{"as_of":"2026-08-08T09:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:faeb13a4705c3791a354b42023a4bb6f14483ac9968a9eeaf25e4d432a04907a","coverage":[{"denominator":80,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":80,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:11:48.611106Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T12:27:30.761467Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22560","snapshot_observed_at":"2026-08-04T12:27:30.761467Z","title":"Geometric hyena networks for large-scale equivariant learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.03511","last_updated":"2026-06-02T22:28:21Z","snapshot_observed_at":"2026-08-04T12:27:29.054298Z","submitted_at":"2025-10-03T20:51:25Z","title":"Platonic Transformers: A Solid Choice For Equivariance","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-04T12:27:30.761467Z"},"links":{"cited_paper":"/paper/2505.22560","citing_paper":"/paper/2510.03511"},"observation_digest":"sha256:167ac024b8e052d5139a6c35f1d63b915129b3de1ec79f1d1db20e1e8323a166","observation_id":"45eb6947-5c2d-4e79-aba2-942160608328","resolution":{"observed_at":"2026-08-04T12:27:30.761467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.22560/citation-record","integrity":"/paper/2505.22560/integrity","json":"/paper/2505.22560/citation-record.json","paper":"/paper/2505.22560"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:11:40.249880Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.249880Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:16fd12c953d83effe886d579886862651b5de332c317276816c08ffc8941cc42","observation_id":"d9923490-6e52-4717-b638-2f2dbdd014d4","resolution":{"observed_at":"2026-08-07T13:11:40.249880Z","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":"2026-08-07T13:11:57.764825Z","title":"Vn-transformer: Rotation-equivariant attention for vector neurons","venue":null,"work_id":"adf8bc67-8098-487f-915c-3b2e5fe8e7ea","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.352209Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:38b12ec9b47a2a4b5394069f475b9d31c8b39ba73ff037ea8a831ec27a4dc409","observation_id":"eaf6812f-af13-4448-9ef6-54cacd784f1b","resolution":{"observed_at":"2026-08-07T13:11:57.870982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:57.546676Z","title":"and Sali, A","venue":null,"work_id":"632ecf91-6f14-4921-9f70-4f2582613af5","year":2001},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.431380Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:a0216aa99b267268ce8662f6ce85fd135fe33e82ba22fac02dbb7b85fdd1ce56","observation_id":"f3cc634c-14b6-480f-b53a-808ca7b3caab","resolution":{"observed_at":"2026-08-07T13:11:57.650885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02970","last_updated":"2024-03-15T09:21:33Z","snapshot_observed_at":"2026-08-05T04:35:44.839958Z","submitted_at":"2023-10-04T17:06:32Z","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02970","snapshot_observed_at":"2026-08-07T13:11:40.514416Z","title":"J., Vadgama, S., Hesselink, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.514416Z"},"links":{"cited_paper":"/paper/2310.02970","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:9c9dbfda05e2ed7fab2cb156931114e615a7654ce1b586771acd549acad923bd","observation_id":"d2c29e39-5003-4644-8a8c-a7630cdf1f74","resolution":{"observed_at":"2026-08-07T13:11:40.514416Z","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":"2026-08-07T13:11:57.337207Z","title":"J., and Welling, M","venue":null,"work_id":"5b14f3b1-dd94-421d-87c6-2aa69eec3fb7","year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.687212Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:db5668fc4ac9620f3e32503efd3d26f068bf49a1742c66c53b59d5fae176bd69","observation_id":"a5e1ba57-72d9-442e-96f9-6e5c5214dc90","resolution":{"observed_at":"2026-08-07T13:11:57.444645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:57.155182Z","title":"D., Behrends, S., and Cohen, T","venue":null,"work_id":"19fb9976-92c5-4f0c-96f2-4a0fbbde8a1f","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.776692Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:7318df1e7963a4d15344b9e5abfe33990bb17722ac1fd5b68bffe9f38e88baa4","observation_id":"d4a1c3fe-7f3a-4d29-a44b-2713b5b0189e","resolution":{"observed_at":"2026-08-07T13:11:57.236676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:56.977908Z","title":"Are high-degree representations really unnecessary in equivariant graph neural networks? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024","venue":null,"work_id":"3c8a1c47-1360-4f0d-8d73-b373721dab34","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:40.868251Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:cb8aa6f79c8302120d242086b62f21e256b6b3f8f8b2cbb69dc355a2b5fdd9cc","observation_id":"e02b7c86-1227-4749-8c40-83ca9bdcab5c","resolution":{"observed_at":"2026-08-07T13:11:57.075233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:56.743885Z","title":"Nomenclature of inorganic chemistry: Iupac recommendations 2005","venue":null,"work_id":"9bb1d09d-a632-43c1-abea-16ece0407612","year":2005},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.064064Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:d5ac56a9b608650e65c1df1245bd36e2af76a85bd0111ae4be123aef33fa0e8f","observation_id":"054e34da-d888-41d6-bd20-159399a3032d","resolution":{"observed_at":"2026-08-07T13:11:56.863693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:56.549921Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","venue":null,"work_id":"654e0259-6864-4782-97a1-3b3da4e90260","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.330286Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:960d9bc93bb0158a34d0fc144ed113dc9a78e02d59a1ee1e6c79c25d33cf0e60","observation_id":"61fa0ac6-24cb-4cac-ab9b-65c41c91328d","resolution":{"observed_at":"2026-08-07T13:11:56.628267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:56.392223Z","title":"Openvaccine: