-08T06:02:12.199192Z","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":"10.18653/v1/2024.naacl-short.56","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"The unreasonable effectiveness of random target embeddings for continuous-output neural machine translation","venue":null,"work_id":"53183f26-ef3b-419b-98d5-c4601295e4fd","year":2024},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.204034Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:8192251aac602637e0e1bddc750465cb65ce75642e465d35c79e0f1b6172baa7","observation_id":"8fee534c-ddd3-4de0-ae1c-f4d8f8cea89c","resolution":{"observed_at":"2026-08-08T06:02:12.307206Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T06:02:12.452628Z","title":"Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings","venue":null,"work_id":"49d46be7-0a0e-4f1a-b83c-1f1d0d35f531","year":2023},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.208588Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:4647100be0b1a754d1c3a189af917662a99927856a8c2410c1b2b61afac731f4","observation_id":"352b605e-76ca-446a-8b6f-ca32abbc5e04","resolution":{"observed_at":"2026-08-08T06:02:12.458017Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.355148Z","title":"Auf welcher kugel haben 5, 6, 7, 8 oder 9 punkte mit mindestabstand eins platz ? Mathematische Annalen, 123: 0 96--124, 1951","venue":null,"work_id":"bca67f63-03fd-42e6-b4a0-afc391eec16d","year":1951},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.213149Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:0a574546e77fb982bb70314e4c9f1363e0974e332ae38e15de81514735084d92","observation_id":"3b09b6de-2ded-46a0-b4c3-6e489fbb1ade","resolution":{"observed_at":"2026-08-08T06:04:13.359948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.339500Z","title":"Gomez, ukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":"7bfffa92-67f7-47bc-a53e-b7ee240d67eb","year":2017},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.217783Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:7ac69c85b229e3750d940e5dd74f450547ba8b820649753c23869192c9f333a6","observation_id":"8f75ca71-a33c-4f03-ad4e-c753b9febb43","resolution":{"observed_at":"2026-08-08T06:04:13.344182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.323373Z","title":"Optimal transport: old and new, volume 338","venue":null,"work_id":"ddd52383-4fec-457b-a258-4ac9db48da65","year":2008},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.222478Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:081e9f16ad06df26e457e1ce9f86aec8851156bee9e9c36e3ee443de83810dc1","observation_id":"909f705b-e2d0-404a-8d4e-a16ffdc50833","resolution":{"observed_at":"2026-08-08T06:04:13.328553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.306605Z","title":"On convergence of projected gradient descent for minimizing a large-scale quadratic over the unit sphere","venue":null,"work_id":"a0e758aa-ed82-44c3-bf35-42a88db83396","year":2019},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.227227Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:0ef4517dd898b79105eacdb7a61bc5eec5d2969552269adafbeeb82d72365265","observation_id":"bea249f2-d403-4f2e-ad0a-310ae82734a1","resolution":{"observed_at":"2026-08-08T06:04:13.311947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.290014Z","title":"Improving neural language generation with spectrum control","venue":null,"work_id":"dcd2b2e2-0ddb-43ee-adbe-9ea80df68423","year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.232093Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:87785ae15ed8064cf2f5962d72a8841dba7c42f29ae5c56a98170ec65620576a","observation_id":"08904f17-384c-4c3a-a6ce-440de1de1ddc","resolution":{"observed_at":"2026-08-08T06:04:13.295147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.273757Z","title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere","venue":null,"work_id":"35b5b9a6-46b5-4ca5-9730-59c984fdfd31","year":2020},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.236671Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:99286f2a96ecc288dcc4954f0ea589424e663a1f31a3c3c26635eaa159d2947a","observation_id":"8a41a669-91e2-4007-b53c-92bbdd55f722","resolution":{"observed_at":"2026-08-08T06:04:13.278784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.257410Z","title":"Mma regularization: Decorrelating weights of neural networks by maximizing the minimal angles, 2021","venue":null,"work_id":"2282efa4-c22f-46b6-bc4f-8423e5c7a81b","year":2021},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.240603Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:4d0cb6d6ac8ae432cd75d056b495819e2975758480ed4731682e4e12fe690894","observation_id":"4255f09b-3816-4f7c-a80d-5ce057294848","resolution":{"observed_at":"2026-08-08T06:04:13.262205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:04:13.241893Z","title":"Atan2 --- Wikipedia , the free encyclopedia","venue":null,"work_id":"76a2138e-cb38-449b-83f9-7ce0b8c6d568","year":2024},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.244465Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:b0a0882dcb0cb4727d8af8b5e54c6a1dbbb8c26e7fcd048a1240734db8dd803c","observation_id":"dcc83e2a-b405-4ec9-aeab-c1246ec33eb3","resolution":{"observed_at":"2026-08-08T06:04:13.246817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v36i10.21426","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Frequency-aware contrastive learning for neural machine translation","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"2823351c-d3c7-4a9c-a1d3-3a42801f7511","year":2022},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.248530Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:d60c12e518bc80c7eb68ccc93db1b94ef659da259b6f70f2c1417760bf60450f","observation_id":"82ef3c7f-573f-43e4-9ae9-a76f4438646d","resolution":{"observed_at":"2026-08-08T06:02:12.292023Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-08T06:02:12.252510Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$","version":4},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-08T06:02:12.252510Z"},"links":{"citing_paper":"/paper/2502.08231"},"observation_digest":"sha256:1324e20bf8c8a3aedea797a818ab5c22928621f35c15f9030f7c666ef580d723","observation_id":"e2a0c028-cfa4-4571-9551-f310bd7429f7","resolution":{"observed_at":"2026-08-08T06:02:12.252510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.08231","last_updated":"2025-08-26T12:04:11Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T03:25:36.440215Z","submitted_at":"2025-02-12T09:20:08Z","title":"Keep your distance: learning dispersed embeddings on $\\mathbb{S}_m$"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":27,"verified_exact":8,"verified_fuzzy":47},"total_outbound_references":83},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2502.08231."}