{"as_of":"2026-08-11T09:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:454df90ba7f03bb0356f85d8a6f4650bf2ef4eced48071417ed6b87d77c3194c","coverage":[{"denominator":79,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":79,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T09:01:56.727768Z","state":"measured"},{"denominator":79,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":79,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2606.22801/citation-record","integrity":"/paper/2606.22801/integrity","json":"/paper/2606.22801/citation-record.json","paper":"/paper/2606.22801"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T09:01:56.727768Z","title":"Single image haze removal using dark channel prior","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:6dd902c2dcd59f11f0ac2461d9122f0847e629d93f82fd478cbe28fa9b3cd6e5","observation_id":"43437226-156f-4cd5-9127-3908818976ea","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Imu-assisted accurate blur kernel re- estimation in non-uniform camera shake deblurring,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:7453d9c663ee2b23a7137eaea95e70c1635ae82d6e718f217d0ebf07bb99274d","observation_id":"4e22d771-051d-48cb-80c3-47629b302c91","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Learning physics-informed noise models from dark frames for low-light raw image denoising,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:ed13d76bff61d9dfa28830fe8a14fe3fbef931e9c1487a03b1ad9ba671e3e4c4","observation_id":"5b0892b7-b3d2-4117-88cb-023010d96f0d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Emphasizing crucial features for efficient image restoration,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:4f219803248038f0978a545710fc5f0b049769f5fbf3d4e46febf40b8c7746f7","observation_id":"d8efe170-5e4d-4308-b5e3-548f06676abb","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Starir: Convolutional image restoration with spatial-frequency fusion,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:23265ef84ebb3aa5e2c258602d98a5008940ed9a6d6830cb543451442117ad48","observation_id":"4c5e2858-5ed1-49be-9dc6-7d4347dc068a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Mixed hierarchy network for image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:1e35650bf592b01c8d93790dfb13337daf466ad77f91114ba742bbb4bafeae7f","observation_id":"82f08999-c445-44fe-b86e-4a58d2596434","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Dswinir: Rethinking window-based attention for image restoration,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:a954f4e99480eb35fc08c14c78d5b7201c8aeb57b51d88ee9f6cf46e6c092074","observation_id":"11e16315-1beb-445f-96a7-5165a51c7666","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Learning continuous wasser- stein barycenter space for generalized all-in-one image restoration,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:b9e3f26f9a874b3e051fd7d515e0b56b395a36bcd254dafd87375a15dcd573e4","observation_id":"51035a13-15f8-4cbb-ad34-2aa6d8752529","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"All-in-one transformer for image restoration under adverse weather degradations,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:9cc3c6270fbda49bec96c1b9eaf625736d2f8e47d1b1009d62a2b9a9d56cefe4","observation_id":"5c86842c-e386-459f-a433-5c270b695420","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Learning enriched features via selective state spaces model for efficient image deblurring,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:2684397a520cfb803ed44c25a8a4814cc3ca64e7700df70d27f672239ca264c8","observation_id":"182f772c-5b0d-46ec-8403-87c6f618b617","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Tsp-mamba: The travelling salesman problem meets mamba for image super-resolution and beyond,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:542af6fd1c3698746529935029a591620e96c84fdaf7dea1a7cc3d7d067fb0fe","observation_id":"aa29c81a-a1f5-4ddc-a7ed-82d4898dcec3","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Mambair: A simple baseline for image restoration with state-space model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:a3649787168026b05b6e203a3475ed120f429b9501855012bb7ef0d0a41ca3bd","observation_id":"6c77912d-fa8c-410e-ac1c-16e551831bc3","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Mambairv2: Attentive state space restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:28e8dacccf116f56242301884ffe736111e5ce3fd5059625e45741739b7a02bd","observation_id":"e7c2eed0-4bd5-4911-98e9-882ac419493b","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2508.12346","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T10:19:47.185648Z","title":"Mbmamba: When memory buffer meets mamba for structure-aware image deblurring,","venue":null,"work_id":"2e42e186-5339-47d7-8b38-f946232077b4","year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:5381bc0605ff31999e24b14a9e29697e0ed9e6ffa9bc205eb69e2b2c277cc177","observation_id":"6c348290-13c7-4b38-bf3c-bc470580ae01","resolution":{"observed_at":"2026-07-04T10:19:47.187016Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-06-26T09:01:56.727768Z","title":"Learn- ing