Covid-19 mrna vaccine degradation prediction, 2020","venue":null,"work_id":"937ecfa4-8405-42c8-b941-a6873f66cc5a","year":2020},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.423715Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:0ea03a7d68fa1e372dd89785755ed7a37c4c771bead179bedf22c70ad825a7af","observation_id":"e5379a66-369f-4c93-aaac-620a7bc76a41","resolution":{"observed_at":"2026-08-07T13:11:56.468492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19427","last_updated":"2024-02-29T18:24:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-29T18:24:46Z","title":"Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19427","snapshot_observed_at":"2026-08-07T13:11:41.546011Z","title":"L., Fernando, A., Botev, A., Cristian-Muraru, G., Gu, A., Haroun, R., Berrada, L., Chen, Y., Srinivasan, S., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.546011Z"},"links":{"cited_paper":"/paper/2402.19427","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:561f054c2a195ad7f1483e68ba711d0c08b047ce6181e6f8264d2f6f9d692202","observation_id":"62752f89-cdd0-4fdc-a32d-e0684ef8d378","resolution":{"observed_at":"2026-08-07T13:11:41.546011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.05425","last_updated":"2021-11-19T12:00:16Z","snapshot_observed_at":"2026-08-03T06:30:02.988662Z","submitted_at":"2020-03-11T17:21:15Z","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.05425","snapshot_observed_at":"2026-08-07T13:11:41.659284Z","title":"Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.659284Z"},"links":{"cited_paper":"/paper/2003.05425","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:8cd57abb6b5d98600104b80be3cfab21bcdc7a33b3ca4370976ea7f8f289f856","observation_id":"ff43acea-cec6-42d2-ace6-87bfd7ce9a13","resolution":{"observed_at":"2026-08-07T13:11:41.659284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04744","last_updated":"2024-03-14T10:55:46Z","snapshot_observed_at":"2026-08-04T14:08:34.174747Z","submitted_at":"2023-11-08T15:12:31Z","title":"Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04744","snapshot_observed_at":"2026-08-07T13:11:41.739802Z","title":"Euclidean, projective, conformal: Choosing a geometric algebra for equivariant transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.739802Z"},"links":{"cited_paper":"/paper/2311.04744","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:1b8ef0a7d297943f309a91f9cc6756da6fc2ce87fc98d20f2b47b6419501cf34","observation_id":"0d5abc4c-9c65-4a7d-8dc2-50e1c410731f","resolution":{"observed_at":"2026-08-07T13:11:41.739802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.12229","last_updated":"2021-04-25T18:48:15Z","snapshot_observed_at":"2026-07-06T11:03:26.012948Z","submitted_at":"2021-04-25T18:48:15Z","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.12229","snapshot_observed_at":"2026-08-07T13:11:41.948079Z","title":"Vector neurons: a general framework for so(3)-equivariant networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:41.948079Z"},"links":{"cited_paper":"/paper/2104.12229","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:001f606bee5c5964501f30cb9a9a6ce59048a1bbc6bd041a61ffc189b4efe55b","observation_id":"d6cd8b19-eb3a-4172-9539-3e52e190b433","resolution":{"observed_at":"2026-08-07T13:11:41.948079Z","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":"2026-08-07T13:11:56.183007Z","title":"Geometric algebra for computer science (revised edition): An object-oriented approach to geometry","venue":null,"work_id":"ff3925b6-ce8f-45b1-a057-d37b24d1503a","year":2009},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.127583Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:158761f04704be0283f4ace046061d2770ce5416d14fce8f0b3bc602ef438264","observation_id":"544382bc-5a80-473f-aae4-c83f2197fd13","resolution":{"observed_at":"2026-08-07T13:11:56.279781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:55.960528Z","title":"P., Ma, Z.-M., et al","venue":null,"work_id":"fec0d446-b173-46cf-8a08-b8fe5ab58f43","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.290506Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:bfa2ce832b67b93a3494adb9d127a21161e8f2683c3e139dace76e7b9daccf83","observation_id":"fe344d4d-090b-4506-8382-32de080f3fc2","resolution":{"observed_at":"2026-08-07T13:11:56.060715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14052","last_updated":"2023-04-29T03:18:40Z","snapshot_observed_at":"2026-08-06T18:01:24.576458Z","submitted_at":"2022-12-28T17:56:03Z","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.14052","snapshot_observed_at":"2026-08-07T13:11:42.448178Z","title":"Y., Dao, T., Saab, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.448178Z"},"links":{"cited_paper":"/paper/2212.14052","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:e34b59fd4fdac09572dd747c506bd7dccbd95cd8601807b6a4695bf79a096a8b","observation_id":"1a407ea5-8c7d-4e0e-b9cf-638ffc31c967","resolution":{"observed_at":"2026-08-07T13:11:42.448178Z","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":"2026-08-07T13:11:55.742811Z","title":"Se (3)-transformers: 3d roto-translation equivariant attention networks","venue":null,"work_id":"381cacdb-3666-4011-ae34-55a39614058f","year":1970},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.510272Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:4a1359c758b8035e2cc02ee8fc58958043eff19430b3bf56fea7865d71ea468d","observation_id":"8c5d04d2-fd67-42c8-ae9f-cfb13e05a336","resolution":{"observed_at":"2026-08-07T13:11:55.847452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:55.522584Z","title":"S., Riley, P","venue":null,"work_id":"4c06000c-f751-4133-b6a5-f7a75055103a","year":2017},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.645328Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:99803f687aa812eaeaf9be68e387569d3b8e3988c291248be27d7f1c9703294d","observation_id":"26ac329d-f4ac-4fe8-af11-6b497f9a1332","resolution":{"observed_at":"2026-08-07T13:11:55.628099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:55.404255Z","title":"Tuning the performance of synthetic riboswitches using machine learning","venue":null,"work_id":"57fcb2dd-4f0a-4ae8-b68a-153d743a160c","year":2018},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.819260Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:753e39763adcefbd6aba4cbc35d4e115311799ad5e9f882e500c17207c8d4970","observation_id":"e529f80b-90c6-4b98-9d87-09ef2649297e","resolution":{"observed_at":"2026-08-07T13:11:55.476400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-07T13:11:42.946200Z","title":"and Dao, T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:42.946200Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:96fc0071eb906068ff2c138c2d5d3b0ce6bcab8147137d4c4e032dd2a12c7bed","observation_id":"99a07d0c-0a3c-4eb7-9fbb-3305d996356b","resolution":{"observed_at":"2026-08-07T13:11:42.946200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-07T13:11:43.120054Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.120054Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:36e546f983a90afbf307524f594210e96f811ac9f9cacd90a30fbb66d7da529f","observation_id":"cfd8acf1-cef0-4e5c-b8f1-ae02bd528b2f","resolution":{"observed_at":"2026-08-07T13:11:43.120054Z","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":"2026-08-07T13:11:55.271470Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","venue":null,"work_id":"56b0d04b-ff4e-4a09-affd-5810008f36ec","year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.324919Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:b8247136587571c3eaed19a3262e154e3de68605e8288e07d2eda7460b6d693a","observation_id":"86e2121d-1955-4bf2-bc0b-a28aef7344da","resolution":{"observed_at":"2026-08-07T13:11:55.319978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:55.094693Z","title":"Equivariant graph hierarchy-based neural networks","venue":null,"work_id":"97f7a376-fbac-4ce0-b551-d4dddc8762b6","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.453372Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:76086798442afd3c69badf568e31c35a6bedc22cdc1ce03f9c4b8a8cccab8320","observation_id":"d233bbfb-00b0-42a5-a498-3aede2c87a98","resolution":{"observed_at":"2026-08-07T13:11:55.181707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:54.938600Z","title":"C., Karagianes, T","venue":null,"work_id":"45acf164-2e5b-4ef6-b6e4-ccf9ef40502a","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.530583Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:ef5ceedef85e9dd3506a7e9d98cebe92e6ef1a536b7bd058c85030507d32124e","observation_id":"754fb334-1cc8-4dcc-abc9-cf42c1b6132b","resolution":{"observed_at":"2026-08-07T13:11:55.012604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:54.781541Z","title":"S., and Ma, T","venue":null,"work_id":"58a58488-23a9-4b1d-8483-0e44c469d07b","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.638850Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:8dbe7b8a9c74265d4cb6ceeaf6fc8815e746641d7bb86ecec57c6d5bf0fd8247","observation_id":"fc8aef6c-f7d4-4c23-baec-55dcb171daa0","resolution":{"observed_at":"2026-08-07T13:11:54.853415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03843","last_updated":"2021-07-13T12:42:27Z","snapshot_observed_at":"2026-07-06T11:16:49.764856Z","submitted_at":"2021-06-07T17:57:04Z","title":"Equivariant Graph Neural Networks for 3D Macromolecular Structure","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.03843","snapshot_observed_at":"2026-08-07T13:11:43.740248Z","title":"N., and Dror, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.740248Z"},"links":{"cited_paper":"/paper/2106.03843","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:798ef94d465630d7e8866bc32034b8d5dc96d50c64ee3237490e15b12720d59c","observation_id":"d4665ed2-2d38-4cb0-91b6-c37db6c9ffdb","resolution":{"observed_at":"2026-08-07T13:11:43.740248Z","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":"2026-08-07T13:11:54.585473Z","title":null,"venue":null,"work_id":"35e1f44f-9a08-4bb5-8bce-8474c513b9e4","year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.842699Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:75cd1ae5470604e8a225d2b40b6eda90a0f8219391e6fea05b92388f61198c44","observation_id":"db9fad86-f082-49bf-bf52-5f04a4a50444","resolution":{"observed_at":"2026-08-07T13:11:54.673517Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:54.400701Z","title":"and