{"as_of":"2026-08-08T09:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9ec915be8bbb22b9bc146d7d3d34a65c0cbecc7f4c0e61f110212d2a677dabc","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:28:29.856585Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.07878/citation-record","integrity":"/paper/2506.07878/integrity","json":"/paper/2506.07878/citation-record.json","paper":"/paper/2506.07878"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.737993Z","title":"Speech recognition using biologically-inspired neural net- works","venue":null,"work_id":"f3847c78-a992-4903-9c08-057f735cedb7","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.052742Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:4f4ad79724177182adc96642f73272bd8a3cb0851c7ad3d50f4d52441acedc90","observation_id":"f612e2e4-d805-40a3-99da-ddf5866efde9","resolution":{"observed_at":"2026-08-07T05:28:34.838681Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.549206Z","title":"Optical flow es- timation from event-based cameras and spiking neural net- works","venue":null,"work_id":"6e0a3e73-08b8-4aed-92bd-436577370762","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.115645Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:4d771befa920a77ef64057e66ac502e50523dd5f668d14447d2810a564bf7f57","observation_id":"3364c83c-966c-4e33-8a10-53d670dc5a94","resolution":{"observed_at":"2026-08-07T05:28:34.631362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.390826Z","title":"Hugnet: Hemi- spherical update graph neural network applied to low-latency event-based optical flow","venue":null,"work_id":"4dbecf83-5ea2-4407-a4a0-22f1739dadaf","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.190997Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:be726f181feced8b719909a162c323007ce8de98f021df56b627bbe2d867fbe3","observation_id":"2eb28418-d17e-47a5-becd-0bdd7229cfbe","resolution":{"observed_at":"2026-08-07T05:28:34.463062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.225315Z","title":"Spatio-temporal recurrent networks for event-based optical flow estimation","venue":null,"work_id":"bc938319-4678-4a88-91e7-0a98ed51cd46","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.261835Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:ce96cb7d6f4678320d5082a60530d2ba9fa0d73a09b9d9086d7716f9c655dc78","observation_id":"43993dc9-f81c-4622-b129-5286280d3b8d","resolution":{"observed_at":"2026-08-07T05:28:34.316191Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:34.034752Z","title":"Flownet: Learn- ing optical flow with convolutional networks","venue":null,"work_id":"7e4e7b5e-85a0-4490-b6e5-3cd1cc0e7dab","year":2015},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.320810Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:a1b17a3e5d3ad8a41cc6f210911cb765c39074ea18b88f0c73b2cbdb1825bb23","observation_id":"07a0c54a-6dc7-48f7-b78a-e3bb7df7ae66","resolution":{"observed_at":"2026-08-07T05:28:34.130205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.859727Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"629ec971-b61b-4f9d-addc-d2e326dbab24","year":2021},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.394244Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:a65353a142ef8d76a9ddec69644f0423a9997ab6d3ccc655153ed45048c66d8a","observation_id":"4a646a55-9ec1-4bff-87bc-c6d9919ff105","resolution":{"observed_at":"2026-08-07T05:28:33.953018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.714681Z","title":"A unifying contrast maximization framework for event cam- eras, with applications to motion, depth, and optical flow es- timation","venue":null,"work_id":"2163afb7-1e0f-4a53-9633-8053c0b8e42e","year":2018},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.464688Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:da87a957587c950254b8326c4c0b4cf8f8be0a71a3468777f6ee03e14745861c","observation_id":"fbc0ce93-5bc2-491e-8dc6-8e06d5f5f5c1","resolution":{"observed_at":"2026-08-07T05:28:33.784457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.541738Z","title":"Event-based vision: A survey","venue":null,"work_id":"ea69bfb1-0199-42a3-bdb4-0717e3c19622","year":2020},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.534471Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:e7743bd76e4a1e487321ffe1e273a3d129babc88b1e75be3776d642d7d475c97","observation_id":"e245535d-7db0-4525-bc96-a48991bafd5b","resolution":{"observed_at":"2026-08-07T05:28:33.619841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.398587Z","title":"Dsec: A stereo event camera dataset for driv- ing scenarios","venue":null,"work_id":"65cab41a-0eb0-44c4-ae62-3261d7ab6567","year":2021},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.614858Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:cedc27819269abfbcb840661d93ae67cb491efcf863b45a362550e0e5c518e66","observation_id":"ba6c2164-f599-4513-b8ea-31c0d5db371e","resolution":{"observed_at":"2026-08-07T05:28:33.458475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.253674Z","title":"E-raft: Dense optical flow from event cam- eras","venue":null,"work_id":"04211754-3b15-494d-9392-d2d62676c421","year":2021},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.650926Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:08186af149f141a0ec60a44d7e7eadc87be2875baeaa519d16d455516802e6d5","observation_id":"8d3c50a7-590c-4127-9d31-864b826c8a05","resolution":{"observed_at":"2026-08-07T05:28:33.333366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:33.102982Z","title":"Dense continuous-time optical flow from event cameras","venue":null,"work_id":"cb63ee64-535f-47e4-8aa6-dedfb1f71665","year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.713799Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:3cbe11c7c55d1be8ce84cb949cf5400e0cfa3a6d50cafd27ca36d52e360c1c9a","observation_id":"6c6a99a1-c0ff-45e6-b955-361aa8aed451","resolution":{"observed_at":"2026-08-07T05:28:33.165799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-07T05:28:27.780704Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.780704Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:16961f4dd170128fee5bdf2c549a59781befb99cea03e93566b8e023567ff42b","observation_id":"65dbd927-5097-4b97-bb56-33167c670edc","resolution":{"observed_at":"2026-08-07T05:28:27.780704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.934688Z","title":"On the parameterization and initialization of diagonal state space models","venue":null,"work_id":"abe4f14f-525c-4fc8-ab37-4ee4fa6f838f","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.876551Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:2a0520beb3e6cd741502657e24514e0fd3ff981ef868997c3d1052e54b24662b","observation_id":"177aba2c-186b-4b03-bdcb-c4382e4356ff","resolution":{"observed_at":"2026-08-07T05:28:33.041631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.790329Z","title":"Efficiently mod- eling long sequences with structured state spaces","venue":null,"work_id":"12cb7f9e-e2b4-4105-817b-8a3cf3528a43","year":null},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:27.970176Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:38881e26db5b24fc6fbaafdebfac39331d86b011b9cc132840ebb8d1401c84ef","observation_id":"3ca7d826-84fa-4abe-b84e-e6d1781ead8f","resolution":{"observed_at":"2026-08-07T05:28:32.844926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.637184Z","title":"Self-supervised learning of event-based optical flow with spiking neural networks.Advances in Neural Infor- mation Processing Systems (NeurIPS), 34:7167–7179, 2021","venue":null,"work_id":"8c8d3fca-6090-4dd9-89af-7d5331fa735e","year":2021},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.072406Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:72dc1f37981728b6989c4c7cc4358cc90a098d1d58ee9c6c7f1fb53b81f125ac","observation_id":"6836e267-b532-4dbf-b621-5544ff77c73f","resolution":{"observed_at":"2026-08-07T05:28:32.729759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.504231Z","title":"Adaptive-spikenet: event-based optical flow estimation using spiking neural net- works with learnable neuronal dynamics","venue":null,"work_id":"64a3ba4d-63f4-4a6b-bc08-0e2537946f69","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.144818Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:b363f97dd04ecab458bb46494722b0745094639e63ff4a053e2e80e885e66d51","observation_id":"33b0c07e-5368-4f9a-bc86-564adf383530","resolution":{"observed_at":"2026-08-07T05:28:32.580526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.340579Z","title":"Spike- flownet: event-based optical flow estimation with energy- efficient hybrid neural networks","venue":null,"work_id":"53d53b85-3c67-4c3e-bac3-2771db0e27a7","year":2020},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.215387Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:c160e2518d27a7af7ce8c140292e1e26eee41b1c46bc2f0e84291971a326d55c","observation_id":"0f5655fa-5bd0-41ca-8f9f-c81be2402a1b","resolution":{"observed_at":"2026-08-07T05:28:32.417536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.207933Z","title":"Videomamba: State space model for efficient video understanding, 2024","venue":null,"work_id":"a58668cb-05be-4647-85c0-9a9257ff9ce1","year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.307731Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:206c83805bea49ff732d40694cdf12d37f26beb0b6bf12b3fb9d72cd9ddce847","observation_id":"5839ae86-68da-4190-a393-51c351f21199","resolution":{"observed_at":"2026-08-07T05:28:32.272393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.07716","last_updated":"2024-10-27T14:13:02Z","snapshot_observed_at":"2026-08-06T01:43:52.286841Z","submitted_at":"2023-03-14T09:03:54Z","title":"BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation","version":2},"cited_work":{"arxiv_id":"2303.07716","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.07716","snapshot_observed_at":"2026-08-07T05:28:30.521991Z","title":"BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation","venue":"cs.CV","work_id":"056d8db8-484e-45fb-bf3e-e196f61287e8","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.392512Z"},"links":{"cited_paper":"/paper/2303.07716","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:43196304f5915e63b25cc60f43b4e10ca8606cfd2243cd73217c32e82c26f11c","observation_id":"fd147fee-e82d-4122-8377-53391febf8fb","resolution":{"observed_at":"2026-08-07T05:28:30.579555Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11629","last_updated":"2023-08-21T07:07:43Z","snapshot_observed_at":"2026-08-06T03:57:34.041254Z","submitted_at":"2023-03-21T06:51:31Z","title":"TMA: Temporal Motion Aggregation for Event-based Optical Flow","version":2},"cited_work":{"arxiv_id":"2303.11629","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.11629","snapshot_observed_at":"2026-08-07T05:28:30.377544Z","title":"TMA: Temporal Motion Aggregation for Event-based Optical Flow","venue":"cs.CV","work_id":"e5227854-8803-46f5-b5a6-e69996bc4059","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.446404Z"},"links":{"cited_paper":"/paper/2303.11629","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:e1bd55104001901eca42c32529bca12772de4d46a6e4872e69c50204e7872160","observation_id":"d85850aa-f0bd-45fe-9f75-93cee0b68f28","resolution":{"observed_at":"2026-08-07T05:28:30.435579Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-07-06T17:17:31.008185Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-07T05:28:28.500438Z","title":"Vmamba: Visual state space model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.500438Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:aa008381db1534d2e43558d902fd0884df34b281eb569ed02bc84cf6c64b8da0","observation_id":"e6596e47-0baf-4236-bdbb-4418cf58bdf5","resolution":{"observed_at":"2026-08-07T05:28:28.500438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:32.021292Z","title":"Efficient meshflow and opti- cal flow estimation from event cameras","venue":null,"work_id":"edaf55e8-0387-4bce-b3a5-4ae3a1db7883","year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.582994Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:ae9041e5e6575d44787e14743f5948214f89196089cdf1cd18bbf8e1f5718b03","observation_id":"b68e09a1-f005-47c9-8e5c-1ab3a2c78253","resolution":{"observed_at":"2026-08-07T05:28:32.083567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:28.631918Z","title":"Back to event basics: Self-supervised learning of image reconstruc- tion for event cameras via photometric constancy","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.631918Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:7ea32b2a91616cbc0da6921c16ce3aa02e57dd77d6afc57ba6b6ea31274a6c98","observation_id":"1241211f-f10e-4e5e-9523-a4ea58f08f93","resolution":{"observed_at":"2026-08-07T05:28:28.631918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.05214","last_updated":"2023-09-27T15:21:35Z","snapshot_observed_at":"2026-07-06T15:00:35.606123Z","submitted_at":"2023-03-09T12:37:33Z","title":"Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow","version":2},"cited_work":{"arxiv_id":"2303.05214","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.05214","snapshot_observed_at":"2026-08-07T05:28:30.190305Z","title":"Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow","venue":"cs.CV","work_id":"a4b1398c-9862-46a3-955c-3ca4986cf4c4","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.683241Z"},"links":{"cited_paper":"/paper/2303.05214","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:2d03236b2ad80a038316ff736f650ebc8adbc92397f1d6550a54834501a88a22","observation_id":"2ca5524d-de66-470a-8461-41ff65f75190","resolution":{"observed_at":"2026-08-07T05:28:30.252580Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.815702Z","title":"Neuromorphic