optimal combination patterns for lightweight stereo image super- resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:664c66181d37003cf5600c2bea2ea6a450b3e7833a9e899fd1e1076a622b741f","observation_id":"2c7d14e3-d7fe-427e-9e66-651a8878d331","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Image restoration via frequency selection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:248f5c910d4aa90fa92b600063aa7e507dcaa977a2cc100c5a35d18606ddc3a3","observation_id":"a53686a8-367c-489a-bbff-86da737e2b4a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Prompt- based ingredient-oriented all-in-one image restoration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:50fe1b86f97ad2aff25fcc65b5724fc49781d0f5badba8a7a4e726f7987ab874","observation_id":"1dd42022-232f-45eb-8bf1-5ec9378cef08","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Frequency domain task-adaptive network for restoring images with combined degradations,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:bd2b0d649877a4f28ac41604c5a735f07dcc28016089dbfb2236f997a44b9e9c","observation_id":"af7e1421-f916-47c1-b245-2251c9625e78","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Xyscannet: An interpretable state space model for perceptual image deblurring,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:1f0ae0a0d70fe4d220889ad19638c4668ef4bd5114813915a094697f802b774c","observation_id":"41a3103e-9805-4689-b49a-d8f6ab916c5c","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Efficientderain+: Learning uncertainty-aware filtering via rainmix augmentation for high-efficiency deraining,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:47d1973abab0810d26e7c70d9bb1abc744073b7bdb98ac375c4ab9ab2fa9f402","observation_id":"51c16e5f-cacc-4f68-99f6-d8041aa8df7a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Prior-guided hierarchical harmonization net- work for efficient image dehazing,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:4cb82397462edae6b32ff22b642b98f8fcacfd886881ea9bdc9cbeac340787a7","observation_id":"9f86cac0-a19f-49f9-84c9-5d36fdfee01d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Unpaired photo-realistic image deraining with energy-informed diffusion model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:66332698433af05326443380d4328dba749783241f907f394550ad52e3c787d4","observation_id":"62cd4e64-891f-4397-872e-d75a783f7029","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Unmix- ing diffusion for self-supervised hyperspectral image denoising,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:71e195b926824af2f4e443ad47b56d5e90ad2b4e9a4b5234e2486f1037fbdcac","observation_id":"9f9af03a-ad3c-467a-b627-b79d00d3101a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Selective frequency network for image restoration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:cbcb4f57ec1c3b660c70fe1323bdf6a2b07c70b62f09bab9a9ee90509f13a2f6","observation_id":"0520f2dd-5acc-4deb-bd64-9cdc58bf2925","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Acl: Activating capability of linear attention for image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:9ea2da0e759cc4b7d5f96fa32ed9231596281aab59edd814ec75b36627ed32f4","observation_id":"f2125582-635f-4213-bc49-3f84a8e2151d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Promptir: Prompting for all-in-one blind image restoration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:6d3f675c7f6f5c0a70220b891c90fd1bca9999a21731d91eb6c1370021e69a7f","observation_id":"93e32c64-00a1-460d-9cea-2c7ce7f8c9c8","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Neural degradation representation learning for all-in-one image restoration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:aa39b2de20e0eba1b92b8d8578a6aaa1f83a4b92e32750ef565b92e4b63ef25a","observation_id":"3bf37883-ad1d-467e-b175-8e21bf999d30","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Vision-language gradient descent-driven all-in-one deep unfolding networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:e598982017b944282af38945a54d1966dd0bc4a151bd101804ff337e9b645437","observation_id":"88ea2bf3-3dda-48ae-add6-ba9fb7f690e5","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Autodir: Automatic all-in-one image restoration with latent diffusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:57d5e729fa75fd413e67a5cde194a2124e1adcd0b7c0a488f3fbebfd29136b97","observation_id":"e88f2d8d-9764-4c65-92a9-93858371f369","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Visual-instructed degradation diffusion for all-in-one image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:a25638e75ac46f49f24e5c8e5d47d7361de82bbfcb55849def58e299129417c3","observation_id":"3aa05374-7f68-42f3-b510-77cad76c7e2c","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"AdaIR: Adaptive all-in-one image restoration