{"as_of":"2026-08-09T18:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9ac10e58006158c7d9d59f98a6a5bf7d8bebc22205350fb63588d3fa88e21dca","coverage":[{"denominator":130,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T22:37:55.212998Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.15699/citation-record","integrity":"/paper/2607.15699/integrity","json":"/paper/2607.15699/citation-record.json","paper":"/paper/2607.15699"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T22:37:42.940948Z","title":"2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:42.940948Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f3f8f07827dab047837094a42317686b83a1134d859421242ccf1ce1c805826a","observation_id":"5ec27136-a547-42e7-a008-4a3dd3f8891c","resolution":{"observed_at":"2026-08-01T22:37:42.940948Z","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":"2026-08-01T22:37:43.012014Z","title":"2017 IEEE International Conference on Robotics and Automation (ICRA) , pages=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.012014Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:ee1cbd6c0cee2dbf49a985caf2a8d6eded93d5e70e5cb8aaff685736abfdad83","observation_id":"ac591e66-582d-431e-a71c-f46d10c550a1","resolution":{"observed_at":"2026-08-01T22:37:43.012014Z","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":"2026-08-01T22:37:43.120121Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.120121Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f7fd2c78c7bc7d3c9fa5f589f156042e149533393e24b26800aa07efcdde4f56","observation_id":"3bcd5266-6555-4d8a-b4ff-81638a48902f","resolution":{"observed_at":"2026-08-01T22:37:43.120121Z","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":"2026-08-01T22:37:43.260256Z","title":"31st British Machine Vision Virtual Conference (BMVC 2020) , pages=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.260256Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:9a42b30b118ed5b7285c66fba72749f9d7a2f263a8fe546c46137e0da6f36a2f","observation_id":"9aec7f3b-359a-47a6-8b18-a6e848d3ddec","resolution":{"observed_at":"2026-08-01T22:37:43.260256Z","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":"2026-08-01T22:37:43.349407Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.349407Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:350a8662703740c9a7090a8dba92437bcb7b599ef19d9bc131b342f2fbb6ac1a","observation_id":"f3a5cfc4-65e9-4f85-9268-bd1b5d36f16a","resolution":{"observed_at":"2026-08-01T22:37:43.349407Z","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":"2026-08-01T22:37:43.540186Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.540186Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:126d7b703a0565bd87fb563fecd36a4219feb439a74969253efe02d661e040ad","observation_id":"0581c000-3ca8-4519-acba-a00d005b4546","resolution":{"observed_at":"2026-08-01T22:37:43.540186Z","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":"2026-08-01T22:37:43.700699Z","title":"European conference on computer vision , pages=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.700699Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:b9ed84596b8bf11840ed05a47079ea83c1a6bcb05eb900e6c6a1510ccbb1eaf7","observation_id":"3d61839f-90c5-4f8d-97f0-d700a221261e","resolution":{"observed_at":"2026-08-01T22:37:43.700699Z","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":"2026-08-01T22:37:43.792814Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.792814Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:cba2e25a5cb7862276e792fc02de8240d9c251329a08f54543b5529336ef0073","observation_id":"4bfdc47f-884c-4bc7-b92c-1bf835498106","resolution":{"observed_at":"2026-08-01T22:37:43.792814Z","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":"2026-08-01T22:37:43.918193Z","title":"2024 IEEE International Conference on Robotics and Automation (ICRA) , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:43.918193Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:e3fcb25c04b83befb5f483b43ba4fce01cb2a35c5549ad4ba4a949291b56bfc5","observation_id":"ad2657a6-5327-432d-a432-1c2ca91bae4b","resolution":{"observed_at":"2026-08-01T22:37:43.918193Z","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":"2026-08-01T22:37:44.040699Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.040699Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:2426dfb57a4bf12e1cd9f8fe2ce43f15a655d1fd54c2183f7081a6d9399528f6","observation_id":"fc69624f-a325-48d5-92f6-c1184afc603c","resolution":{"observed_at":"2026-08-01T22:37:44.040699Z","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":"2026-08-01T22:37:44.190669Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.190669Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:a3c392286414f58d2443dba14027c6ba69e66f7f1b3b2bcc66c756f3a3a2678d","observation_id":"06470f14-6a82-4ad4-b989-5358805e5bf3","resolution":{"observed_at":"2026-08-01T22:37:44.190669Z","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":"2026-08-01T22:37:44.375049Z","title":"IEEE transactions on pattern analysis and machine intelligence , volume=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.375049Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:8fd0debc35622411864d3698216d30373be43f0f6e71298ba0a583b2a9fde9ce","observation_id":"d481393c-ecff-4b45-9eea-3fe6a7231d4c","resolution":{"observed_at":"2026-08-01T22:37:44.375049Z","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":"2026-08-01T22:37:44.585842Z","title":"European conference on computer vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.585842Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:33c9da0cc751bc7096b2a9abebbbab51011ad52e0163ccd96dece8dfe965130d","observation_id":"4fcb77ab-4575-429b-9ef1-5b0f42439e87","resolution":{"observed_at":"2026-08-01T22:37:44.585842Z","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":"2026-08-01T22:37:44.742974Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.742974Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:dd99b84cdd4af50bf7eba3804c182e78f73abf9ceb86c2d8136a3160cb1569ff","observation_id":"48dab548-f274-4f30-9d94-d8c742c8ac3c","resolution":{"observed_at":"2026-08-01T22:37:44.742974Z","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":"2026-08-01T22:37:44.903380Z","title":"Proceedings of the European Conference on Computer Vision (ECCV) , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:44.903380Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:195c05f8bc37c37b33f74b771993a52edae703427052fdef019a44ef29470427","observation_id":"9962f7c2-cc5e-415b-a94e-c12306f09cbe","resolution":{"observed_at":"2026-08-01T22:37:44.903380Z","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":"2026-08-01T22:37:45.057184Z","title":"International Journal of Computer Vision , volume=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.057184Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:9d013168e1bea4d065f3d97a6ae055ee6cc4193d5239682c8c82ad02cdf56784","observation_id":"5d56e820-04ac-4575-a2ff-6acf23bf24b8","resolution":{"observed_at":"2026-08-01T22:37:45.057184Z","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":"2026-08-01T22:37:45.194917Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.194917Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:3792e2313be8771ac251a9663831c4dfa681ed17a33f47e261c91c8a7ddc14da","observation_id":"8335257f-0cd8-45d2-9f57-395937d0c06e","resolution":{"observed_at":"2026-08-01T22:37:45.194917Z","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":"2026-08-01T22:37:45.323390Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.323390Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:0d015573ff6c30dca29f0e95f92945639a6ad6773f83ec4b1350ffff53028694","observation_id":"16e45e1d-6210-467c-bcac-82635efd5fd0","resolution":{"observed_at":"2026-08-01T22:37:45.323390Z","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":"2026-08-01T22:37:45.443443Z","title":"nature , volume=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.443443Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:50805449ee41f1f7be45c0cf741d2d2abbde6ac4c1a3e1c826c6f9f2903f81cb","observation_id":"e20d1017-fafa-4aab-9fc7-e541a58b918f","resolution":{"observed_at":"2026-08-01T22:37:45.443443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-01T22:37:45.512005Z","title":"arXiv preprint arXiv:1707.06347 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.512005Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:59334115834ea3fd60c2a72aaa576e77abc3788ca2c4837fae9190e7d27f2950","observation_id":"76db5cf3-8057-4b72-a4d0-e3fee601b4ed","resolution":{"observed_at":"2026-08-01T22:37:45.512005Z","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":"2026-08-01T22:37:45.597032Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.597032Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f51eb45b8407a903e92f537f4d9ce45d98cc8aace7e6487f38e77f202a7ea540","observation_id":"2424d0aa-01f3-4e87-b12d-24b4f6b97527","resolution":{"observed_at":"2026-08-01T22:37:45.597032Z","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":"2026-08-01T22:37:45.666217Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.666217Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:cab4b2df4ccfc8479adc1ef2c889e061e6df5dffd5be09ab781cc9c2a3350e09","observation_id":"27b6f16e-c2c1-4e15-a283-1d5da61a6426","resolution":{"observed_at":"2026-08-01T22:37:45.666217Z","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":"2026-08-01T22:37:45.774936Z","title":"The International journal of robotics research , volume=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.774936Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:9087c871e7c1d3b453452170a2d913c04b0083b9339814f8e7982379eec3108a","observation_id":"a34195ff-8c1b-402b-a2eb-2fad7e376628","resolution":{"observed_at":"2026-08-01T22:37:45.774936Z","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":"2026-08-01T22:37:45.855190Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.855190Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:eee8cf50c54a32fefbadce35b456860384750c1e6a74a8198fc63f9aa5f1e9f5","observation_id":"a09a63a8-e131-4aae-a5e2-dabcbcc3555f","resolution":{"observed_at":"2026-08-01T22:37:45.855190Z","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":"2026-08-01T22:37:45.945131Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:45.945131Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:30982b47ebda4eff7a55af7102b69bab9c11864a99acb6203df7df292f1cf4fd","observation_id":"b915d057-3ab5-45e9-b4e0-20f5e5067111","resolution":{"observed_at":"2026-08-01T22:37:45.945131Z","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":"2026-08-01T22:37:46.038908Z","title":"European conference on computer vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.038908Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:72661ce08e69a0977d76ba7712654d958d6a029ac49affe4277e8de1c73f2bf1","observation_id":"5dd3562c-b910-4aa3-8a5f-9d8b99d4c071","resolution":{"observed_at":"2026-08-01T22:37:46.038908Z","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":"2026-08-01T22:37:46.184402Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.184402Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:cd7ed9fbb6736a38a7603f0f888cccc5d81c264d8dde5adb986aa9fe1d9eac60","observation_id":"f4482e74-d7e2-4685-9762-fc176e0848c9","resolution":{"observed_at":"2026-08-01T22:37:46.184402Z","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":"2026-08-01T22:37:46.364596Z","title":"2018 