Gasteiger, J","venue":null,"work_id":"19d49bf8-3480-4ec9-a945-9642eacd6bf9","year":1977},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:43.957412Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:a2d44ac60eaa06819922d65a8b5679eab5d4a65fec45f618345c634b6ae3237b","observation_id":"b1dafaff-7316-45d7-9487-d31af3628546","resolution":{"observed_at":"2026-08-07T13:11:54.495864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:54.214255Z","title":"Pure transformers are powerful graph learners","venue":null,"work_id":"fed098df-33a9-4e77-93b4-fdea63a65361","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.045508Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:8157687bcebda37b711778ee50a858c6f64de32f34f6d7681e799158559f0f99","observation_id":"080bb8be-0cd8-483e-859d-d95e408f7f6d","resolution":{"observed_at":"2026-08-07T13:11:54.313476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T13:11:44.136186Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.136186Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:a315427b23e4b031f2401c478d928a82c9838c4667f4b4d0c908c0880248b27c","observation_id":"ed954245-708b-4076-a210-d1d341446482","resolution":{"observed_at":"2026-08-07T13:11:44.136186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-07T13:11:44.230831Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.230831Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:d2a30e311301df27d180630e25392441f11f62d10e06544f378279855f6c4233","observation_id":"ddfa7f2f-c84a-42d1-b095-9016245e3d7e","resolution":{"observed_at":"2026-08-07T13:11:44.230831Z","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":"2026-08-07T13:11:44.299409Z","title":"Equivariant flows: exact likelihood generative learning for symmetric densities","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.299409Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:7c7b41e2d0b5c6511416043dbfdea6334dffade749eb605dab24e06349159b57","observation_id":"7416a841-793d-46c6-af18-9497da9e7ed2","resolution":{"observed_at":"2026-08-07T13:11:44.299409Z","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":"2026-08-07T13:11:44.433136Z","title":"Rethinking graph transformers with spectral attention","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.433136Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:ec1d3d3a8890ecee38bd9778979ad492bbdf64f3b74ac57d8d0aedf88deb2e31","observation_id":"680f7b8b-e761-48d3-9b8e-06efef32a0ac","resolution":{"observed_at":"2026-08-07T13:11:44.433136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05892","last_updated":"2024-07-13T17:37:00Z","snapshot_observed_at":"2026-07-06T17:27:37.212340Z","submitted_at":"2024-02-08T18:30:50Z","title":"Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05892","snapshot_observed_at":"2026-08-07T13:11:44.519384Z","title":"Mamba-nd: Selective state space modeling for multi-dimensional data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.519384Z"},"links":{"cited_paper":"/paper/2402.05892","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:e819e0b5ec8ab790706be7c127ea061df5da0e2acd28f16f9c4b0a3bdd39f79d","observation_id":"cc9191ba-b5f5-413e-82cf-59a402be1811","resolution":{"observed_at":"2026-08-07T13:11:44.519384Z","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":"2026-08-07T13:11:53.944176Z","title":"and Smidt, T","venue":null,"work_id":"3e03d64a-6531-45d6-8756-557ca0606744","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.614547Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:8d561420394a687939c03a0e71b2014746ff32c98dfc9b8b227290b908795a9b","observation_id":"7eca8bca-8ebd-450f-9ba9-f53a9b739b69","resolution":{"observed_at":"2026-08-07T13:11:54.051686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-07T13:11:44.748600Z","title":"and Hutter, F","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.748600Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:659235853aaa6e894d3596aae24113d23978bc2e51d0097ccee937f9a76d423d","observation_id":"497fc091-7145-42ff-8c0d-ce565d732a60","resolution":{"observed_at":"2026-08-07T13:11:44.748600Z","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":"2026-08-07T13:11:53.734666Z","title":null,"venue":null,"work_id":"bc4d24e9-9b56-40ef-b923-c84b1ad09e9c","year":2010},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.834763Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:eeff4465da6af565f384e8718ba655b5d8ae04a71030b3e79d308d36c2e2ddb0","observation_id":"659060d7-7cdf-4ed8-9aa2-5c285de62e22","resolution":{"observed_at":"2026-08-07T13:11:53.831893Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:53.525778Z","title":"J., and Smeulders, A","venue":null,"work_id":"b9220d5d-851d-4ee3-8d71-902a51d1afd9","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.906253Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:7ebeb09fa6202b3541d61eed6eae674707578149a3b34e4fbc645a7b284f315e","observation_id":"fd825b1d-d0bb-47a0-96b9-3b9a8e681999","resolution":{"observed_at":"2026-08-07T13:11:53.609661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:44.978626Z","title":"Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:44.978626Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:3b2105ab75647b8eca719f73009247c520c41090d4f1662c8c70a9319cafcc6d","observation_id":"2cfd4b53-1507-471a-ab4b-d804e19d72c9","resolution":{"observed_at":"2026-08-07T13:11:44.978626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-09-24T00:43:19Z","title":"In-context Learning and Induction Heads","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11895","snapshot_observed_at":"2026-08-07T13:11:45.047075Z","title":"In-context learning and