{"as_of":"2026-08-09T11:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d2fe6d5eed159ae30eb93ef958e87cd5bf567a70e431dafe5abbde6e6d8ef90","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T18:11:50.670236Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T01:51:42.288679Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T15:09:54.772320Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"cited_work":{"arxiv_id":"2502.05741","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.05741","snapshot_observed_at":"2026-07-04T15:09:54.772320Z","title":null,"venue":null,"work_id":"17cf313f-552d-42e0-9c89-cf335f074487","year":2025},"citing_paper":{"arxiv_id":"2606.23545","last_updated":"2026-06-22T16:18:00Z","snapshot_observed_at":"2026-08-08T19:26:38.856829Z","submitted_at":"2026-06-22T16:18:00Z","title":"UI-LIC: A Unified Framework for Evaluating Learned Image Compression Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T01:51:42.288679Z"},"links":{"cited_paper":"/paper/2502.05741","citing_paper":"/paper/2606.23545"},"observation_digest":"sha256:451e85d6fb5c6bfa7dd56936610575297d081982970369227cd2c8f15a8e73fd","observation_id":"dd17dc5a-e076-4de9-ac86-eeaf0cfb0fcd","resolution":{"observed_at":"2026-07-04T15:09:54.774238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.05741/citation-record","integrity":"/paper/2502.05741/integrity","json":"/paper/2502.05741/citation-record.json","paper":"/paper/2502.05741"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.222800Z","title":"Chou, David Minnen, Saurabh Singh, Nick Johnston, Eirikur Agustsson, Sung Jin Hwang, and George Toderici","venue":null,"work_id":"8536cc65-46d2-431a-96a4-af85ee7d3692","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.474489Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:b05c6d84c8806d2fd9cf062004248958a4713fa824a6f16ef0aa29bf1fc3012d","observation_id":"1c658041-edf6-43d2-94ab-ca7e018d9085","resolution":{"observed_at":"2026-08-08T18:11:51.226468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.211749Z","title":"Simoncelli","venue":null,"work_id":"ebfd6eb4-5da6-4e9c-9347-8fd18ea35575","year":2017},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.478251Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:2540be27ad212c1f3c8eeea87e53886b3b55e7bfefa89621f07759542cb076a1","observation_id":"bb1440a2-dd76-4323-bcf1-484f7e0e95d1","resolution":{"observed_at":"2026-08-08T18:11:51.215640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.200416Z","title":"Variational image compression with a scale hyperprior, 2018","venue":null,"work_id":"f9500008-6bcd-49eb-99db-2c4a3ab36108","year":2018},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.481888Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:32421d7a0b17a2b2dcfd486aca0dcb71c4d51559159d6adb504a789006f5475d","observation_id":"46fc9bf9-d91e-432d-8058-b8f968eace0a","resolution":{"observed_at":"2026-08-08T18:11:51.204532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.189964Z","title":"Sullivan, and Jens-Rainer Ohm","venue":null,"work_id":"7a70864d-5f22-48cc-ab23-d227e784c204","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.486031Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:d546decb5150a7a93da5e05996e2590847c3e58ef6e74dc4c0ec5de9840f7f22","observation_id":"dea836fe-702d-4d3f-bf8d-88cbb4a3e4c7","resolution":{"observed_at":"2026-08-08T18:11:51.194096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.179628Z","title":"CompressAI: A PyTorch library and evaluation platform for end-to-end compression research, 2020","venue":null,"work_id":"17119db4-3b83-4d52-9d59-6ad3370d0c9c","year":2020},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.489675Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:994faf98c296f758d64f66e622f8b3eeae8c17bf2d67870e9b43978366144aa2","observation_id":"6500078b-77d9-4fcb-a0d1-a60d384b3c97","resolution":{"observed_at":"2026-08-08T18:11:51.183205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09626","last_updated":"2024-03-14T17:57:07Z","snapshot_observed_at":"2026-07-06T17:44:45.924645Z","submitted_at":"2024-03-14T17:57:07Z","title":"Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09626","snapshot_observed_at":"2026-08-08T18:11:50.493486Z","title":"Video mamba suite: State space model as a