{"as_of":"2026-08-22T17:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:354b0755e77159a950299d5c62637b2bc0fb03149b321ebb3a6946fe138f5d8d","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:59:33.134847Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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":"2026-06-26T17:53:38.503877Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T03:39:29.503341Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"cited_work":{"arxiv_id":"2507.09514","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.09514","snapshot_observed_at":"2026-07-04T03:39:29.503341Z","title":"arXiv preprint arXiv:2507.09514 , year=","venue":null,"work_id":"d0e28b27-9f8b-4a2c-bb70-f59800a42783","year":null},"citing_paper":{"arxiv_id":"2606.19932","last_updated":"2026-06-18T08:31:11Z","snapshot_observed_at":"2026-08-16T02:44:30.244332Z","submitted_at":"2026-06-18T08:31:11Z","title":"Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-06-26T17:53:38.503877Z"},"links":{"cited_paper":"/paper/2507.09514","citing_paper":"/paper/2606.19932"},"observation_digest":"sha256:c518e73c0275e203a98856667e2c0cf1ab257602e461436e94aa08905affd01a","observation_id":"11caece9-f833-4485-a219-df364e35db4c","resolution":{"observed_at":"2026-07-04T03:39:29.505186Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.09514/citation-record","integrity":"/paper/2507.09514/integrity","json":"/paper/2507.09514/citation-record.json","paper":"/paper/2507.09514"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-06T17:59:26.828008Z","title":"E., and Cohan, A","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:26.828008Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:35e8cddf178f2b7804ad56055da7de94e0457625a53b0501d8867234d43a78e8","observation_id":"2aa73c59-da76-4018-a454-3e8dd3d14a3f","resolution":{"observed_at":"2026-08-06T17:59:26.828008Z","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":"2026-08-06T17:59:41.150999Z","title":"and Hoffman, J","venue":null,"work_id":"aaecdc3a-5a91-4f55-8764-0d199f538998","year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:26.881185Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:07143b6c48de05c8ed08286d65d6ccd4b69004be841f013eb64cce689b78f92b","observation_id":"6410f858-8541-4c76-a855-541b9795f512","resolution":{"observed_at":"2026-08-06T17:59:41.196456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.09461","last_updated":"2023-03-01T19:45:11Z","snapshot_observed_at":"2026-08-13T04:00:22.647615Z","submitted_at":"2022-10-17T22:23:40Z","title":"Token Merging: Your ViT But Faster","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.09461","snapshot_observed_at":"2026-08-06T17:59:26.951484Z","title":"Token merging: Your vit but faster, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:26.951484Z"},"links":{"cited_paper":"/paper/2210.09461","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:1f33f4403b265bbd2992318a05d779e0a30a3a0d13f07bd7067518d6dc4a43e3","observation_id":"a88e0aa8-519b-4b98-bf40-e34aa64d1b45","resolution":{"observed_at":"2026-08-06T17:59:26.951484Z","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":"2026-08-06T17:59:27.002807Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.002807Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:4eeb8f5d8562f369ff4ea3d74983c9584e9516639b47aeb23d5cf62a58154341","observation_id":"52d3b278-420f-4a34-9b57-2f3f801c7199","resolution":{"observed_at":"2026-08-06T17:59:27.002807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T17:59:27.074610Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.074610Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:fbb79f307af34fa08195073676279a8fcbc2c89b0ae5e9d064157c445ce615ef","observation_id":"c2afab03-5f70-40e2-b02d-b3c27c761c0b","resolution":{"observed_at":"2026-08-06T17:59:27.074610Z","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":"2026-08-06T17:59:27.138967Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.138967Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:6918f72a451305b5005012b190f4f78d4126fbc2fb24f74d3360b27a4b8ad8e9","observation_id":"1a3fcd91-ee5e-4387-8f2f-dd76d743ae0a","resolution":{"observed_at":"2026-08-06T17:59:27.138967Z","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":"2026-08-06T17:59:40.954341Z","title":"v., Williams, C., Winn, J., and Zisserman, A","venue":null,"work_id":"82e9c2a0-c673-4f98-ad90-b0faa793f81f","year":2008},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.200096Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:3702c6e119c9ce3b423ff39624de7cf5fd51ac0b1387047b9b60c26ea1d7e958","observation_id":"5caf2e6c-e0f6-48af-85ba-1ae870cf4804","resolution":{"observed_at":"2026-08-06T17:59:41.021664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-14T19:37:43.556604Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-06T17:59:27.249505Z","title":"and