International Conference on 3D Vision (3DV) , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.364596Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:45472954bced651c86aa5275463fe4b256ac5acacca02b8bb15bbf669895840c","observation_id":"570cdde3-8c15-4f16-bb3c-19e9a108a3b0","resolution":{"observed_at":"2026-08-01T22:37:46.364596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.04536","last_updated":"2021-07-09T16:41:20Z","snapshot_observed_at":"2026-08-09T13:25:10.280801Z","submitted_at":"2021-07-09T16:41:20Z","title":"Event-Based Feature Tracking in Continuous Time with Sliding Window Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.04536","snapshot_observed_at":"2026-08-01T22:37:46.522469Z","title":"arXiv preprint arXiv:2107.04536 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.522469Z"},"links":{"cited_paper":"/paper/2107.04536","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4382d2b5569a4d39a58c2c88993321687e4f910d3aab870f89dbc2265bf6e831","observation_id":"2b6eabff-af5e-4d01-b51a-76aaeadaff09","resolution":{"observed_at":"2026-08-01T22:37:46.522469Z","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":"2026-08-01T22:37:46.650607Z","title":"Authorea Preprints , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.650607Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:701f26732905e6d536bbcd052eb6658d12603137c004b25fc0fc95dcfe9e38f6","observation_id":"0c04b740-69c7-4aba-85eb-3f21d6b834a1","resolution":{"observed_at":"2026-08-01T22:37:46.650607Z","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":"2026-08-01T22:37:46.756361Z","title":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.756361Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:401c5bfe520e3227cd788ca896d39ed39a990572d86abf586c38bce28b3ec56b","observation_id":"6a52b8f2-67ce-4ade-8ee2-1f2c343133b8","resolution":{"observed_at":"2026-08-01T22:37:46.756361Z","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":"2026-08-01T22:37:46.873937Z","title":"Robust feature tracking in dvs event stream using b","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:46.873937Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:615fa6ec81ce70ce4c6bf410fffee351ab77946087757de9a4fe71e18deeb58e","observation_id":"23c142be-8caa-40e0-bb1c-47df4517aa0f","resolution":{"observed_at":"2026-08-01T22:37:46.873937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10593","last_updated":"2024-09-04T01:13:40Z","snapshot_observed_at":"2026-07-06T15:56:15.361460Z","submitted_at":"2023-07-20T05:15:03Z","title":"Asynchronous Blob Tracker for Event Cameras","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10593","snapshot_observed_at":"2026-08-01T22:37:47.015685Z","title":"arXiv preprint arXiv:2307.10593 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.015685Z"},"links":{"cited_paper":"/paper/2307.10593","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:30d6937cf4fa55a928a9d602f919756a64fda15092bc3d96e2610e131f8a216a","observation_id":"c92f122b-4691-48ea-9cd6-60865055f887","resolution":{"observed_at":"2026-08-01T22:37:47.015685Z","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":"2026-08-01T22:37:47.093944Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.093944Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:7b0ec131b89757e6b8ce735f9ce796332ea57a0ce0c3cf3631d7f8bf63eda6db","observation_id":"ab6a5005-06fc-4643-972e-6bade846d87e","resolution":{"observed_at":"2026-08-01T22:37:47.093944Z","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":"2026-08-01T22:37:47.293912Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.293912Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4505f08375639e8bc31cef5cf58458b628f34d9b2e3b0f512336c220c411f8ec","observation_id":"bfd2fe62-6998-4e28-8b78-b165f0ddf8df","resolution":{"observed_at":"2026-08-01T22:37:47.293912Z","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":"2026-08-01T22:37:47.413030Z","title":"Proceedings of the IEEE/CVF international conference on computer vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.413030Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:9cfeaecab014b0b2b42ba35bd148a17e13e3c5f816dc217cd57cad5caaa62dac","observation_id":"9d09b075-8221-4125-afe5-884ed97222b0","resolution":{"observed_at":"2026-08-01T22:37:47.413030Z","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":"2026-08-01T22:37:47.520075Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.520075Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:65768e8d1766b027d8e6730e999de54bdd8e5d0c07c282a1b8f673b8676cab93","observation_id":"87e05135-a72f-478e-89f4-4ad049cd097f","resolution":{"observed_at":"2026-08-01T22:37:47.520075Z","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":"2026-08-01T22:37:47.644770Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.644770Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f452b862f1ed9b9bb2bacd64c39a47edbe0f5109363196472e51b493450f33d0","observation_id":"b6871191-9170-4199-8a11-66bcccbf8a7b","resolution":{"observed_at":"2026-08-01T22:37:47.644770Z","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":"2026-08-01T22:37:47.761413Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.761413Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:de7be1b48d80790b50128bd40833e0427fc214acfc5c5a8ecf3ba889fdd37bd6","observation_id":"ea485b9d-c6e8-4ef9-91f5-a468dce71b1e","resolution":{"observed_at":"2026-08-01T22:37:47.761413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.04511","last_updated":"2022-08-09T02:34:53Z","snapshot_observed_at":"2026-07-06T13:40:05.327378Z","submitted_at":"2022-08-09T02:34:53Z","title":"Object