induction heads","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.047075Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:f076d96961737614d20ac908ddf4d375312158100271a5e53e0cd76b1b32c976","observation_id":"d8e47549-f78d-437b-9225-4fd653b6e82c","resolution":{"observed_at":"2026-08-07T13:11:45.047075Z","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":"2026-08-07T13:11:45.177368Z","title":"L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., and De, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.177368Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:a7e0174f55ef06cad088c765fefccf23ca4ffb27f9456ad946b461c3286785f3","observation_id":"9baca3fd-66ae-418b-aecd-cbe4dae2559f","resolution":{"observed_at":"2026-08-07T13:11:45.177368Z","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":"2026-08-07T13:11:53.288812Z","title":"P., Simeon, G., Galvelis, R., Mirarchi, A., Eastman, P., Doerr, S., Thölke, P., Markland, T","venue":null,"work_id":"83dda731-9b83-4ca6-a6e3-67a68cc2817e","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.272508Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:c903e5a5b36b3e5d6a67044da271cd1726af47a24d0e41a612d7f5b453aa66aa","observation_id":"d2fa48c2-abaf-4dfe-8e74-392698f89ccd","resolution":{"observed_at":"2026-08-07T13:11:53.398246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:53.049942Z","title":"Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and R \\'e , C","venue":null,"work_id":"8bb085fa-abe6-407e-b4d0-3400a86c7444","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.354247Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:a905f3b34899b87a5ad97f72e6a8336b01a39956337d8e3f0fcd69af77fc9a3e","observation_id":"2fe78664-2c67-4407-8169-7945550a8f51","resolution":{"observed_at":"2026-08-07T13:11:53.193062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:52.890798Z","title":"W., Nguyen, E., Ponnusamy, P., Deiseroth, B., Kersting, K., Suzuki, T., Hie, B., Ermon, S., Ré, C., Zhang, C., and Massaroli, S","venue":null,"work_id":"1ee1de4f-d6c2-4dc2-a043-4e0f6b3a1a03","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.423615Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:f782e119dbea1d0b00321db219f37b792f1d147aa42271907f15d96e07351bd9","observation_id":"f74a95fb-64d4-4eff-98e0-4f557453c850","resolution":{"observed_at":"2026-08-07T13:11:53.000225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:45.492078Z","title":"P., Luu, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.492078Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:d3f7d3952bb66874ef835318cd4919a1f024e8dadf897e6501d65700e2a2a8fb","observation_id":"9c6655e6-84bf-4d4b-a0a3-556d8d58905d","resolution":{"observed_at":"2026-08-07T13:11:45.492078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.02611","last_updated":"2022-03-17T13:26:32Z","snapshot_observed_at":"2026-07-06T10:38:29.014713Z","submitted_at":"2021-02-04T13:51:19Z","title":"CKConv: Continuous Kernel Convolution For Sequential Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.02611","snapshot_observed_at":"2026-08-07T13:11:45.529206Z","title":"W., Kuzina, A., Bekkers, E","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.529206Z"},"links":{"cited_paper":"/paper/2102.02611","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:078d8c0f270f5e66f5442133ca0a4130225d95b626ea631f5c530ae7b0206ad2","observation_id":"b7b44462-7026-490e-b2b5-d0cbfcf54361","resolution":{"observed_at":"2026-08-07T13:11:45.529206Z","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":"2026-08-07T13:11:52.622134Z","title":"and Bahri, C","venue":null,"work_id":"3f7d5869-4155-408b-a9fb-f64d49f785d1","year":2000},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.594913Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:cd91583a6d12f6e9b298ea0d072f4a94621acd81d0110f1af88df3eb005730d3","observation_id":"dfda45c3-5f78-48e3-b4a5-1383a2ce33a6","resolution":{"observed_at":"2026-08-07T13:11:52.758587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:52.332011Z","title":"Clifford group equivariant neural networks","venue":null,"work_id":"633dba17-2409-4148-b492-6ddedd8e8724","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.660264Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:3573d0549d7771b41d1c0683cb1be6025b44d5b187c0827d43ba862a8c284ace","observation_id":"44a2e302-d9de-420d-b2de-a39a1b446a2f","resolution":{"observed_at":"2026-08-07T13:11:52.465198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:52.057013Z","title":"K., De Keninck, S., Welling, M., and Brandstetter, J","venue":null,"work_id":"582c40ce-a48f-409d-a9c3-675124780119","year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.757010Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:c9cd0da14a52fc5bf9b31611b26b4a1d61b521253054bd792103e58524cd6ae0","observation_id":"f145070b-785f-4d83-817f-e9183b353592","resolution":{"observed_at":"2026-08-07T13:11:52.189623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10993","last_updated":"2023-03-20T10:21:29Z","snapshot_observed_at":"2026-07-06T15:05:38.707313Z","submitted_at":"2023-03-20T10:21:29Z","title":"A Survey on Oversmoothing in Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10993","snapshot_observed_at":"2026-08-07T13:11:45.854852Z","title":"K., Bronstein, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.854852Z"},"links":{"cited_paper":"/paper/2303.10993","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:bd5059746cce3a219ad387d3a84a7c5895ed6cff2e73d26f9f4f6ad208485527","observation_id":"e5234b9a-b82d-4560-b695-6b4bec69708e","resolution":{"observed_at":"2026-08-07T13:11:45.854852Z","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":"2026-08-07T13:11:51.831556Z","title":"Rna