Carbin, M","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.249505Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:a512b1d006749f0163261be78d781734f0a0e0526519b0d4a23525968427af4e","observation_id":"38fb0b3f-b2e2-431e-b689-46977e2e3ed5","resolution":{"observed_at":"2026-08-06T17:59:27.249505Z","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":"2026-08-06T17:59:27.325173Z","title":"F., Powell, J","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.325173Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:788ab2a5354ddcb769c9b62754120a4cc0dce7c588e1853bd8cd334498acf6a2","observation_id":"e6b31128-d35d-4f4c-912c-daf149455075","resolution":{"observed_at":"2026-08-06T17:59:27.325173Z","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":"2026-08-06T17:59:40.760639Z","title":"Y., Dao, T., Saab, K","venue":null,"work_id":"7f0befe5-b50a-4377-8033-de6a997c0cdc","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.378517Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:e169c0214433371fa51569b0d09c0f8623cb4bf173a35d7aaf45260abec05c0b","observation_id":"b2766bee-dfda-4ec0-8e6e-814dea7b10cb","resolution":{"observed_at":"2026-08-06T17:59:40.829544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:40.541104Z","title":"Fast r-cnn","venue":null,"work_id":"2f7021aa-21f6-40fb-8781-679acc98403a","year":2015},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.459314Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:8bd5ddaf092dcff76bba5725075f8ebace56f74efa80f2080df078303ada9eed","observation_id":"50a8a855-03a3-4f48-b24c-a76701f550a0","resolution":{"observed_at":"2026-08-06T17:59:40.648414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:27.537052Z","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.537052Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:3e5ad42d80dfe84408ba6ac6c0a012630d60a70ca93c25a5f5b6557abd5583a6","observation_id":"c421c463-304e-452d-a508-f95b9844f63e","resolution":{"observed_at":"2026-08-06T17:59:27.537052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","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":"2026-08-06T17:59:27.541474Z","title":"and Dao, T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.541474Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:9b1e8baebe4759cc508f92aab54d081312a3c47594936db1abd28f27b7388daa","observation_id":"32f2baa7-d3c4-4c64-9bc8-17f608424f54","resolution":{"observed_at":"2026-08-06T17:59:27.541474Z","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":"2026-08-06T17:59:40.360886Z","title":"Hippo: Recurrent memory with optimal polynomial projections","venue":null,"work_id":"eba16a95-bdee-4a65-bb94-a890d934df48","year":2020},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.544350Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:d9fcbe3497b0b294fb6632cfd4981ab8d30a8a77f43cc302f4776dd5e7506c71","observation_id":"96f5b6d0-0c02-4c0d-9e8b-6c56acbcb6a5","resolution":{"observed_at":"2026-08-06T17:59:40.418981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:40.227303Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":"165a692d-0a66-4f5e-988d-522a19b17ae0","year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.604905Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:b7159a043bc045177538d22da8ae0927d1f19c5b873e054f9b201122b14e5f73","observation_id":"7582af97-3f66-4133-92fa-56709ff270b5","resolution":{"observed_at":"2026-08-06T17:59:40.299987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1510.00149","last_updated":"2016-02-15T06:25:40Z","snapshot_observed_at":"2026-08-04T16:59:47.843960Z","submitted_at":"2015-10-01T09:03:44Z","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.00149","snapshot_observed_at":"2026-08-06T17:59:27.704718Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.704718Z"},"links":{"cited_paper":"/paper/1510.00149","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:c2bb6a15f32a357443523269d0ffc6b40a78f6eb27ea948fd96ad22120d8b25e","observation_id":"ceae620d-560c-4eeb-8a5e-53615d343e10","resolution":{"observed_at":"2026-08-06T17:59:27.704718Z","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":"2026-08-06T17:59:27.823022Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.823022Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:cbbf3a9273fd380318e129cbe7a1571df90b04dd0427a4c6987709c4e1e8c68e","observation_id":"b7501206-bd9a-4cb8-9252-ed86fb8293c7","resolution":{"observed_at":"2026-08-06T17:59:27.823022Z","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":"2026-08-06T17:59:39.997789Z","title":"Channel pruning for accelerating very deep neural