Detection with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.04511","snapshot_observed_at":"2026-08-01T22:37:47.885392Z","title":"arXiv preprint arXiv:2208.04511 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.885392Z"},"links":{"cited_paper":"/paper/2208.04511","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:ba66cd6bbd1fbaab97616429b4a5e6d8603a5a7459fa1964a6bd5759fe70555a","observation_id":"6d1f3a27-d688-4c87-959e-0dd9713bfe4d","resolution":{"observed_at":"2026-08-01T22:37:47.885392Z","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":"2026-08-01T22:37:47.999184Z","title":"International conference on machine learning , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:47.999184Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:cd98a1738603dde08c43266af25e2d42d3eb1bffb0c93664ec33d4593170b13a","observation_id":"3b24b4c9-1e66-4b49-99b3-8cef7ab1631d","resolution":{"observed_at":"2026-08-01T22:37:47.999184Z","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":"2026-08-01T22:37:48.135190Z","title":"Conference on robot learning , pages=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.135190Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:277713ee598ff99f6ba525ebae4ddccfa1afe5f3398cf3f403b41be831c32cd3","observation_id":"49201b96-642c-4cc2-807e-3eb4a5c4f787","resolution":{"observed_at":"2026-08-01T22:37:48.135190Z","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":"2026-08-01T22:37:48.278721Z","title":"Proceedings of the IEEE international conference on computer vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.278721Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4a55fecaf51f4c879bdbd2ae8eb53aa95964af82495f4e98a97f452f2dc76b22","observation_id":"7e3a7a94-a369-400e-bbd1-68c13f8920a7","resolution":{"observed_at":"2026-08-01T22:37:48.278721Z","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":"2026-08-01T22:37:48.421687Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.421687Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:ac2842b7810ba907cf6766649a060c802d9f481b63b475cce6cbc9cd3d73ea8e","observation_id":"3c025071-d042-4cf3-8f6f-081eca9a1f4c","resolution":{"observed_at":"2026-08-01T22:37:48.421687Z","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":"2026-08-01T22:37:48.509447Z","title":"European Conference on Computer Vision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.509447Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:00ba2a71d9dcef5c754d55f96d950f1ef5444f45d2ea737a1aeb71facdb880bf","observation_id":"ce42c3c0-7bcb-4fdb-b2bc-9a522d31c989","resolution":{"observed_at":"2026-08-01T22:37:48.509447Z","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":"2026-08-01T22:37:48.631724Z","title":"International Conference on Machine Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.631724Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:b6c3d88df579b8b8c5facaa884cf3c1b322369f282e045e8b6b84e0498039174","observation_id":"1edf2d35-e782-423b-a282-91d0344f6dc7","resolution":{"observed_at":"2026-08-01T22:37:48.631724Z","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":"2026-08-01T22:37:48.747760Z","title":"2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.747760Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:6597a141a098ca2ce13baca0effe91f395c059ca182b86ef4aecdac952efb806","observation_id":"4e2c2fc0-8414-4cc3-b490-5d64a2ad6917","resolution":{"observed_at":"2026-08-01T22:37:48.747760Z","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":"2026-08-01T22:37:48.832179Z","title":"Journal of machine learning research , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.832179Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:73d261785327b490dc29e46480b19d15bed62007ba43481c996a4b08c8e3ce19","observation_id":"d2fe547d-5ecf-4bcd-a8af-c63a5eb1bff2","resolution":{"observed_at":"2026-08-01T22:37:48.832179Z","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":"2026-08-01T22:37:48.883606Z","title":"IEEE Journal of Solid-State Circuits , volume=","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.883606Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:7969100640209f932a2ac44881df8b4f53c953a80afe01ecd8507dde70204662","observation_id":"f88e33e6-fe99-4a8d-b661-8c979ce6309a","resolution":{"observed_at":"2026-08-01T22:37:48.883606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.08375","last_updated":"2026-04-14T12:21:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-22T14:30:17Z","title":"Deep Learning using Rectified Linear Units (ReLU)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.08375","snapshot_observed_at":"2026-08-01T22:37:48.976949Z","title":"arXiv preprint arXiv:1803.08375 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:48.976949Z"},"links":{"cited_paper":"/paper/1803.08375","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:a62ef46790eae025292813a48f07f4af8b60ac062dc9408adfd5fb477ffb9078","observation_id":"078247fc-44d0-4e0d-8961-9fafcb09d0de","resolution":{"observed_at":"2026-08-01T22:37:48.976949Z","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":"2026-08-01T22:37:49.057131Z","title":"IEEE transactions on pattern analysis