via frequency mining and modulation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:196182dc14ec64b47f83b605297683dc85254aa231a1ffd12ccb059b033f991a","observation_id":"1165a459-ab72-4c7c-aa63-97ebfcd52944","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Perceive-ir: Learning to perceive degradation better for all-in-one image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:642096fe8f5e436380c850550aa655cb27ca3a6157678b71dea28f62e89b04e2","observation_id":"c3e0630b-e9dc-41b2-a96f-ccacfb5cba7d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:b5a439faa24e9f2ea4ff867f28c1dae392df3bd972fe1f567496ffb595a0b37c","observation_id":"17dd2bc0-307b-4db6-9cb0-228dcd6a02c4","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Simplified state space layers for sequence modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:8be5423471bad358e8202dd39a7136969690069de5fc0c3be4948a94b1fa70a3","observation_id":"0bc5ed98-c1f6-4197-996d-5e4eaac6f9be","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:66ac5c9387045de0fb1f669d74533346bfac0225b483407db072a917b0ff5e74","observation_id":"2011a060-ed44-4c91-99a7-393280b5a769","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"M2restore: Mixture-of-experts-based mamba-cnn fusion framework for all-in-one image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:dacdd36e55f42e7949a52bb3f91dcdf41a46b66f5bd7ea2d5528afa93434e2ef","observation_id":"219f716b-1578-4500-8f64-2b9ce1d5fa51","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Aim- vr: All-in-one video restoration via dual-path mamba with frequency adaptive fusion,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:d54063d4b16e3c62210656e83282e851902cd571bf3d3c6c71d5f613cbb4c8c6","observation_id":"1980a4fd-87da-490f-b0b2-fa02e4a1564d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Liquid time- constant networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:f6e39a7aa2550c0281637a2556eb4e0d82c5741b54c2e57ee57c4f69c85a78c2","observation_id":"6f30ead7-664e-42b6-b8fa-dd44348ea9f8","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Liquid structural state-space models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:d93d2680fa48192f9a4fcccc99ab86a3e02352ad8cd8190d26b3697c3dc04e64","observation_id":"b79dc7ff-d4df-4aa0-b4a9-40cce684f133","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Simple baselines for image restoration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:1918dd5c4bcb1d05f42a4d7ccfd0fb67ce544eb6e10271dd3b5f6a6cf95a51e3","observation_id":"b800f3a9-d14c-4953-a7ce-e83d5c926b61","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Multi-stage progressive image restoration,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:df48b2f7604c1dacc377b825e207c8803f0dee4b216353e5d05277782d68e8af","observation_id":"48db6f2d-b648-421c-a411-56dffd77f039","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Deep joint rain detection and removal from a single image,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:8c45337d917c0bf81005bc6104769ef15df0185de90dfa69f5ee06a53e7384ba","observation_id":"5652ce52-80c1-47b7-a173-26d2e65b39d6","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Image de-raining using a conditional generative adversarial network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:f2bd4586381f561ae5e0a1fd04ba6330d582d9f1f019cd808878a03f0283fa1b","observation_id":"3af499dc-2009-4d14-8618-0777ec46ffe5","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Removing rain from single images via a deep detail network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:5deb8d7e380b12c45f7c59106e124d2f83ae625e4ab02759e20f954b22043760","observation_id":"3f402d91-0b98-4baf-9dbd-785cacc714dd","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Rain streak removal using layer priors,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:354822618f4b49aa951eb5eb0d547d391f74a141ec2b72a5b1ef9b5042fd7615","observation_id":"8cd14b25-8dde-4a52-b390-03efe0070e58","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Density-aware single image de-raining using a multi-stream dense network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:23becd1ae50f23150355444cbf3e931829525aab13bf0db2d9c2f6c431504a7a","observation_id":"6eb4f9f4-376e-42d2-bf8a-80ac1e990d3d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Desnownet: Context-aware deep network for snow removal,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:e5f58a7063f6fea32c9ac10093fc3dfb8be4d5b319d3700a474baa7185c8685f","observation_id":"94599119-eceb-4db8-ac69-2b42a356ba4f","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:eccbf222ff22966e4aa360c1fd0228adfe764d33f8c0e15dfbadb4c7e583c3d6","observation_id":"100b57f1-c7b1-4360-822e-649656022211","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"All snow removed: Single image desnowing algo- rithm using hierarchical dual-tree complex wavelet representation and contradict channel