networks","venue":null,"work_id":"cce91b26-e7de-4a8f-804d-122495a41ab3","year":2017},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:27.911229Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:1c91416d9b6d9948a8a55494578c041c765681d932e5d4aaf9bda0b9292fa648","observation_id":"7428cfd1-1053-42bd-8429-d4bb882aec39","resolution":{"observed_at":"2026-08-06T17:59:40.168067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06866","last_updated":"2018-08-21T12:22:38Z","snapshot_observed_at":"2026-08-14T18:38:54.194750Z","submitted_at":"2018-08-21T12:22:38Z","title":"Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.06866","snapshot_observed_at":"2026-08-06T17:59:28.017036Z","title":"Soft filter pruning for accelerating deep convolutional neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.017036Z"},"links":{"cited_paper":"/paper/1808.06866","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:fe25fd7027872ea797fc430196d658fb17c789900f4516e9ab205af445f7c2de","observation_id":"acd5db23-ac30-45a2-b364-543a7363590d","resolution":{"observed_at":"2026-08-06T17:59:28.017036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13802","last_updated":"2024-11-24T14:25:05Z","snapshot_observed_at":"2026-08-21T00:33:04.928196Z","submitted_at":"2024-03-20T17:59:14Z","title":"ZigMa: A DiT-style Zigzag Mamba Diffusion Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13802","snapshot_observed_at":"2026-08-06T17:59:28.140778Z","title":"T., Baumann, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.140778Z"},"links":{"cited_paper":"/paper/2403.13802","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:344cfc47fa1e88ad4db72852328038044815bd2947174c3896f036e66cd6e8cb","observation_id":"a934d5d2-727c-4aae-afa1-832d5b7e03dc","resolution":{"observed_at":"2026-08-06T17:59:28.140778Z","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":"2026-08-06T17:59:39.812412Z","title":null,"venue":null,"work_id":"5c7acb7f-1dc0-49d4-8ee5-c2a60dbe1f95","year":2012},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.255091Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:130e98cb012c324d73f976e8d07668e31b82bf6595ce2663b6b8df62a78e20b5","observation_id":"00570895-fcdb-4959-b78a-c8aa6a5cbe24","resolution":{"observed_at":"2026-08-06T17:59:39.931492Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:39.653312Z","title":null,"venue":null,"work_id":"a13dd7c1-a8f6-4909-aa77-ab34e7b033ca","year":1960},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.400446Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:729d7cb7fd548581e0584d04267c80e5465414e672ec9974aa567a7c34fdf827","observation_id":"5d2a9e4f-76ec-44d3-b05a-808497e0ddb5","resolution":{"observed_at":"2026-08-06T17:59:39.717224Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:39.467030Z","title":"Crafting papers on machine learning","venue":null,"work_id":"87d85e11-13a6-4b9c-885a-c6206706c321","year":2000},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.476254Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:9e59378b8e800a209bd9b7197aecaeeda88daffc8fc7f8ef31253059a92c4bd1","observation_id":"ac8fe3f0-ba52-40c6-91ef-433c42176d82","resolution":{"observed_at":"2026-08-06T17:59:39.539437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:39.227023Z","title":"Optimal Brain Damage","venue":null,"work_id":"608a0846-bd61-4d04-ad04-0e22e0074d93","year":1989},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.613638Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:08c928a0b18396c20a8180d1f2ae22a0af903f8f926199ae7e455a016944c9ec","observation_id":"60822566-733f-4ed1-a18d-e001d36f399d","resolution":{"observed_at":"2026-08-06T17:59:39.348580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.08710","last_updated":"2017-03-10T17:57:56Z","snapshot_observed_at":"2026-08-20T02:05:14.188305Z","submitted_at":"2016-08-31T02:29:59Z","title":"Pruning Filters for Efficient ConvNets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.08710","snapshot_observed_at":"2026-08-06T17:59:28.715444Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.715444Z"},"links":{"cited_paper":"/paper/1608.08710","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:4a02d554eb8d9ae4b59706f22a59f6c3ff389f65e4ae5eb174669a48bac6f1a6","observation_id":"cc844d1a-1a2f-4e1b-be87-acc900edb4b3","resolution":{"observed_at":"2026-08-06T17:59:28.715444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06977","last_updated":"2024-03-12T15:22:52Z","snapshot_observed_at":"2026-08-16T14:10:51.584702Z","submitted_at":"2024-03-11T17:59:34Z","title":"VideoMamba: State Space Model for Efficient Video Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06977","snapshot_observed_at":"2026-08-06T17:59:28.795259Z","title":"Videomamba: State space model for efficient video understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.795259Z"},"links":{"cited_paper":"/paper/2403.06977","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:2ed6ec9fbb2a89b67b7600871aac8a622122dae5912a0ce8bf01f23f1dea28d5","observation_id":"421ad7d2-17c9-475e-b20f-1894381d7cff","resolution":{"observed_at":"2026-08-06T17:59:28.795259Z","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":"2026-08-06T17:59:39.006007Z","title":"Videomamba: State space model for efficient video understanding","venue":null,"work_id":"9517270d-72a9-4c0e-b45d-ad61965b557b","year":2025},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:28.912856Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:e379e4d9de4bb1bf02ef769830097d0b2e47284ec2f0109d30464a1936f1c5f7","observation_id":"92f8753a-0555-4af1-b861-ad250c5eb7b0","resolution":{"observed_at":"2026-08-06T17:59:39.104894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:38.796481Z","title":"Supervised masked knowledge distillation for few-shot transformers","venue":null,"work_id":"550762a0-6cb8-499d-ac64-7c846e93ab1f","year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.026875Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:4a552b013025d216dbb0888076325dbf7e1a6d864acf93ce11ce2d32adb20c80","observation_id":"7f828e15-3224-43f1-a56f-368b18786e9c","resolution":{"observed_at":"2026-08-06T17:59:38.891164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:38.553826Z","title":"Hrank: Filter pruning using high-rank feature map","venue":null,"work_id":"c4844e14-55bc-4ab3-84f2-99f399aee487","year":2020},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.102008Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:7086314aa64bad4cad6027734493deabbde8d4faa3bc9cbfebcf1cb8f1572f75","observation_id":"31940c1b-6656-4199-bdc4-fe8bbfa7604d","resolution":{"observed_at":"2026-08-06T17:59:38.677423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.13824","last_updated":"2023-02-17T13:17:52Z","snapshot_observed_at":"2026-08-16T22:48:47.551504Z","submitted_at":"2021-11-27T06:20:53Z","title":"FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.13824","snapshot_observed_at":"2026-08-06T17:59:29.255532Z","title":"Fq-vit: Post-training quantization for fully quantized vision transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.255532Z"},"links":{"cited_paper":"/paper/2111.13824","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:2057df13b6891c7b7234f633d79dd48d331f08257a2adcd575ea78f1586d07a5","observation_id":"915de67a-f83b-483f-b78d-610083b4c6dd","resolution":{"observed_at":"2026-08-06T17:59:29.255532Z","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":"2026-08-06T17:59:38.367980Z","title":"Efficientvit: Memory efficient vision transformer with cascaded group attention","venue":null,"work_id":"d170cd9a-702f-4760-817b-6425d443a160","year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.368833Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:49946576a5484b5482b8b7871fb22dee689a3232ce86a3d676469a3131aa5d2f","observation_id":"9aaa47ca-014f-459d-aeeb-4df93450d29f","resolution":{"observed_at":"2026-08-06T17:59:38.455301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-06T17:59:29.412939Z","title":"VMamba : Visual State Space Model , April 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.412939Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:29225f362a44573d51c47b1688b9f7168176d7503f2e25a173147233577eeeba","observation_id":"20de0ecf-ae59-4f6e-8714-f21244bb6669","resolution":{"observed_at":"2026-08-06T17:59:29.412939Z","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":"2026-08-06T17:59:38.079403Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"b9a193eb-68cf-4f6f-bc4d-4d283a70a6f9","year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.534074Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:464b36a9da153f8faa391f5edecbf5a9438b3e485d227ca1adb01d0444f28765","observation_id":"c46be122-0db1-451d-a448-03b77610843f","resolution":{"observed_at":"2026-08-06T17:59:38.278509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:37.809213Z","title":"Post-training quantization for vision transformer","venue":null,"work_id":"cfc0d4c0-74d2-4669-aa4a-f80163d82cba","year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.673387Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:5f73cf7c892c6b3e31f9a74420e7cac8baaeb8eb22c9c2edd55f7c27a0d9a145","observation_id":"f93b555c-0812-4d5f-9834-9850e9d013a7","resolution":{"observed_at":"2026-08-06T17:59:37.942952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:37.526780Z","title":"Swin transformer v2: Scaling up capacity and