and machine intelligence , volume=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.057131Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4108d9110300ccba75ae4594b99015ea201ad8c14e0aeee98ce4aede0c13a9a8","observation_id":"6e3d5685-b588-4018-8e42-1a8b9bbd9729","resolution":{"observed_at":"2026-08-01T22:37:49.057131Z","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":"2026-08-01T22:37:49.126980Z","title":"Transactions of the association for computational linguistics , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.126980Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:5f2521f242ee50a3bf0d98ba10f11f9e1c801b0b590044a6f34666579a41f40e","observation_id":"2bd1f091-f707-4e7d-9872-836c730f90ed","resolution":{"observed_at":"2026-08-01T22:37:49.126980Z","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":"2026-08-01T22:37:49.216364Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.216364Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:ec0cafdbf652b7e66ee0c767f1035c50c9cb7910fb1a977b62a5ccc868561eef","observation_id":"c38603be-5e32-4680-aa70-d3c74967b043","resolution":{"observed_at":"2026-08-01T22:37:49.216364Z","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":"2026-08-01T22:37:49.327793Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.327793Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:2287d34e611d9364eaa9a439bdb998fdc86686eea39f43315163df908c5d1a9d","observation_id":"a7336a07-6dc4-4279-9337-691b647b617b","resolution":{"observed_at":"2026-08-01T22:37:49.327793Z","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":"2026-08-01T22:37:49.471419Z","title":"2022 International Conference on Robotics and Automation (ICRA) , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.471419Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:8a55ee724742c074c6fe935ca203eb1e00b3e2271e8079731108d8b7a3812f2a","observation_id":"cd66d407-60fa-449e-a360-afe0e1afb567","resolution":{"observed_at":"2026-08-01T22:37:49.471419Z","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":"2026-08-01T22:37:49.597001Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.597001Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:99dea0424dd18e8bd39250e16b780a352ea87b2765f53b431f563618b12f88d7","observation_id":"28c6c208-f367-48d9-b468-5e712ab991ad","resolution":{"observed_at":"2026-08-01T22:37:49.597001Z","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":"2026-08-01T22:37:49.732326Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.732326Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:477f2cccfcd6592ecd50d7a48142264d640cb4243f92ed64d420d342dedd39cf","observation_id":"05ce556d-99f0-4120-bcea-22838a3959c4","resolution":{"observed_at":"2026-08-01T22:37:49.732326Z","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":"2026-08-01T22:37:49.843259Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.843259Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:c277446c25b6254bf9ec2c14515d9b9e103b18e382e027541c058d065b331845","observation_id":"a828c4b0-30f8-4e5f-8fb1-0739e578c07f","resolution":{"observed_at":"2026-08-01T22:37:49.843259Z","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":"2026-08-01T22:37:49.954226Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:49.954226Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:8bd37682547dc8168f4e65089c3f559aea8901644aae19d43038e147b9cc803f","observation_id":"50e67a0d-8f4a-4d29-9abd-599779d724e3","resolution":{"observed_at":"2026-08-01T22:37:49.954226Z","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":"2026-08-01T22:37:50.072244Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.072244Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4cca40459c64a02eef3fc25f99f0c6b1a3832e9c322c0f67954611841848c8b0","observation_id":"740fde60-dde7-4c53-8942-8d5ce306d6b5","resolution":{"observed_at":"2026-08-01T22:37:50.072244Z","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":"2026-08-01T22:37:50.241472Z","title":"arXiv preprint arXiv:2512.01188 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.241472Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:c59f550b51814f8e4c7337e607bd88c0bfcf4002881b8bcef1aba96aef87c9ea","observation_id":"15561da1-a101-4a83-ae23-0aa1215cfb8c","resolution":{"observed_at":"2026-08-01T22:37:50.241472Z","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":"2026-08-01T22:37:50.426147Z","title":"2020 IEEE International Conference on Robotics and Automation (ICRA) , pages=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.426147Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:795f6d74ca9ce869a98a057891050c3fa893702db6b9a0af5c0da606a39c8a3f","observation_id":"042fc716-d799-4425-b470-b494f0abdec0","resolution":{"observed_at":"2026-08-01T22:37:50.426147Z","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":"2026-08-01T22:37:50.553752Z","title":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.553752Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:e32f071e9fe958e67f4a35685d11987ba0fcbdd5c2c8e45af3a1de12840c1d72","observation_id":"82701853-89bc-4cb5-a450-b921b1e76fba","resolution":{"observed_at":"2026-08-01T22:37:50.553752Z","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":"2026-08-01T22:37:50.700636Z","title":"Neural networks , volume=","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.700636Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:2dba9b5b9a9fc11404a18b4976970091b77a0db662d74b47ef9ea01e6c90266f","observation_id":"cd51f33b-aa05-41e7-a0db-a78d8fc3e388","resolution":{"observed_at":"2026-08-01T22:37:50.700636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11488","last_updated":"2024-06-12T13:58:19Z","snapshot_observed_at":"2026-07-06T15:44:35.147343Z","submitted_at":"2023-06-20T12:20:23Z","title":"Informed