versatile alternative for video understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.493486Z"},"links":{"cited_paper":"/paper/2403.09626","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:bf32a45bb3fc0afab0a42d18431bb28cbd248db3920b5efb9834c496ffec3e63","observation_id":"175ec90a-0661-4cdb-895e-522ddfc9e56c","resolution":{"observed_at":"2026-08-08T18:11:50.493486Z","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":"2026-08-08T18:11:51.170123Z","title":"Learned Image Compression With Discretized Gaus- sian Mixture Likelihoods and Attention Modules","venue":null,"work_id":"ed26ae24-dd6e-43cc-b29b-6efa7cf42ccc","year":2020},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.497618Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:a3b37bd2e3dcd7b5d8aa2b2e441d8b8220e62bed1f6f794975a75a8b1a3d7438","observation_id":"fc3c425a-82eb-4521-b766-53ec7b7293d6","resolution":{"observed_at":"2026-08-08T18:11:51.173447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.159529Z","title":"The evolution of rwkv: Advancements in effi- cient language modeling, 2024","venue":null,"work_id":"c9de38f1-b410-41f7-bf4b-930c18720e38","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.501517Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:da5c2f9e3f70010cb0e60e6999264976b23a079a835ba414e82a7b79666dc208","observation_id":"a8eea176-bc8d-4d2e-993e-a89927dad6d9","resolution":{"observed_at":"2026-08-08T18:11:51.163305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.148657Z","title":"Vision-RWKV: Efficient and Scalable Visual Per- ception with RWKV-Like Architectures, 2024","venue":null,"work_id":"5b370ee5-c76e-4638-b6b0-9967bb2e6a64","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.504699Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:c26d92b57c9a2f0dbfb964e5005bbc97fbb6d5286ce42c5904258cf8628fc311","observation_id":"d8a27fba-c8a3-4ba3-9fb7-9244eaaf990a","resolution":{"observed_at":"2026-08-08T18:11:51.153077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02308","last_updated":"2025-03-31T06:14:48Z","snapshot_observed_at":"2026-08-09T01:57:03.186839Z","submitted_at":"2024-03-04T18:46:20Z","title":"Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02308","snapshot_observed_at":"2026-08-08T18:11:50.508793Z","title":"Vision-rwkv: Efficient and scalable visual percep- tion with rwkv-like architectures","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.508793Z"},"links":{"cited_paper":"/paper/2403.02308","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:bebc6de6b10467f33ed6b6bf36ee5695486fc3b0a1d557c18789f9a48eab5da4","observation_id":"5b7b609d-728e-4990-9f52-35e320e5ce20","resolution":{"observed_at":"2026-08-08T18:11:50.508793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04478","last_updated":"2024-04-06T02:54:35Z","snapshot_observed_at":"2026-08-07T08:36:52.565708Z","submitted_at":"2024-04-06T02:54:35Z","title":"Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04478","snapshot_observed_at":"2026-08-08T18:11:50.512474Z","title":"Diffusion-rwkv: Scaling rwkv-like archi- tectures for diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.512474Z"},"links":{"cited_paper":"/paper/2404.04478","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:bdaad26b52c39771f65373ec4456646bbfc78e217ce5252c6d0640edc8a5e359","observation_id":"428b882f-b093-49c3-a0af-0c0e8e5c85ea","resolution":{"observed_at":"2026-08-08T18:11:50.512474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-08T18:11:50.517156Z","title":"Mamba: Linear-time sequence mod- eling with selective state spaces","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.517156Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:d83b2fd9731dfdb08a253a7b64a28dc2f3d6123ee97ae9b40af7e9c9682fc450","observation_id":"8e88365c-e242-46a0-8fe5-d5a462d06ee1","resolution":{"observed_at":"2026-08-08T18:11:50.517156Z","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":"2026-08-08T18:11:51.133084Z","title":"Mambair: A simple baseline for im- age restoration with state-space model","venue":null,"work_id":"47ce87dd-3e56-4436-884e-6c79421b9d56","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.522073Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:9c7a7547af3332a156b8162655afb7c1a2447cefbbf376e8b466e9dd27e733eb","observation_id":"cbfbc256-043a-437e-ab6c-ae16562492bd","resolution":{"observed_at":"2026-08-08T18:11:51.136889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.121760Z","title":"ELIC: Efficient Learned Image Com- pression with Unevenly Grouped Space-Channel Contextual Adaptive Coding","venue":null,"work_id":"4f0c24eb-21a8-4f3c-b644-36e5c2e56924","year":2022},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.525392Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:a576342d8bd67abd8f7100980be702ebf19c51bedb9c7399d4c7f475227401f8","observation_id":"2e489843-68e2-4f1f-9428-66079bc76207","resolution":{"observed_at":"2026-08-08T18:11:51.125898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05969","last_updated":"2024-07-08T14:07:26Z","snapshot_observed_at":"2026-07-06T18:43:00.310399Z","submitted_at":"2024-07-08T14:07:26Z","title":"Deform-Mamba Network for MRI Super-Resolution","version":1},"cited_work":{"arxiv_id":"2407.05969","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.05969","snapshot_observed_at":"2026-08-08T18:11:50.767621Z","title":"Deform-Mamba Network for MRI Super-Resolution","venue":"cs.CV","work_id":"36a341b4-88d2-4bbc-a0a1-93cd5d450f99","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.528669Z"},"links":{"cited_paper":"/paper/2407.05969","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:8381f0d85896a4cd503323daaacb39cde8ad680986f9f3e7fd523421a0216f39","observation_id":"7149897a-3dcf-4ad7-a3d3-9498aa3dae81","resolution":{"observed_at":"2026-08-08T18:11:50.773239Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.109279Z","title":"MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression","venue":null,"work_id":"cf71df4a-831d-4e84-8a7e-b8dee1a71d99","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.532534Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:523ea98c0f3e25280117111df8e83ab55264f012dd5ddcebcdff25f8f3b908f0","observation_id":"7ee9ead4-5332-4ccf-9b2e-c5e15758c5b6","resolution":{"observed_at":"2026-08-08T18:11:51.113798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.098268Z","title":"Burakhan Koyuncu, Han Gao, Atanas Boev, Georgii Gaikov, Elena Alshina, and Eckehard Steinbach","venue":null,"work_id":"320556c2-630c-4136-a878-d39a03932720","year":2022},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.536900Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:1c3e001f8507101879bf28361d138439a243a440870e95e867bde5acfd68737d","observation_id":"98ec6be7-af36-4614-a886-69f12a4adabe","resolution":{"observed_at":"2026-08-08T18:11:51.102376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.086463Z","title":"Frequency-Aware Transformer for Learned Image Compression","venue":null,"work_id":"cd2dff60-b3e1-4574-b2d0-cead3f56c121","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.540501Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:06be22adfd19ab4c53985bb040aa075d5e8802a25db5e22f5acece19f2b73202","observation_id":"fe97cf47-1837-4105-85db-f28bb0b16737","resolution":{"observed_at":"2026-08-08T18:11:51.091178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.076213Z","title":"Learned Image Compression with Mixed Transformer-CNN Architectures","venue":null,"work_id":"f57a58df-b7bf-48f1-83c6-38f8d943be37","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.544306Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:62f8d146e12fdd23ae5f0a56b396315c8a9f849e8ef72c2546b7532ef53238ff","observation_id":"d438a2a8-4621-4b55-9ad9-fa963157383f","resolution":{"observed_at":"2026-08-08T18:11:51.079818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"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":"2026-08-08T18:11:50.548239Z","title":"Vmamba: Visual state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.548239Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:5490eb679c8ed12fe428be700a4860acfc8c1c5aee40baa22b237490b81df4f1","observation_id":"8e904f44-19ac-46e4-b78a-961629836f0b","resolution":{"observed_at":"2026-08-08T18:11:50.548239Z","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":"2026-08-08T18:11:51.066317Z","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","venue":null,"work_id":"a6bda461-b99b-4ba1-9403-c49ba742d81e","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.552869Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:e87ab3c7c09d8362ba2f1d20ba3fc2dd27974464a77f446e03f880228daa5d5f","observation_id":"d63460f5-9297-4e2f-89fb-12cb1e80326e","resolution":{"observed_at":"2026-08-08T18:11:51.069965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.055333Z","title":"Transformer-based