resolution","venue":null,"work_id":"3871f06a-817d-4a09-ac9d-346fb86435ab","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.863104Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:be197a7ed3176267361d8e196c393c9bad52b9f90da0d328a53850a603f6c797","observation_id":"510f641a-0baf-489d-93fd-96553665c81c","resolution":{"observed_at":"2026-08-06T17:59:37.651505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04722","last_updated":"2024-01-09T18:53:20Z","snapshot_observed_at":"2026-08-17T03:22:34.203372Z","submitted_at":"2024-01-09T18:53:20Z","title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04722","snapshot_observed_at":"2026-08-06T17:59:29.982476Z","title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:29.982476Z"},"links":{"cited_paper":"/paper/2401.04722","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:5fd67bdec62da58d34374c3a960931225918ce9c825290559822357ed9eca298","observation_id":"1bc66269-5c2d-441a-bc71-2443ec0612e7","resolution":{"observed_at":"2026-08-06T17:59:29.982476Z","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":"2026-08-06T17:59:37.334784Z","title":"S4nd: Modeling images and videos as multidimensional signals with state spaces","venue":null,"work_id":"0ce339ac-5dbe-4a55-9cef-775e264293fc","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.104969Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:b88906e0c4077a5651cf3ff7d6c39d2d9e237d454bf6e7f65bb23d5695baa42d","observation_id":"473e7b50-e1b0-40f6-8034-e6c0112c05e7","resolution":{"observed_at":"2026-08-06T17:59:37.409905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09977","last_updated":"2024-03-15T02:48:47Z","snapshot_observed_at":"2026-08-17T01:16:32.185673Z","submitted_at":"2024-03-15T02:48:47Z","title":"EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09977","snapshot_observed_at":"2026-08-06T17:59:30.230457Z","title":"Efficientvmamba: Atrous selective scan for light weight visual mamba, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.230457Z"},"links":{"cited_paper":"/paper/2403.09977","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:89c08b4cef91b45bf928d4ee3361df30ceda07f7181d394e4dfe88235b3d70af","observation_id":"a2950387-485f-4054-be2f-61e641763b91","resolution":{"observed_at":"2026-08-06T17:59:30.230457Z","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":"2026-08-06T17:59:37.097894Z","title":"K., et al","venue":null,"work_id":"b04b7fb7-da2e-426b-ac8c-584be0644015","year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.337397Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:4f46ae46e86661330fc989804feeb23c422dfd5d2cebda930a75e7ef9d1aa17b","observation_id":"44eb437b-d4d0-48d5-be09-a7fb2cb45344","resolution":{"observed_at":"2026-08-06T17:59:37.211355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:30.474574Z","title":"Dynamicvit: Efficient vision transformers with dynamic token sparsification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.474574Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:c3aefe77291d5e4bb497d6b76db8e5ba53cf21db2ff27f054f37744af9a0e143","observation_id":"f48dd64e-d856-48d2-a132-90c248b4b618","resolution":{"observed_at":"2026-08-06T17:59:30.474574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01580","last_updated":"2023-06-02T13:50:01Z","snapshot_observed_at":"2026-08-19T06:06:02.796482Z","submitted_at":"2022-07-04T17:00:51Z","title":"Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks","version":2},"cited_work":{"arxiv_id":"2207.01580","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.01580","snapshot_observed_at":"2026-08-06T17:59:33.282510Z","title":"Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks","venue":"cs.CV","work_id":"0561d5cc-1324-44d1-b4c2-70fa2b9bef66","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.608284Z"},"links":{"cited_paper":"/paper/2207.01580","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:0c05c697ad2ed69433eb4aa2fb755161a785bbcc7e16a0000c6088b101d9a993","observation_id":"520eccca-66ab-402a-8fc2-c6bf79d813e1","resolution":{"observed_at":"2026-08-06T17:59:33.360101Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:36.905518Z","title":"You only look once: Unified, real-time object detection","venue":null,"work_id":"15f9ca82-a491-49ee-8d26-65445d4484ef","year":2016},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.708567Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:1120980f01482da977a45d5bfa720aab4967cd06d85a2f3074e545da0b520c2f","observation_id":"d1ab60f6-4382-4b1e-96df-78ce439aa45a","resolution":{"observed_at":"2026-08-06T17:59:36.970522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:36.735455Z","title":null,"venue":null,"work_id":"45af5521-a0c1-4ea9-9f1e-487cdc24d9bb","year":2007},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.822525Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:e3a1fbaca35d01705eea8a0e6259f37f65a20225c0aa53c955d8827049b916a6","observation_id":"35416c50-b71d-485e-b21b-c758605a4123","resolution":{"observed_at":"2026-08-06T17:59:36.818992Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12496","last_updated":"2025-04-14T09:37:17Z","snapshot_observed_at":"2026-08-19T13:19:25.397938Z","submitted_at":"2024-12-17T02:56:35Z","title":"Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12496","snapshot_observed_at":"2026-08-06T17:59:30.946262Z","title":"R., Zhao, W., Wang, K., and