POMDP: Leveraging Additional Information in Model-Based RL","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11488","snapshot_observed_at":"2026-08-01T22:37:50.872210Z","title":"arXiv preprint arXiv:2306.11488 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.872210Z"},"links":{"cited_paper":"/paper/2306.11488","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:23fc9d3849798eb829b2d67c5a9409e77b3cc2162ddf0b236fbe627159ec1783","observation_id":"cf35112e-ae92-4366-bc79-48afaed7bb41","resolution":{"observed_at":"2026-08-01T22:37:50.872210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14853","last_updated":"2024-05-23T17:57:14Z","snapshot_observed_at":"2026-07-06T18:18:51.814306Z","submitted_at":"2024-05-23T17:57:14Z","title":"Privileged Sensing Scaffolds Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14853","snapshot_observed_at":"2026-08-01T22:37:50.990637Z","title":"arXiv preprint arXiv:2405.14853 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:50.990637Z"},"links":{"cited_paper":"/paper/2405.14853","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:94855c0900242e94a296ed77bf7bc17ffd1a2a727c500205bcb694712809825c","observation_id":"82020d3b-83e0-4f49-a0ed-103ebb8185d4","resolution":{"observed_at":"2026-08-01T22:37:50.990637Z","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":"2026-08-01T22:37:51.144808Z","title":"Conference on Robot Learning , pages=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.144808Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:98c26e520aa876b678a1d70634ba0e3df375a25b5f8a74213763da1d7c44d4d2","observation_id":"b8e6d227-2dcc-42bb-b658-abcab05bbe4c","resolution":{"observed_at":"2026-08-01T22:37:51.144808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.06542","last_updated":"2017-10-18T01:10:37Z","snapshot_observed_at":"2026-07-06T06:04:47.929758Z","submitted_at":"2017-10-18T01:10:37Z","title":"Asymmetric Actor Critic for Image-Based Robot Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.06542","snapshot_observed_at":"2026-08-01T22:37:51.261635Z","title":"arXiv preprint arXiv:1710.06542 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.261635Z"},"links":{"cited_paper":"/paper/1710.06542","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:320ee5df81142387223b6ffdcc0c5b47e2976474ac4c26a654d19035d99d460f","observation_id":"5db03be4-4fb0-47d0-bc08-9fba342177cb","resolution":{"observed_at":"2026-08-01T22:37:51.261635Z","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":"2026-08-01T22:37:51.402517Z","title":"Science robotics , volume=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.402517Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:1ad5c5a72f44dab9c34a2898012fc8acddb60ca9bd0492c4fccf960f75c7964b","observation_id":"3d68ffdd-225a-41f4-95c1-8eb9d7a57478","resolution":{"observed_at":"2026-08-01T22:37:51.402517Z","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":"2026-08-01T22:37:51.552302Z","title":"Machine learning proceedings 1990 , pages=","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.552302Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:070426366c490b83a0c164d4df1ef0bf8bb70dbfe2bb689d91e596abf1972f8a","observation_id":"08278957-7083-47f7-bf8c-d809d132a715","resolution":{"observed_at":"2026-08-01T22:37:51.552302Z","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":"2026-08-01T22:37:51.688394Z","title":"Transactions on Machine Learning Research , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.688394Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:e18d6951d4ab9adc3b481be7ce93c98bdcd23385c179f493d88ef4547ac2eff3","observation_id":"3cbe011a-8228-47e9-b6cf-d1f600f06668","resolution":{"observed_at":"2026-08-01T22:37:51.688394Z","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":"2026-08-01T22:37:51.858201Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:51.858201Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:e60cc00d55eea888301384f1b8c1fec1f3061cba7b48a5398b3facd0c504a47f","observation_id":"ac75971f-7267-4cfe-aae7-da533bd9067d","resolution":{"observed_at":"2026-08-01T22:37:51.858201Z","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":"2026-08-01T22:37:52.006090Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.006090Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:66eb275a4d9dc8da6bea8bf58a11e1b89a20dab0bc0b90a9ed43a1cabd833b0c","observation_id":"cd24f5e3-dd52-44fa-b970-adeb877c7cd2","resolution":{"observed_at":"2026-08-01T22:37:52.006090Z","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":"2026-08-01T22:37:52.143321Z","title":"The 8th Conference on Robot Learning , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.143321Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:ab83bebb5876a6be07be25f5d51556876d708b5cc7bc220b59eed39fdfeb6e4b","observation_id":"e6df8f62-dc5b-454d-87ec-8e9e523ab4bf","resolution":{"observed_at":"2026-08-01T22:37:52.143321Z","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":"2026-08-01T22:37:52.285539Z","title":"European Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.285539Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:d60e50d154fbd2669b603dbaf915c10f4b5b9a4434b71c12535b19339a9300b2","observation_id":"31e03b87-39fc-4103-9a9f-a3f6722385cd","resolution":{"observed_at":"2026-08-01T22:37:52.285539Z","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":"2026-08-01T22:37:52.400451Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.400451Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:8e7538b0ff4a2ff5d8e38826b2c1a4787aa25883ce5a2d9757052ea96ece777e","observation_id":"8ddcc05c-8a7d-45e1-ac8d-ed1c8a449eac","resolution":{"observed_at":"2026-08-01T22:37:52.400451Z","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":"2026-08-01T22:37:52.485505Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.485505Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:fb88591abb29046f9188fef452c292208c0db51182343b57b0da8420dbc290c6","observation_id":"2f438457-84e3-4192-ac1b-f67efa616c24","resolution":{"observed_at":"2026-08-01T22:37:52.485505Z","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":"2026-08-01T22:37:52.602294Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.602294Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:998512009e8a2829aa4bbc3746fc033474362d105b05477ac462d733d2564f18","observation_id":"523a06f0-e70b-4bfa-9fd3-3969ba01c918","resolution":{"observed_at":"2026-08-01T22:37:52.602294Z","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":"2026-08-01T22:37:52.774587Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.774587Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:6785a85e04e0874715d3811a8f548da9189125cebe0ef139a201cdca7113dc74","observation_id":"df086667-8053-44bb-8a80-9267c161dfc5","resolution":{"observed_at":"2026-08-01T22:37:52.774587Z","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":"2026-08-01T22:37:52.914486Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:52.914486Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:9f39d0c9b5658ec04885e4b6f2266d3d5cf9b2a3b741ff14f0c6bbcf13fe75c0","observation_id":"aa6cc626-6e0f-4081-8886-9a6a6a6d699b","resolution":{"observed_at":"2026-08-01T22:37:52.914486Z","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":"2026-08-01T22:37:53.025075Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.025075Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4e9172c4e58b1d82336d3f3f10d46f7b769664c4421fbcc981c562955683333b","observation_id":"31334791-88f1-4d68-897e-c23cda219862","resolution":{"observed_at":"2026-08-01T22:37:53.025075Z","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":"2026-08-01T22:37:53.112391Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.112391Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:000786790a28b04057e34f1551f32b744a487e69623da9db71d64ca02345d32e","observation_id":"210b2713-365d-4380-80c8-d2f1c39544ea","resolution":{"observed_at":"2026-08-01T22:37:53.112391Z","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":"2026-08-01T22:37:53.226135Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.226135Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:1f67d6fc80ac2fc2d1517feb0c65bfef6bd0644d906f9094ba57090f07b7e2af","observation_id":"fc2e06c8-e49a-4816-810e-8ebde3d407ff","resolution":{"observed_at":"2026-08-01T22:37:53.226135Z","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":"2026-08-01T22:37:53.336583Z","title":"arXiv preprint arXiv:2410.15392 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.336583Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f5a800528f2e3f2c44a6c7c4c72c75133c002fe0351312723a8f8e412182713d","observation_id":"38c1bbe2-af71-43a5-a5f2-57431a5c15f9","resolution":{"observed_at":"2026-08-01T22:37:53.336583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13279","last_updated":"2025-05-20T07:45:25Z","snapshot_observed_at":"2026-08-07T15:43:06.225319Z","submitted_at":"2025-05-19T16:02:37Z","title":"Event-Driven Dynamic Scene Depth Completion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.13279","snapshot_observed_at":"2026-08-01T22:37:53.450299Z","title":"arXiv preprint arXiv:2505.13279 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.450299Z"},"links":{"cited_paper":"/paper/2505.13279","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:b4750306da5933c731e5410ce11a151eaa32083ab698a19aec7881b52884d399","observation_id":"ece3cce3-b658-4199-b186-1327d2ec5b6c","resolution":{"observed_at":"2026-08-01T22:37:53.450299Z","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":"2026-08-01T22:37:53.593713Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.593713Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f0fe11da20bfb3ea14362aebed09a422cf93c57f60c0e76cd527887c691c4893","observation_id":"fa4b54d1-f01c-42c7-b601-260eabfcf401","resolution":{"observed_at":"2026-08-01T22:37:53.593713Z","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":"2026-08-01T22:37:53.730376Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.730376Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:8fd5afcf665294c224ece8b418c83ea0beee5829c59d75bdee723f39783ee71e","observation_id":"e72f0b65-b0b5-450c-b2a0-449c4e2dae20","resolution":{"observed_at":"2026-08-01T22:37:53.730376Z","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":"2026-08-01T22:37:53.810702Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.810702Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:4d838aba5eaeaaff17a172c449528fe6d4107d64739b3c5836309748d58ab2b2","observation_id":"a5cc473b-bb20-4311-976a-348c7b5b7318","resolution":{"observed_at":"2026-08-01T22:37:53.810702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.03467","last_updated":"2022-05-06T20:09:18Z","snapshot_observed_at":"2026-08-09T04:12:12.795228Z","submitted_at":"2022-05-06T20:09:18Z","title":"EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.03467","snapshot_observed_at":"2026-08-01T22:37:53.986602Z","title":"arXiv