Image Compression","venue":null,"work_id":"38370675-c334-4ade-a976-b27975d043f3","year":2022},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.556453Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:1b30ec380b44a883d5b8ed8fc3d91c019ab7b2f6cc8a9a31810e0efbf97afd1c","observation_id":"e6faa7a1-786f-4257-b889-f0bc441ec625","resolution":{"observed_at":"2026-08-08T18:11:51.058880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.043525Z","title":"Understanding the effective receptive field in deep convolu- tional neural networks","venue":null,"work_id":"b510e0cb-4dff-48af-869e-e1ff1b554117","year":2016},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.560873Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:cb4acba3b467567269197f2a15a7b29fd470c0dd75c8ac29c6d05ada24fb6947","observation_id":"34487b2c-af01-4046-929d-8be18b0a12a9","resolution":{"observed_at":"2026-08-08T18:11:51.047578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04722","last_updated":"2024-01-09T18:53:20Z","snapshot_observed_at":"2026-07-06T17:13:25.341853Z","submitted_at":"2024-01-09T18:53:20Z","title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04722","snapshot_observed_at":"2026-08-08T18:11:50.564487Z","title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.564487Z"},"links":{"cited_paper":"/paper/2401.04722","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:db8f96c543a6fda849110e28a6464693b13853870adb8ae0051b9a8a0dff3bb9","observation_id":"31768300-3bd8-426f-bd0d-162de4ddc916","resolution":{"observed_at":"2026-08-08T18:11:50.564487Z","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":"2026-08-08T18:11:51.030954Z","title":"Channel-Wise Autore- gressive Entropy Models for Learned Image Compression","venue":null,"work_id":"61118d35-c41b-4e26-bffe-e6c25daa32b9","year":2020},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.568263Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:0ed2c6d5e2115a9a182a9202afcfb3d3f073f2a088646585fd6fa333f24ca06a","observation_id":"49babda6-8b80-47f1-99d9-4cdbfa2aad03","resolution":{"observed_at":"2026-08-08T18:11:51.034933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.019780Z","title":"Joint Autoregressive and Hierarchical Priors for Learned Image Compression","venue":null,"work_id":"61217965-4bdd-4af2-8fc3-572dae61da79","year":2018},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.571503Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:f832bc823762662ef343ddfb96d738a569d9e6e0f13d9bf1f04fa366dbb1bc57","observation_id":"0ca86a82-9bc6-47aa-95a3-ce4e55c49e64","resolution":{"observed_at":"2026-08-08T18:11:51.023387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:51.008152Z","title":"Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, and Rui-Jie Zhu","venue":null,"work_id":"2db921c5-2971-458a-baf5-7e04ce406baa","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.575024Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:5a60e1da0124956115ccb26f4ab07f89c5d6ee2b95050bee3f4ff165d9d74733","observation_id":"1f2c98e3-6e3c-4f63-805a-6a85d8070e66","resolution":{"observed_at":"2026-08-08T18:11:51.012722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.997561Z","title":"Rwkv: Rein- venting rnns for the transformer era, 2023","venue":null,"work_id":"bde5a6ed-27a1-4540-a654-900e19dda5af","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.578610Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:cf4b9b9b50dce08a27ca9acb396a38708b3bec3b06eb4fb4dc38660783d2cbc3","observation_id":"15110047-8be4-4490-8217-20b43078b6f3","resolution":{"observed_at":"2026-08-08T18:11:51.001227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05892","last_updated":"2024-09-26T22:39:08Z","snapshot_observed_at":"2026-08-06T19:53:21.536135Z","submitted_at":"2024-04-08T22:20:59Z","title":"Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05892","snapshot_observed_at":"2026-08-08T18:11:50.583010Z","title":"Eagle and finch: Rwkv with matrix-valued states and dynamic