You, Y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:30.946262Z"},"links":{"cited_paper":"/paper/2412.12496","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:bbec22a85fb29f4bb239e510f0de097d1d4e40ed41429aa49edff2593aac6ce4","observation_id":"ee770119-5eb2-4921-8e8a-d29486685686","resolution":{"observed_at":"2026-08-06T17:59:30.946262Z","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":"2026-08-06T17:59:36.503318Z","title":"and Zisserman, A","venue":null,"work_id":"cb5a424c-3263-4c64-a30a-434261a5b81f","year":2015},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.059124Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:20cb403f1a73384aaa9658c5df691a9605175613ae4d89cacdb9620f9f476261","observation_id":"f777d99c-fcb4-404f-a003-8a2b80b77fbe","resolution":{"observed_at":"2026-08-06T17:59:36.660312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:36.300553Z","title":"T., Warrington, A., and Linderman, S","venue":null,"work_id":"f1b4ccad-b953-4e62-8910-de5372116114","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.196928Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:dca5a66b99225bea08fabe6c64eceadda6de026748783913e217d8af3c00c608","observation_id":"1b719dc9-3e26-41e0-9011-84f273779f17","resolution":{"observed_at":"2026-08-06T17:59:36.408124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:36.018425Z","title":"Patch slimming for efficient vision transformers","venue":null,"work_id":"db37845d-e1b6-435b-8506-459a44536de4","year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.313394Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:3377846584304c20d00c3de61de920d32679bee9c114153180a7b770db5fd5b9","observation_id":"34478a53-a716-4ac1-aa0f-2ab2d0f515a3","resolution":{"observed_at":"2026-08-06T17:59:36.177084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:35.830004Z","title":"Vmrnn: Integrating vision mamba and lstm for efficient and accurate spatiotemporal forecasting","venue":null,"work_id":"7808b8ba-eee8-48b2-9030-e2f028b7a9d8","year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.394175Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:de194c36ef83a8b1d6a11c3238f154e79b94359586fe755b904625ac60d45d27","observation_id":"972f78ea-74f6-40a4-9353-4342ae3fcced","resolution":{"observed_at":"2026-08-06T17:59:35.889111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14224","last_updated":"2024-07-10T09:02:11Z","snapshot_observed_at":"2026-08-16T20:38:15.729152Z","submitted_at":"2024-05-23T06:53:18Z","title":"DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14224","snapshot_observed_at":"2026-08-06T17:59:31.491654Z","title":"Dim: Diffusion mamba for efficient high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.491654Z"},"links":{"cited_paper":"/paper/2405.14224","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:1bf279506f150b23f7fb278141c9502b03cb864fcbf1d01b1d08cf34c0c7d170","observation_id":"0219d8c5-b9ed-4e13-9623-316f062cdec8","resolution":{"observed_at":"2026-08-06T17:59:31.491654Z","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":"2026-08-06T17:59:35.564973Z","title":"Training data-efficient image transformers & distillation through attention","venue":null,"work_id":"f17c6821-1af7-4975-b74c-b6d0e6e1d0dd","year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.554878Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:a4f52a4f3fa8317441070758bd811bfb70232a3bdfbedcef004c6bd15835199e","observation_id":"54f006d9-7526-49de-aa5a-643720a4868b","resolution":{"observed_at":"2026-08-06T17:59:35.712506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04768","last_updated":"2020-06-14T08:15:54Z","snapshot_observed_at":"2026-07-06T09:27:03.809621Z","submitted_at":"2020-06-08T17:37:52Z","title":"Linformer: Self-Attention with Linear Complexity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04768","snapshot_observed_at":"2026-08-06T17:59:31.618240Z","title":"Z., Khabsa, M., Fang, H., and Ma, H","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.618240Z"},"links":{"cited_paper":"/paper/2006.04768","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:10818291989b8ef02b3edc6aee0422999883ff5fbc504264dd58da153833846c","observation_id":"2c32fe0e-74b2-4939-bd58-1ab7510a6626","resolution":{"observed_at":"2026-08-06T17:59:31.618240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05079","last_updated":"2024-03-30T17:51:35Z","snapshot_observed_at":"2026-08-16T14:20:30.127674Z","submitted_at":"2024-02-07T18:33:04Z","title":"Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05079","snapshot_observed_at":"2026-08-06T17:59:31.707399Z","title":"Mamba-unet: Unet-like pure visual mamba for