optical flow and real-time implementation with event cameras","venue":null,"work_id":"4b62bdb2-787f-42bf-b218-bbe28e1a7e4e","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.757890Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:b3faaea78af2c2ef75c9d3d93ebc5c14f4c738a2339542d19f5b28c2b58d0610","observation_id":"9231df3c-09d5-450b-8196-5a92d3bdd384","resolution":{"observed_at":"2026-08-07T05:28:31.889742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18508","last_updated":"2024-10-09T06:57:39Z","snapshot_observed_at":"2026-07-06T18:06:53.888474Z","submitted_at":"2024-04-29T08:50:27Z","title":"Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18508","snapshot_observed_at":"2026-08-07T05:28:28.840621Z","title":"Scalable event-by-event processing of neuromorphic sen- sory signals with deep state-space models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.840621Z"},"links":{"cited_paper":"/paper/2404.18508","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:7948289e66a96f1468ea9215a47e50d647314abac8d0a65e6dd3c9384ac0a329","observation_id":"8746b51b-3df3-476a-9184-fa4375c60de9","resolution":{"observed_at":"2026-08-07T05:28:28.840621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.654967Z","title":"Secrets of event-based optical flow","venue":null,"work_id":"68c91914-011b-4c6d-a2ef-8eba26e93da4","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:28.919224Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:5dc0e2de55c69aed13bea7e08b51911ec1c958ee2cfc8712ac40ba02979df76d","observation_id":"70c55dec-32f0-4464-a1c1-9830d8488d99","resolution":{"observed_at":"2026-08-07T05:28:31.752362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.507751Z","title":"Smith, Andrew Warrington, and Scott Linder- man","venue":null,"work_id":"a5dad760-4a57-412f-8d8c-e151c8379b61","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.000822Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:8f0b942c1288aa1e48ffe1656169071058790b8830a6e4d7f6574baa8f3f6500","observation_id":"ea57c3fb-f76a-481a-92fb-181f1d3d5f73","resolution":{"observed_at":"2026-08-07T05:28:31.583235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12326","last_updated":"2018-05-31T05:57:09Z","snapshot_observed_at":"2026-08-06T13:11:17.274369Z","submitted_at":"2018-05-31T05:57:09Z","title":"Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12326","snapshot_observed_at":"2026-08-07T05:28:29.084312Z","title":"Simultaneous opti- cal flow and segmentation (sofas) using dynamic vision sen- sor","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.084312Z"},"links":{"cited_paper":"/paper/1805.12326","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:03938ba5c04754a2a0936b6565a827f97687c5534dab810ddf8f4ce4d7e6af01","observation_id":"3675cf46-6320-453a-833d-d60868cc9c99","resolution":{"observed_at":"2026-08-07T05:28:29.084312Z","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":"2026-08-07T05:28:29.175011Z","title":"Raft: Recurrent all-pairs field transforms for optical flow","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.175011Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:6892efc30b9cf8a6536db3abd35f439db2f3be9896c984e1d5ea87568098ed21","observation_id":"f2194262-e50e-4fae-bef2-95d092678f33","resolution":{"observed_at":"2026-08-07T05:28:29.175011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.320439Z","title":"Sdformerflow: Spatiotem- poral swin spikeformer for event-based optical flow estima- tion, 2024","venue":null,"work_id":"db8ed60c-fd66-4e2b-a18b-427597c6e572","year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.258188Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:bca5f471122451fe97542c972e0d0054e015845cc755478135304acde0143f64","observation_id":"85f1e04a-4629-4c18-bcc7-50df88ea9bad","resolution":{"observed_at":"2026-08-07T05:28:31.424938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:29.314653Z","title":"Attention is all you need","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.314653Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:f3f72616fd8febbb5075646cdceebd8a7a0b8a778bb016f507127bab5c12618e","observation_id":"bd43b16f-b103-49ea-aeb1-b53e18f95772","resolution":{"observed_at":"2026-08-07T05:28:29.314653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:31.076278Z","title":"Learning