secondary structure prediction using deep learning with thermodynamic integration","venue":null,"work_id":"5291dccf-e856-4cdf-8807-712749e69817","year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:45.928894Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:70e2cb7fbfa2b1ab365c95f9222e997c84a51c77b4ea5b9b8c4c152487d89bf4","observation_id":"cfc23bec-4c57-465c-b0a2-8475c6bd8b42","resolution":{"observed_at":"2026-08-07T13:11:51.933159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:46.021667Z","title":"G., Hoogeboom, E., and Welling, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.021667Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:f3f0a0667c8d04a7a252b67b2c6e635d908e174cb4189b850d78e644e02ceffc","observation_id":"19b72610-dad1-49ab-b946-48d0bd4327d1","resolution":{"observed_at":"2026-08-07T13:11:46.021667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03234","last_updated":"2024-06-05T21:02:37Z","snapshot_observed_at":"2026-07-06T17:40:01.975792Z","submitted_at":"2024-03-05T01:42:51Z","title":"Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03234","snapshot_observed_at":"2026-08-07T13:11:46.114752Z","title":"Caduceus: Bi-directional equivariant long-range dna sequence modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.114752Z"},"links":{"cited_paper":"/paper/2403.03234","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:863b9e10324be2bef0049b970917c15f632241592379ad1bd3f198699ccb6c8e","observation_id":"6c82c161-19ee-4c5f-9d4d-4c79636ce67c","resolution":{"observed_at":"2026-08-07T13:11:46.114752Z","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":"2026-08-07T13:11:46.209782Z","title":"u tt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and M \\","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.209782Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:c74bb30bb8862acf4d3c7be605a5838dc66416ca594cb3badcfce7d2d864753d","observation_id":"5ee03497-7a89-4e29-a44f-4f9009dbcac0","resolution":{"observed_at":"2026-08-07T13:11:46.209782Z","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":"2026-08-07T13:11:46.313979Z","title":"and Beckstein, O","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.313979Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:1c212b9ff287372590588e5c225d68d31bcf3a915c01be0d67509d0bc75fd9f8","observation_id":"055d02c1-061e-4b73-9023-3d42dd661140","resolution":{"observed_at":"2026-08-07T13:11:46.313979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01586","last_updated":"2025-01-02T18:03:15Z","snapshot_observed_at":"2026-07-06T13:27:38.650283Z","submitted_at":"2022-07-04T17:15:35Z","title":"Accurate RNA 3D structure prediction using a language model-based deep learning approach","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01586","snapshot_observed_at":"2026-08-07T13:11:46.400690Z","title":"E2efold-3d: End-to-end deep learning method for accurate de novo rna 3d structure prediction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.400690Z"},"links":{"cited_paper":"/paper/2207.01586","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:c647505692e4b72df52c2599949a57eca8d6c74f26f38546238a9e6616ac95ba","observation_id":"06793730-8461-4257-835c-44ce7df95a5d","resolution":{"observed_at":"2026-08-07T13:11:46.400690Z","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":"2026-08-07T13:11:46.495442Z","title":"Implicit neural representations with periodic activation functions","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.495442Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:9fdf24a519f87ce94b39dab895a7fb659ce6f2fd1d1df1625511fb6fd5b0e928","observation_id":"ec38b64c-ab6f-4d68-88f4-802b74331d14","resolution":{"observed_at":"2026-08-07T13:11:46.495442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13526","last_updated":"2025-04-07T17:33:06Z","snapshot_observed_at":"2026-07-06T18:17:50.573600Z","submitted_at":"2024-05-22T10:51:12Z","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13526","snapshot_observed_at":"2026-08-07T13:11:46.588799Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.588799Z"},"links":{"cited_paper":"/paper/2405.13526","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:6ffad21bac8f0c083eae76454e88afbd1d266252866343a23f09d7030e648a95","observation_id":"b0ffeffb-5de7-4704-8994-c0b02fa19af6","resolution":{"observed_at":"2026-08-07T13:11:46.588799Z","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":"2026-08-07T13:11:51.568549Z","title":"A., Gil-Ley, A., Pinamonti, G., Poblete, S., Jurecka, P., et al","venue":null,"work_id":"0094509d-e2e4-4198-96b1-1d79ff633022","year":2018},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.674765Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:869f445b3810fc18abc9b90edb0e7c004dace514e96b8c044c86f377c66d5785","observation_id":"0ff59d22-2bba-4e49-8adb-abbd6e21dd2d","resolution":{"observed_at":"2026-08-07T13:11:51.668780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08219","last_updated":"2018-05-18T20:09:34Z","snapshot_observed_at":"2026-07-06T06:24:51.822169Z","submitted_at":"2018-02-22T18:17:31Z","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.08219","snapshot_observed_at":"2026-08-07T13:11:46.765853Z","title":"Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.765853Z"},"links":{"cited_paper":"/paper/1802.08219","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:eafc1e74136c26f2ca6953e3809e56a121924ceed5c90b8da80ae719acfd901a","observation_id":"3371954a-b4e0-4eee-80b1-c6d381b4aee4","resolution":{"observed_at":"2026-08-07T13:11:46.765853Z","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":"2026-08-07T13:11:46.851805Z","title":"N., Kaiser, ., and