medical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.707399Z"},"links":{"cited_paper":"/paper/2402.05079","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:ca41631c821ec648de61017e046dffeaecbe0853b02236ea4c7c27c27df4457b","observation_id":"80d0eb42-1e52-4ddf-9eb4-3c3e41c7e9fb","resolution":{"observed_at":"2026-08-06T17:59:31.707399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03771","snapshot_observed_at":"2026-08-06T17:59:31.765024Z","title":"L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.765024Z"},"links":{"cited_paper":"/paper/1910.03771","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:67708b695b2beba7a5da535808d868807479c4c9b20a550c64cf6b9fd66c5294","observation_id":"a410d545-86d3-476c-9a16-631b8ef30530","resolution":{"observed_at":"2026-08-06T17:59:31.765024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00808","last_updated":"2023-01-02T18:59:31Z","snapshot_observed_at":"2026-08-18T10:34:23.603924Z","submitted_at":"2023-01-02T18:59:31Z","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00808","snapshot_observed_at":"2026-08-06T17:59:31.845406Z","title":"S., and Xie, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.845406Z"},"links":{"cited_paper":"/paper/2301.00808","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:5a4c6d4ec0677368bb517ad15ef142ce4c5a7ccd9ace0824aa99ccf09e90b2cf","observation_id":"c7567e3a-c51c-4aca-94ff-18800bf341a7","resolution":{"observed_at":"2026-08-06T17:59:31.845406Z","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":"2026-08-06T17:59:35.340912Z","title":"Unified perceptual parsing for scene understanding","venue":null,"work_id":"4201e764-46fe-4ae8-bc41-05df2e610b47","year":2018},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:31.938409Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:c8768a9fd78868c927041f5b527e381ef214b2a59ce4e173e4ffc19e8fd3affc","observation_id":"f15e8afc-e611-45e5-8ccf-4abff36fb131","resolution":{"observed_at":"2026-08-06T17:59:35.441021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:35.060212Z","title":"Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation","venue":null,"work_id":"8e330696-48f5-4cc2-8d22-ca9033717267","year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.014813Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:bef1f248d071235cc53bacef2dcd27ad50644bdf670fe71761af6e9183be3d64","observation_id":"09a9fc2b-968d-46a6-a88a-7ccd7f594640","resolution":{"observed_at":"2026-08-06T17:59:35.225825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:34.810461Z","title":null,"venue":null,"work_id":"2a8b9dce-7ffd-4b81-a10a-5fdc2f909501","year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.076861Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:df52da7089489205a80f7b14b65a8d0d3d2757e14cc0bfc1bf7fd1b95f9c1f33","observation_id":"21cac31f-37f8-4ae2-865a-c501daf71bfe","resolution":{"observed_at":"2026-08-06T17:59:34.910491Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:34.577083Z","title":"Medmnist classification decathlon: A lightweight automl benchmark for medical image analysis","venue":null,"work_id":"16c73999-079d-4f2e-9f29-cc6f9f1c1ba4","year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.177462Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:5a800a5de48c693abfa2fbfbcd1cefdc060045e18ddf8c46157ec88d153f585f","observation_id":"98659115-7d99-4dc9-bb4a-dd1042292961","resolution":{"observed_at":"2026-08-06T17:59:34.697095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:34.204871Z","title":"Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification","venue":null,"work_id":"0e332a34-0467-4062-8788-3b657d820e55","year":2023},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.295169Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:37a689eb1b414df558aa208f6fe22386393a5abb3d80e52e3b2ce235b17f8d8b","observation_id":"6df773b8-afa3-4882-882e-121986279cce","resolution":{"observed_at":"2026-08-06T17:59:34.321676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.07658","last_updated":"2022-10-05T18:39:43Z","snapshot_observed_at":"2026-08-16T17:34:39.625481Z","submitted_at":"2021-12-14T18:56:07Z","title":"AdaViT: Adaptive Tokens for Efficient Vision Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.07658","snapshot_observed_at":"2026-08-06T17:59:32.445461Z","title":"Adavit: Adaptive tokens for efficient vision transformer, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.445461Z"},"links":{"cited_paper":"/paper/2112.07658","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:a8c98ba884a8840735be16312cb760682a6f8950d53af51600f6e70d19e0c76a","observation_id":"ab366496-5db8-4675-bec0-fd9bfd0b685b","resolution":{"observed_at":"2026-08-06T17:59:32.445461Z","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":"2026-08-06T17:59:34.056536Z","title":"I., Han, X., Gao, M., Lin, C.