dense and continuous optical flow from an event camera","venue":null,"work_id":"2220d101-c10b-4a5b-b5dc-ad9e36c21be7","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.389845Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:11334f4a822631664f658764e1684300fc1b0c4d2fd5c98d70c76f42347c5e42","observation_id":"ecfacef0-cacd-487e-9595-7bcc00504793","resolution":{"observed_at":"2026-08-07T05:28:31.169881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.13726","last_updated":"2024-05-05T08:17:30Z","snapshot_observed_at":"2026-08-03T19:20:10.461550Z","submitted_at":"2022-11-24T17:26:27Z","title":"Lightweight Event-based Optical Flow Estimation via Iterative Deblurring","version":4},"cited_work":{"arxiv_id":"2211.13726","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.13726","snapshot_observed_at":"2026-08-07T05:28:30.023134Z","title":"Lightweight Event-based Optical Flow Estimation via Iterative Deblurring","venue":"cs.CV","work_id":"113f97d5-878f-4359-841c-69966658f445","year":2022},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.444837Z"},"links":{"cited_paper":"/paper/2211.13726","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:c4c0585ecf83b7127338e5743e8b9cab96525e5ab6c622309a33210a8e930a96","observation_id":"40eb9980-a36f-4128-98ee-8929fbbe3269","resolution":{"observed_at":"2026-08-07T05:28:30.078223Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.928162Z","title":"Sa-flownet: Event-based self- attention optical flow estimation with spiking-analogue neu- ral networks","venue":null,"work_id":"53119842-6501-4f10-9564-4bee6ce7a49e","year":2023},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.522463Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:311494e062ed7b5b9f1c8c2a0564150e3962f21aa03fd2c5b72f9d9c17cd6d68","observation_id":"20b0ef50-8853-436b-a76f-56dffaa4069f","resolution":{"observed_at":"2026-08-07T05:28:30.984925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06898","last_updated":"2018-08-13T15:32:01Z","snapshot_observed_at":"2026-07-06T06:24:15.593553Z","submitted_at":"2018-02-19T22:47:52Z","title":"EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06898","snapshot_observed_at":"2026-08-07T05:28:29.615621Z","title":"Ev-flownet: Self-supervised optical flow estimation for event-based cameras","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.615621Z"},"links":{"cited_paper":"/paper/1802.06898","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:17571e8305b31ae18326966c12598b23fa22fc1bf43d29daa1633d88e9dea155","observation_id":"a3baa019-b0c9-45e3-b8e7-614dd370d70b","resolution":{"observed_at":"2026-08-07T05:28:29.615621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.782583Z","title":"Unsupervised event-based learning of optical flow, depth, and egomotion","venue":null,"work_id":"1e771e99-dd0b-443f-bc5d-f530b59677c2","year":2019},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.716052Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:ba9dbfe76e25daf485d1e46a61fb3fdec456bc6830603abb5e25d6bfe8a7bd37","observation_id":"90c021a7-bf2f-48cb-8fb9-5e14befb4297","resolution":{"observed_at":"2026-08-07T05:28:30.855708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-07-06T17:16:59.193820Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-07T05:28:29.779408Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.779408Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:2614743d125eedb2f2bafb8dad30ac423c032baa71d99ec0df00cb7395b04207","observation_id":"7aaafc9d-93c8-4997-807c-6b24b994e36f","resolution":{"observed_at":"2026-08-07T05:28:29.779408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:28:30.669963Z","title":"State space models for event cameras","venue":null,"work_id":"522d26f9-f277-4167-a3b9-74f4d9a4df45","year":2024},"citing_paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:29.856585Z"},"links":{"citing_paper":"/paper/2506.07878"},"observation_digest":"sha256:48e04cc386c1af69ec9f2988e6e69c09ddaf03d6dd92e082eafd65ceb2c08b56","observation_id":"75b2c04a-130e-496e-bea2-526049ea3a60","resolution":{"observed_at":"2026-08-07T05:28:30.699919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07878","last_updated":"2025-06-09T15:51:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T05:20:48.573038Z","submitted_at":"2025-06-09T15:51:06Z","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":3,"verified_fuzzy":26},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.07878."}