loss,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:63b2e0d6d49b8e0eb1571911100e6dc294c78c46bcd956beb5415a02a67fad09","observation_id":"f495c9e8-b0aa-45a2-b398-342be62f881f","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Benchmarking single-image dehazing and beyond,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:91af575154a4f8c7eb705d17e4caea82a85ba5654da8189bc112cf6083be6489","observation_id":"46d1fd7c-72d4-4a7a-8e52-dd7bc649149c","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Deep multi-scale convolutional neural network for dynamic scene deblurring,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:85206ffd59f9ed3188da15ed41c61076ca687758a3cd9df03e14fbf687dbf16c","observation_id":"ac21633c-f117-4d0a-9bed-915ea6be8534","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Ntire 2017 challenge on single image super-resolution: Dataset and study,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:48a072ca93e05ad40b5fd7210b964ca47e21ac816f886cce01899ce359d5a46d","observation_id":"5f7fbe8e-6028-4241-b9d6-deaf85581a02","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Enhanced deep residual networks for single image super-resolution,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:37e3267e78ab9474a03a0ce6a9dbc70fd3c3fb6a8e77d947136d43e38f7646dd","observation_id":"bb7abf85-4bee-47d1-9ef1-2ebbe7543b44","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Contour detection and hierarchical image segmentation,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:5380c07e57f55a54a64bf14492f241694ccae06b0975fa2d6c5796978c556807","observation_id":"b370a4b2-46e6-4ffe-b5bd-17334555709d","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Waterloo exploration database: New challenges for image quality assessment models,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:73e688114aa9c6aeedd8f9513a66faaa1bf300e8bff1dac3d0f4ccd058959c01","observation_id":"16059f81-a6c2-4130-b7ab-b1a1a393ab42","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:2c758e8fff1cc1bf75013e4f58ae2c393fdc7ce67005da95077bca816284652b","observation_id":"90c623e3-f27d-46a9-88b6-d5bb5119f94a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Single image super-resolution from transformed self-exemplars,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:92cbbd4ddf5f4f4e2eeedf03fbe1c3b5a4bf7c9dfd4148a8102b9099a9eb007d","observation_id":"49796cc5-45b9-4d21-ad43-45fe547f9f77","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Kodak lossless true color image suite,","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:e804bd990e366b34c54db845598c7217c3c1f8fc39ca540617814105cef97985","observation_id":"ab9c7446-927d-4d18-9f92-ed3504e0375e","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"The unreasonable effectiveness of deep features as a perceptual metric,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:d34f218552883b6d2f12d89d263583e46bdb5f3c874639927b79a7e8469b87ce","observation_id":"192c8c6b-8d44-4dce-9b57-c3de149d5f6f","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"An underwater color image quality evaluation metric,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:383325a2481229a57ff01e2bab77e8c17609623c2f036f9797c71dd8728e357f","observation_id":"bfbde5ca-10ce-4ceb-9c53-dc887c7ca4d1","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Human-visual-system-inspired un- derwater image quality measures,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:63c79fc76d8af20df9c35d290c4fad75e49e0be6a208751fbf640c363600f8f5","observation_id":"88437357-abca-4524-9c61-75e882b1d58a","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Referenceless prediction of per- ceptual fog density and perceptual image defogging,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:344dbc0d6718ccc04cd2908a2a8731fddc3ab586fcb0175e639ac4665ce08474","observation_id":"b67cf17d-0650-403b-aa06-eade2afa2d76","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Blind/referenceless image spatial quality evaluator,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:00b6aa59549ab135f0f4a9f70e96699d6fd46f030046be823c51ee4f7f764447","observation_id":"591e2a4e-0514-4fb4-9784-84e75350213f","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Nima: Neural image assessment,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:332c2cf80facfe2715aecd77d5b2b5530afaad0e54240c8e1c92ae6735bfe56d","observation_id":"4b870bec-191c-40f8-bf4b-1398bf66a70b","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Msp- former: Multi-scale projection transformer for single image desnowing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:ba1083ecb01bf25283274b0115037706b3bc2a3c3a72b0f8e398acaa15fbaa20","observation_id":"c2525bc9-d7e7-44fa-8c35-bdd6db9f2366","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Intra and inter parser- prompted transformers for effective image