Polosukhin, I","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.851805Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:301a6db8cea03c71a901865543b623c0ae8a650b18a2f44ddf0ae4ea9fc5c198","observation_id":"96315550-afe3-49e3-8d09-d99cb8b9bf88","resolution":{"observed_at":"2026-08-07T13:11:46.851805Z","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":"2026-08-07T13:11:51.288173Z","title":"M., Keating, K","venue":null,"work_id":"672d73b6-b3f0-4570-ba8a-f5a353109a4f","year":2007},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:46.917393Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:ca1cc2797d917f5bed6c978520e038402438fcf2b8f7614ca4ae1adedc783657","observation_id":"837d8031-1ca9-4cd0-b65c-c48ed8e2a314","resolution":{"observed_at":"2026-08-07T13:11:51.416184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09516","last_updated":"2024-04-15T07:24:45Z","snapshot_observed_at":"2026-07-06T18:00:07.537987Z","submitted_at":"2024-04-15T07:24:45Z","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09516","snapshot_observed_at":"2026-08-07T13:11:47.039341Z","title":"State space model for new-generation network alternative to transformers: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.039341Z"},"links":{"cited_paper":"/paper/2404.09516","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:02ddf5b57686cf65a6d90c6414fd1a8696bc65391ea662320dfa3a9045f87cc5","observation_id":"f7c6cbca-2e74-4f2a-baee-f984d743b03b","resolution":{"observed_at":"2026-08-07T13:11:47.039341Z","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":"2026-08-07T13:11:51.066337Z","title":"Neural p\\ 3\\ m: A long-range interaction modeling enhancer for geometric GNN s","venue":null,"work_id":"6d4707ea-78f6-4754-8d60-3a23d98d777c","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.132944Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:1497072a60a64c15b52972f56c5647b94492fd819ea6ec5662335aad5f39d899","observation_id":"453c08ed-f56d-47bf-8423-2acda0daa5db","resolution":{"observed_at":"2026-08-07T13:11:51.139781Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:50.861695Z","title":"K., Kladwang, W., Watkins, A","venue":null,"work_id":"14b57f39-7394-4dc6-832e-a42d7e5b1a65","year":2022},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.222596Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:77d9ee2f15f61784902589574bc34156f34149a1c7e5a8d85fa578d48de79449","observation_id":"3f4a3ee6-8356-4684-b11c-829555dd93ca","resolution":{"observed_at":"2026-08-07T13:11:50.974726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:47.356175Z","title":"and Cesa, G","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.356175Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:efa810774642b646c0365dc8b4cc7cfc57b5390bb5f12dabcf526e113aa166a9","observation_id":"6259e132-aad6-4450-af5c-0ec4085a9d1f","resolution":{"observed_at":"2026-08-07T13:11:47.356175Z","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":"2026-08-07T13:11:50.638043Z","title":"Pointconv: Deep convolutional networks on 3d point clouds","venue":null,"work_id":"cca9f587-7fad-4b8e-882c-e945948c795d","year":2019},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.428100Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:bd3b6e34dcf16d5bd4172de6f6fc9926409d95144bdba29a616bf4462c0fe3ea","observation_id":"f3f0819b-9f71-4c28-9bb0-05f26242254d","resolution":{"observed_at":"2026-08-07T13:11:50.707059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:47.519143Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.519143Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:44b85bbe64366473539db5e62dfe4556dca99a15abd9f69d119d5dd8126c92e4","observation_id":"d078769c-6a5b-47fd-85a9-6713f8fc1dae","resolution":{"observed_at":"2026-08-07T13:11:47.519143Z","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":"2026-08-07T13:11:50.474079Z","title":"B., and Schlick, T","venue":null,"work_id":"d62af92f-28ec-46a9-9ebf-391c5b7410b1","year":2008},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.652756Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:c87b1baf277288b98753fa09965165dc01eace8c5a53b5f5cdc307f127dbd44e","observation_id":"e0897265-a32a-4e0a-a36b-a9dba9d90427","resolution":{"observed_at":"2026-08-07T13:11:50.542406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11933","last_updated":"2025-04-21T00:07:03Z","snapshot_observed_at":"2026-07-06T19:34:05.643989Z","submitted_at":"2024-10-15T17:09:34Z","title":"Beyond Sequence: Impact of Geometric Context for RNA Property Prediction","version":2},"cited_work":{"arxiv_id":"2410.11933","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.11933","snapshot_observed_at":"2026-08-07T13:11:48.760485Z","title":"Beyond Sequence: Impact of Geometric Context for RNA Property Prediction","venue":"q-bio.QM","work_id":"35c9de83-d38c-4867-87ee-9c017892aa07","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.721488Z"},"links":{"cited_paper":"/paper/2410.11933","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:98f92e8a40719c380584570e23271afe10e9662a7af5e8b149fabb311a40a8c4","observation_id":"a45b1f36-c27c-4dc5-89bd-711c344d5628","resolution":{"observed_at":"2026-08-07T13:11:48.836833Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:50.291878Z","title":"HARMONY : A multi-representation framework for RNA property prediction","venue":null,"work_id":"43ce40e6-25f9-4c0e-bae8-e0a3b137304d","year":2025},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.793546Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:93d8aaced85aedc3073c2b3a8e1fb3bff8f6a6d0d6cdd0356391f5a09337edb4","observation_id":"249f6608-f73e-4e30-9124-18cab251eba1","resolution":{"observed_at":"2026-08-07T13:11:50.397584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:50.098539Z","title":"Graphformers: Gnn-nested transformers for representation learning on textual graph","venue":null,"work_id":"534f6348-c774-45e3-b433-da3b2c78e950","year":2021},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.891896Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:daf501a19d60dbdaa5c73987e800f708c6c3ba9120458264e7b841ae685b9ecf","observation_id":"2a1cbe34-10f0-4604-8c73-d1bc93bbd43e","resolution":{"observed_at":"2026-08-07T13:11:50.196871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:49.930384Z","title":"HELM : Hierarchical encoding for m RNA language modeling","venue":null,"work_id":"157e983a-04ba-41dc-9944-ff93ffa9b751","year":2025},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:47.992231Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:47a15b595726bb1167771ea97078200861cdbd106133084d97765fa46267db09","observation_id":"e615b7fb-b8a1-44ff-a655-10e51684f1ed","resolution":{"observed_at":"2026-08-07T13:11:49.994378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08423","last_updated":"2025-07-24T21:15:25Z","snapshot_observed_at":"2026-07-06T15:54:47.352645Z","submitted_at":"2023-07-17T12:14:14Z","title":"Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08423","snapshot_observed_at":"2026-08-07T13:11:48.097484Z","title":"Artificial intelligence for science in quantum, atomistic, and continuum systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.097484Z"},"links":{"cited_paper":"/paper/2307.08423","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:e7d7886952b5951a30304708c4521a647292426676cf6ad40b5dbc52b43f37b9","observation_id":"f89d3c9e-9d33-49fd-bb3f-a29ef69804f7","resolution":{"observed_at":"2026-08-07T13:11:48.097484Z","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":"2026-08-07T13:11:49.791652Z","title":"Improving equivariant graph neural networks on large geometric graphs via virtual nodes learning","venue":null,"work_id":"06411798-38fa-4453-9720-839cc0da94b1","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.182908Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:ef3fded61f5cba4e419ffc63eebc223d56801157288e11ccc1029cc225dcf185","observation_id":"d5a42509-9a80-4435-8ae7-9cc2ad5fe268","resolution":{"observed_at":"2026-08-07T13:11:49.859645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:49.567186Z","title":"Implicit convolutional kernels for steerable cnns","venue":null,"work_id":"c8f241ee-9e9b-4f4b-9420-b0a47bddf8fc","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.304609Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:8358d71064a850bfdef9b9012085376423de37e66ada9e44717bf9151778ae84","observation_id":"7c0b54cf-1a81-42ee-93f9-bf6f2fefc8fd","resolution":{"observed_at":"2026-08-07T13:11:49.685823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:49.388177Z","title":"Clifford-steerable convolutional neural networks, 2024 b","venue":null,"work_id":"6fc5a311-206a-40a9-95ee-a941e4975098","year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.383116Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:4473b71c8449ef5766c1fd1764bf6a2cd6590bc661b0ed68a3fbb1a6a27d10b2","observation_id":"6c53caba-1977-475f-9568-c2f09dade115","resolution":{"observed_at":"2026-08-07T13:11:49.491113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:11:49.226734Z","title":"Erwin: A tree-based hierarchical transformer for large-scale physical systems","venue":null,"work_id":"87ad3ce8-0570-47e1-a4b6-7855551148be","year":2025},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.493780Z"},"links":{"citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:298ca3f0b8b42ab68056cb9dc0855979ef6240bb4e018db536d4fb01f24e04f2","observation_id":"bf3914c5-29c3-43dd-b38a-57351d8e827e","resolution":{"observed_at":"2026-08-07T13:11:49.312725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-07-06T17:16:59.193820Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-07T13:11:48.611106Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:48.611106Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2505.22560"},"observation_digest":"sha256:0eba8546acaa2cce8c878109b71fb19436014d7ede2cd751f26ac126b35cb4b5","observation_id":"aca0d723-b993-4306-9abe-93116db076e9","resolution":{"observed_at":"2026-08-07T13:11:48.611106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.22560","last_updated":"2025-05-28T16:38:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T06:04:28.958281Z","submitted_at":"2025-05-28T16:38:35Z","title":"Geometric Hyena Networks for Large-scale Equivariant Learning"},"reference_resolution":{"displayed":80,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":38,"verified_exact":1,"verified_fuzzy":41},"total_outbound_references":80},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 1 inbound Pith citation observation for arXiv:2505.22560."}