-Y., and Davis, L","venue":null,"work_id":"d47080f9-b576-4e0c-9c9d-e5a4c084a999","year":2018},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.587852Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:e13e17e7f1ffe2afe2f3686964e7130a8561cd98db224fdd3e64c3280ab5c898","observation_id":"08aeac04-2662-4e5a-8cf3-091326a8660b","resolution":{"observed_at":"2026-08-06T17:59:34.124830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03849","last_updated":"2024-09-29T03:55:46Z","snapshot_observed_at":"2026-08-18T18:35:58.893716Z","submitted_at":"2024-03-06T16:49:33Z","title":"MedMamba: Vision Mamba for Medical Image Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03849","snapshot_observed_at":"2026-08-06T17:59:32.688931Z","title":"and Li, Z","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.688931Z"},"links":{"cited_paper":"/paper/2403.03849","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:070713d9ef967903f7e783973f3789a545abcd3869295af6c4346387afd7a051","observation_id":"4c3dad39-6b4a-4e6c-85d1-c4b5cb648176","resolution":{"observed_at":"2026-08-06T17:59:32.688931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.18962","last_updated":"2024-09-27T17:59:50Z","snapshot_observed_at":"2026-08-17T01:19:59.593238Z","submitted_at":"2024-09-27T17:59:50Z","title":"Exploring Token Pruning in Vision State Space Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.18962","snapshot_observed_at":"2026-08-06T17:59:32.755285Z","title":"Exploring token pruning in vision state space models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.755285Z"},"links":{"cited_paper":"/paper/2409.18962","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:8c460301b00009851e2d51bc2c11818698815e380fa68265d04909ce37a096f2","observation_id":"b5792f12-1ed1-4810-9a0d-fd56e6c81e97","resolution":{"observed_at":"2026-08-06T17:59:32.755285Z","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":"2026-08-06T17:59:33.893970Z","title":"Scene parsing through ade20k dataset","venue":null,"work_id":"1b367815-b0b0-4d6b-924c-cd48ec9330f6","year":2017},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.845993Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:2e1a43bc241cdc4bae13ae65917536d440dfd67523f5d2ddc963cc8a6421f7e7","observation_id":"3d0705c8-49a3-4dec-a889-bf80301af547","resolution":{"observed_at":"2026-08-06T17:59:33.956026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:33.729043Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":"c1953fc9-3a63-4da0-af3e-61b5ab2ca6e1","year":2024},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.891956Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:d6dfc8016c4519eec2b8072807b4a3bfc5a6c04b32d07b642717b98b4c33b3dc","observation_id":"460c3563-e59a-4c01-95b1-19668d2111d8","resolution":{"observed_at":"2026-08-06T17:59:33.822025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08500","last_updated":"2021-08-14T06:06:37Z","snapshot_observed_at":"2026-08-16T18:30:54.894095Z","submitted_at":"2021-04-17T09:49:24Z","title":"Vision Transformer Pruning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08500","snapshot_observed_at":"2026-08-06T17:59:32.981542Z","title":"Vision transformer pruning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:32.981542Z"},"links":{"cited_paper":"/paper/2104.08500","citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:af3a790d6fa0a81b4cb67039066154ae51dd5f9d626ffbfd6ac78fdf569a8b68","observation_id":"89b80900-9961-43bc-bdf6-4d85d5cf9259","resolution":{"observed_at":"2026-08-06T17:59:32.981542Z","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":"2026-08-06T17:59:33.521710Z","title":"Discrimination-aware channel pruning for deep neural networks","venue":null,"work_id":"092564d1-a0c3-4573-b754-10ac0c430efa","year":2018},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:33.056178Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:d4792e237ce2483b94f3d059933fe8e1020012649d4d81c9d90c37efe0bb5e46","observation_id":"28ddd627-74d3-42be-b02c-1316a803741f","resolution":{"observed_at":"2026-08-06T17:59:33.644190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-06T17:59:33.134847Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-06T17:59:33.134847Z"},"links":{"citing_paper":"/paper/2507.09514"},"observation_digest":"sha256:75de044e74327af01e786cfb91e163e55db44cb5c696c80ec020fe259b457ee9","observation_id":"0ef91300-b866-4778-b562-d7c57d039093","resolution":{"observed_at":"2026-08-06T17:59:33.134847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.09514","last_updated":"2025-07-13T06:49:32Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-18T10:40:09.729689Z","submitted_at":"2025-07-13T06:49:32Z","title":"QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":1,"verified_fuzzy":32},"total_outbound_references":68},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2507.09514."}