restoration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:7df1dd78062eb2a24a2e8f7d02958e935d8248cf7686a084e4523ddf1c610b77","observation_id":"9d709dbe-fa4e-4ac0-9bc7-65c599d63006","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:dd0124b2f5c37a1816f509f545fbfc146e3eb45f8c91302a3f0051102f6ac109","observation_id":"85951d1b-e619-4b66-b571-11e45ac3f41b","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Sgdr: Stochastic gradient descent with warm restarts,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:f7320b98afef8a723b0ca6abe97c58fb0e0da4e64cf3b0d26f0cf88222a80c44","observation_id":"aa1e302c-45f8-4922-be46-9ef4b6cea7c6","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Focal network for image restoration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:b9f80a3c48dfe8cfc46214281dd6aec7ffc670565dd520b1fe5706df9e138a7b","observation_id":"c00763bc-abd5-4d7a-9995-1d73cb5bf680","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Irnext: Rethinking convolutional network design for image restoration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:039e3be51a557213e0ef19c1d54745e8247b37eda431cb52348ab8722172028f","observation_id":"2c99e764-e24d-4a25-923b-806ca668a474","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Deep unfolding network for image desnowing with snow shape prior,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:b7d7e4b114f38b02310a45da24243b85b31ac8cbd5a311e879721bed7c9d109d","observation_id":"53961507-eb56-4599-98e0-1e412f68f9ef","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Global modeling matters: A fast, lightweight, and effective baseline for efficient image restoration,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:01a3478352b529c7d0815e15f644e6e0a94349f907f031cf6585f4f8e1328ea9","observation_id":"706b0dc5-5850-481a-b354-5c9868e8db96","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:1ba4bd401fa6d0ec7413391e76d9aa00bb63097c82c0786cc94c7b0e18d2dbc5","observation_id":"ffa5ae8a-a98a-4e1f-a543-c1a9b57c2857","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Omni-deblurring: Capturing omni-range context for image deblurring,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:6c7613b5f9551cf94a34efc5ffdfaf0fda77d9f9c23a19e5ebd6d73d197c3959","observation_id":"849c7628-37b7-4862-accc-070cdd2f7f18","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Human- aware motion deblurring,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:4e9f8e6384262628a21ad4677b44ef1f2a915b35eca4ef243dd362fd216bc65f","observation_id":"896e08a5-2f1d-4a7d-8533-4783c30ab3bf","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.05514","last_updated":"2022-11-19T13:11:27Z","snapshot_observed_at":"2026-08-03T09:24:22.204244Z","submitted_at":"2022-06-11T12:26:59Z","title":"Toward Real-world Single Image Deraining: A New Benchmark and Beyond","version":2},"cited_work":{"arxiv_id":"2206.05514","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.05514","snapshot_observed_at":"2026-07-04T10:19:47.188057Z","title":"Toward real-world single image deraining: A new benchmark and beyond","venue":null,"work_id":"c2124d4e-d32f-452a-b052-7a02b42366fd","year":2022},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"cited_paper":"/paper/2206.05514","citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:0c550be36fc2cbbc472b62708ce13979a4ad6562149d942501907b5d46b18704","observation_id":"26163f7a-0066-4fbb-b6d3-4698ef259558","resolution":{"observed_at":"2026-07-04T10:19:47.189660Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-06-26T09:01:56.727768Z","title":"Benchmarking single-image dehazing and beyond,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:454d36960a82f3025625bac292753a9248db38742df3a9b007ba181b23c147df","observation_id":"be9f9161-8aba-44cc-a3f7-82e3f015a377","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"A high-quality denoising dataset for smartphone cameras,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:eaca3caf463e460cf71d2b0beaa90b0a931fec902ad7cd3b72c720b3607c4b56","observation_id":"abbcbd63-d190-4c9e-a127-a5020c35b175","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","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":"2026-06-26T09:01:56.727768Z","title":"Reference-based multi-stage progressive restoration for multi-degraded images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-06-26T09:01:56.727768Z"},"links":{"citing_paper":"/paper/2606.22801"},"observation_digest":"sha256:996ee432fd7be8d18f86f5cd8b25802c92f6f637916012e6e005fc79fabedfbe","observation_id":"e08a3a86-7d44-4cf8-95c1-c7d64113f6b3","resolution":{"observed_at":"2026-06-26T09:01:56.727768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.22801","last_updated":"2026-06-22T03:20:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T15:24:28.501942Z","submitted_at":"2026-06-22T03:20:18Z","title":"Learning Adaptive Dynamical Features via Multi-$\\tau$ Liquid-Mamba for All-in-one Image Restoration"},"reference_resolution":{"displayed":79,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":77,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":79},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2606.22801."}