preprint arXiv:2205.03467 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:53.986602Z"},"links":{"cited_paper":"/paper/2205.03467","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:781cd8e1338508a0632d65da8c1a55400c7f168a542d95e326608d71c0227a90","observation_id":"1f0fcb44-4968-4abd-adf8-915c8bf74029","resolution":{"observed_at":"2026-08-01T22:37:53.986602Z","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":"2026-08-01T22:37:54.137964Z","title":"Proceedings of the Computer Vision and Pattern Recognition Conference , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.137964Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:bae16011e6063cd3916af60172999af0e5e9f613f730d2f1fec5cbeb05cadfc9","observation_id":"622deb46-1822-49a0-8f8b-ea9f53300fcc","resolution":{"observed_at":"2026-08-01T22:37:54.137964Z","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":"2026-08-01T22:37:54.265445Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.265445Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:494d1e8952ac97fdc270efcd9e1a9b2329fc41462986d583009e5d7240b21f83","observation_id":"91bfa851-aa2c-4e89-9b7d-213c62535297","resolution":{"observed_at":"2026-08-01T22:37:54.265445Z","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":"2026-08-01T22:37:54.414193Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.414193Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:f37e27a97173abf8e089740d4f8c7569ba340295c8632a95b3fe84921749d5c3","observation_id":"e085bf21-0344-4360-b2c5-f73230060795","resolution":{"observed_at":"2026-08-01T22:37:54.414193Z","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":"2026-08-01T22:37:54.521577Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.521577Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:7aa9650a86abf1921ff4d0892d78e20c1510781c08b30f3af3121f48357762db","observation_id":"c62ecb6d-df7c-44b7-8fdd-1f2df6e3eff0","resolution":{"observed_at":"2026-08-01T22:37:54.521577Z","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":"2026-08-01T22:37:54.603633Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.603633Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:d799ead89f1468a9dfef7a507fbfd3961bba22594f24dfe98388989fb248a172","observation_id":"8a211742-f3f7-4730-b442-8a53a1fad606","resolution":{"observed_at":"2026-08-01T22:37:54.603633Z","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":"2026-08-01T22:37:54.752012Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.752012Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:70242bd8ba2b315ff5b0825382e045fc029f257bea07d64f515f4ad26c7644f9","observation_id":"5787a5ea-6ae3-4e00-b0f6-6100e859af4f","resolution":{"observed_at":"2026-08-01T22:37:54.752012Z","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":"2026-08-01T22:37:54.866130Z","title":"Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:54.866130Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:21ae2a3581f5298f64cca1da1ca46c76ad3c189165c3759b774bd95ca2e4760c","observation_id":"728d57bf-3f10-4970-aeb4-8eebf34614f4","resolution":{"observed_at":"2026-08-01T22:37:54.866130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02438","last_updated":"2018-10-20T18:55:07Z","snapshot_observed_at":"2026-08-08T15:29:26.223468Z","submitted_at":"2015-06-08T11:12:48Z","title":"High-Dimensional Continuous Control Using Generalized Advantage Estimation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02438","snapshot_observed_at":"2026-08-01T22:37:55.001964Z","title":"arXiv preprint arXiv:1506.02438 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:55.001964Z"},"links":{"cited_paper":"/paper/1506.02438","citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:2eb02c28f4881f1678cabc758e12e289164b2ca153abe5dc53ad7a9dd29b22f4","observation_id":"783eb1f8-16fe-4898-8d71-c2eb01f78fde","resolution":{"observed_at":"2026-08-01T22:37:55.001964Z","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":"2026-08-01T22:37:55.063648Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:55.063648Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:a088846a19e69c3e0669e1a3dcd58919246664e9dc0d93d17f579d4b0272e502","observation_id":"becfcf5c-346b-4dec-9dea-466fe16e9b8c","resolution":{"observed_at":"2026-08-01T22:37:55.063648Z","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":"2026-08-01T22:37:55.134229Z","title":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:55.134229Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:35be73245d7e36baa7a019cb0676333806a4a728010c29b25b709398c04b3726","observation_id":"2a71a9be-fb32-4097-854a-ca5d8cffe6bd","resolution":{"observed_at":"2026-08-01T22:37:55.134229Z","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":"2026-08-01T22:37:55.212998Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-01T22:37:55.212998Z"},"links":{"citing_paper":"/paper/2607.15699"},"observation_digest":"sha256:099fd5ad612db2a61994d56dfa0b00575cad1afa13bb9e94e5d92592b0f090e4","observation_id":"7937138b-3df0-4bac-a670-6b30f94acba2","resolution":{"observed_at":"2026-08-01T22:37:55.212998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.15699","last_updated":"2026-07-17T07:18:07Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T09:58:10.589527Z","submitted_at":"2026-07-17T07:18:07Z","title":"GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":100,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":130},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 100 of 130 outbound references and 0 inbound Pith citation observations for arXiv:2607.15699."}