recur- rence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.583010Z"},"links":{"cited_paper":"/paper/2404.05892","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:6d506201207c93fe416b14fe47a8ad8bbe2e36b92172de52ee769b1e7e169a45","observation_id":"97dd0aa9-1f9b-4abd-b2c5-c900a6333ea1","resolution":{"observed_at":"2026-08-08T18:11:50.583010Z","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":"2026-08-08T18:11:50.986041Z","title":"Entroformer: A Transformer-based Entropy Model for Learned Image Compression, 2022","venue":null,"work_id":"39fe9c42-6738-48c7-93d5-c13dda5ae912","year":2022},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.587387Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:57dbe6d02d317ebfad998eb6fc0849b3f8bf599a5a70aae50e6248b752490de9","observation_id":"e61493fa-967b-4314-99a5-68d1ddcb11f4","resolution":{"observed_at":"2026-08-08T18:11:50.990006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13600","last_updated":"2024-03-20T13:48:50Z","snapshot_observed_at":"2026-07-06T17:47:42.867147Z","submitted_at":"2024-03-20T13:48:50Z","title":"VL-Mamba: Exploring State Space Models for Multimodal Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13600","snapshot_observed_at":"2026-08-08T18:11:50.591329Z","title":"Vl-mamba: Ex- ploring state space models for multimodal learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.591329Z"},"links":{"cited_paper":"/paper/2403.13600","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:367368fb207e02f2213b5fe6b877c95289986bb18b04000681a5e0825f988183","observation_id":"7b31ea76-5385-4554-a2c1-15f1673acce0","resolution":{"observed_at":"2026-08-08T18:11:50.591329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15413","last_updated":"2024-05-28T13:58:14Z","snapshot_observed_at":"2026-07-06T18:19:17.573250Z","submitted_at":"2024-05-24T10:24:30Z","title":"MambaVC: Learned Visual Compression with Selective State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15413","snapshot_observed_at":"2026-08-08T18:11:50.595690Z","title":"Mambavc: Learned visual compression with selective state spaces","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.595690Z"},"links":{"cited_paper":"/paper/2405.15413","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:1a33fcb12816a4c9e56ee73d3b63a379c0f8f1fa9b222609ff28ac5c6bff1d2f","observation_id":"3127a8c6-e35b-4359-b451-5e2b18e85d29","resolution":{"observed_at":"2026-08-08T18:11:50.595690Z","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":"2026-08-08T18:11:50.975313Z","title":"Temporal Context Mining for Learned Video Compression","venue":null,"work_id":"20fadcd1-0ea0-46eb-9170-2c6bba610535","year":2023},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.599499Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:2cd3277f17548e15533338378507184f5e004f81802e8aaba7e84fe4924d8db3","observation_id":"a47ed1ca-3e2c-4c4a-a105-d05c6624978e","resolution":{"observed_at":"2026-08-08T18:11:50.978688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.964542Z","title":"NVC-1B: A Large Neural Video Coding Model, 2024","venue":null,"work_id":"b8b76441-034d-4cc8-b3ce-a1882ff0be15","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.602896Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:12d80cab3d7233358403c6e831ff65e487c23b068cf1e10b698d8f40083841d8","observation_id":"14c5a9d0-bafd-45a1-8ef3-c6f0e1f481fd","resolution":{"observed_at":"2026-08-08T18:11:50.968871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.954096Z","title":"The JPEG 2000 still image compression standard","venue":null,"work_id":"38821f14-2cf0-49f2-9080-4bd73ec692f0","year":2000},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.607482Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:233072985ff3db4c6248c9f8e1993ba385e0b0846318f75d51b8662f2e031dca","observation_id":"feed567a-3838-46c6-8c24-be10030eb8c2","resolution":{"observed_at":"2026-08-08T18:11:50.957571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.942999Z","title":"Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand","venue":null,"work_id":"c6860f7d-b53b-4737-bc97-f14267257319","year":2012},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.611413Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:e5cc264ddca155aaee4c46f1852702719db5d1e4d3e89bfe4d6435c207f7ed4e","observation_id":"af957825-f66f-468e-bd64-c832146d9c33","resolution":{"observed_at":"2026-08-08T18:11:50.946427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.933155Z","title":null,"venue":null,"work_id":"046f2e72-b108-475d-960a-a3c78297d312","year":1992},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.615564Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:c5f250fd59f25d2b536c6df2bbf1f1391655e077513b917622e9d45e66c86a03","observation_id":"1d712786-8b21-4394-91c9-8df8f4dadf44","resolution":{"observed_at":"2026-08-08T18:11:50.936404Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.922803Z","title":"Enhanced Invertible Encoding for Learned Image Compression","venue":null,"work_id":"e3e8979a-3905-4e46-ac27-7ea029efc2d0","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.620044Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:38c69940d5511d4fc2d7912a2c3875878c89f03a1f625d812a4cead339fa12b5","observation_id":"f5e26cae-720b-4a44-a248-6deb6abbecd8","resolution":{"observed_at":"2026-08-08T18:11:50.926415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.912525Z","title":"Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV, 2024","venue":null,"work_id":"a3a34696-d466-45ed-ab5c-075004a3f6c7","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.623337Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:2a241f20d7bcdbc90bd50e44b173e9700ee2a3afb6a661c9b18faa3adffb93e1","observation_id":"e81bd170-a38a-4efc-b00d-cae750b8374e","resolution":{"observed_at":"2026-08-08T18:11:50.916188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11087","last_updated":"2025-01-06T15:27:33Z","snapshot_observed_at":"2026-07-06T18:46:43.829115Z","submitted_at":"2024-07-14T12:22:05Z","title":"Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.11087","snapshot_observed_at":"2026-08-08T18:11:50.626907Z","title":"Restore-rwkv: Efficient and effective medical image restoration with rwkv","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.626907Z"},"links":{"cited_paper":"/paper/2407.11087","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:f89671332d884c77b6df85ef9adde2b821480aedac011fe57edb669901acf46e","observation_id":"726ef8e2-c8fc-4b8a-ad18-a894dd360153","resolution":{"observed_at":"2026-08-08T18:11:50.626907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19369","last_updated":"2024-06-27T17:49:25Z","snapshot_observed_at":"2026-07-06T18:38:05.229292Z","submitted_at":"2024-06-27T17:49:25Z","title":"Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19369","snapshot_observed_at":"2026-08-08T18:11:50.631154Z","title":"Mamba or rwkv: Exploring high-quality and high-efficiency segment anything model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.631154Z"},"links":{"cited_paper":"/paper/2406.19369","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:c8f66a3cd79b69cd9163d91c697e28079a602dc7868cce2a1aeb77646a9fbcff","observation_id":"a9b48553-2acd-45cc-95d4-31a7cdd46f17","resolution":{"observed_at":"2026-08-08T18:11:50.631154Z","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":"2026-08-08T18:11:50.902800Z","title":"An Attention Free Transformer, 2021","venue":null,"work_id":"bb4a5447-eda9-4387-8186-25180cb66650","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.635185Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:9cd4af242436636943f08387a59578a4b249c9f3729b67e2d91c3c740276e916","observation_id":"d17b1fe1-54e0-4fb2-a18e-ba131cb01a24","resolution":{"observed_at":"2026-08-08T18:11:50.905914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.892609Z","title":"End-to-end Optimized Image Compression with Attention Mechanism","venue":null,"work_id":"b8a9a61c-e628-4db4-8688-35b23ea64c98","year":2019},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.639099Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:052982b9a6387784b21c2b925b091a1a08ff7b5d0a326e989e57633649b888c6","observation_id":"070a6c87-5116-456d-84d9-3425711e2d82","resolution":{"observed_at":"2026-08-08T18:11:50.896313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.882159Z","title":"Bsbp-rwkv: Background suppression with boundary preservation for efficient medical image segmentation","venue":null,"work_id":"f1b456d3-aaf7-4cc1-b7e2-5c74877243ed","year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.643307Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:684b00b2cc5a43cb504d8427472fb950fa504ea8867efe5f9ee319be01337dc0","observation_id":"40fb517f-bcb3-45c0-8eca-ff58c21d450f","resolution":{"observed_at":"2026-08-08T18:11:50.886372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"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":"2026-08-08T18:11:50.647419Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.647419Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:e3551648adce1c4ad6064fd5dd5f641f04ee7f9559f3a7005947a12dbc84877f","observation_id":"4de85aa8-c388-4786-9438-cf6261cfd0b1","resolution":{"observed_at":"2026-08-08T18:11:50.647419Z","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":"2026-08-08T18:11:50.872130Z","title":"Transformer- based Transform Coding","venue":null,"work_id":"8b990021-a0b3-4c99-9206-8008285976a5","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.652116Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:85233421533225261e7388c15916e2ee39f069e64133f622e3bb5564da65288f","observation_id":"d297ebff-a403-4e43-82cd-bae10dab920a","resolution":{"observed_at":"2026-08-08T18:11:50.875805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.859194Z","title":"The Devil Is in the Details: Window-based Attention for Image Compression","venue":null,"work_id":"fe9b4fa8-b4a6-4119-a895-ac21d8551b34","year":2022},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.655632Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:31739230a42b2d7da230676e14c7e07addd1834620688cf05384af90813b7b94","observation_id":"e4ac7dd9-8a7a-4518-9cb9-ca88a0fff6fc","resolution":{"observed_at":"2026-08-08T18:11:50.863329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.847726Z","title":"The Open Images Dataset V4: Unified image classifica- tion, object detection, and visual relationship detection at scale","venue":null,"work_id":"f32de3a9-1f79-4387-8feb-680650de0490","year":1956},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.659839Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:8c30fe41b8ceda4a1c5fb66d9cd5fdb9e181d2915e69d51b44fa819de3834eed","observation_id":"72824df7-e539-4af5-a46f-9a38868ed699","resolution":{"observed_at":"2026-08-08T18:11:50.852092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.836492Z","title":"Kodak lossless true color image suite (pho- tocd pcd0992)","venue":null,"work_id":"2260289f-f429-4983-8f1b-4038d3e12bda","year":1993},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.663381Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:d12cb2bce31ccde06949f0d120938d6cff784e846cc542bf350c478a0acaceb2","observation_id":"5cbb1615-ce46-479c-a737-188bc299f2d9","resolution":{"observed_at":"2026-08-08T18:11:50.840473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.825717Z","title":"Testimages: A large- scale archive for testing visual devices and basic image pro- cessing algorithms","venue":null,"work_id":"2677d54c-af11-4f5f-b8a6-7ce1acb53ae8","year":2014},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.666798Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:99e0947112930425babcb4f676a4fe0eb4f156231812922bac3be64b1127f901","observation_id":"9725d254-ba30-45fc-b55c-318eeefed322","resolution":{"observed_at":"2026-08-08T18:11:50.829237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:11:50.815829Z","title":"Workshop and challenge on learned image compres- sion","venue":null,"work_id":"3baa0ae9-8374-478f-8e04-7e37a9270639","year":2021},"citing_paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T18:11:50.670236Z"},"links":{"citing_paper":"/paper/2502.05741"},"observation_digest":"sha256:8eba212ac4005a8ed734106846cd0bf2b4ad586bf9a0cbd7d90943a814cc586b","observation_id":"816b42b0-5211-411d-b022-3b2005d581e8","resolution":{"observed_at":"2026-08-08T18:11:50.819313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.05741","last_updated":"2025-03-22T17:16:31Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T18:05:33.390636Z","submitted_at":"2025-02-09T01:57:17Z","title":"Linear Attention Modeling for Learned Image Compression"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":37},"total_outbound_references":51},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.05741."}