{"as_of":"2026-08-21T01:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8053e079d3e98dd0c7ceacc21c6b5bcac697bf4f13570ab76a44a5463be807ab","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:28:52.525376Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.22471/citation-record","integrity":"/paper/2506.22471/integrity","json":"/paper/2506.22471/citation-record.json","paper":"/paper/2506.22471"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:51.967986Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:51.967986Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:1524a7a3daf36aedcc74d3351d2b9851d2946b16273d572702d4a0aa80ab86f9","observation_id":"681327bb-e7ee-4152-9228-6b15a467104f","resolution":{"observed_at":"2026-08-15T19:28:51.967986Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11229","last_updated":"2025-01-20T02:29:34Z","snapshot_observed_at":"2026-08-19T17:27:39.393947Z","submitted_at":"2025-01-20T02:29:34Z","title":"Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11229","snapshot_observed_at":"2026-08-15T19:28:52.004532Z","title":"A., Rajabalifardi, K., and Cioffi, J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.004532Z"},"links":{"cited_paper":"/paper/2501.11229","citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:30854d7019eb8f191b891e107548367b18e2f05dc95834e2d2c2fe684d7c0002","observation_id":"a98c1e18-533d-4bc1-9757-cee33a545651","resolution":{"observed_at":"2026-08-15T19:28:52.004532Z","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":"2026-08-15T19:28:53.825521Z","title":"and Calmet, J","venue":null,"work_id":"b368b2cb-c595-4c55-b331-e086b107572e","year":2005},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.063661Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:66f487169d8feac15444005eba1cec92d464becab54d7479aef60b881918cc79","observation_id":"bc6b2f9a-81d2-464a-a975-8fe87e4879a0","resolution":{"observed_at":"2026-08-15T19:28:53.909989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.810595Z","title":"and Salem, F","venue":null,"work_id":"8a18d3f8-6ee4-4abb-a103-75de0612f4fd","year":2017},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.069311Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:aaac6fb26fb8207b107542d08213bfb3f6fe83d11ec4de345ed512486be70d38","observation_id":"2d791af2-bcdf-4ad6-b1f1-47d32d5a3444","resolution":{"observed_at":"2026-08-15T19:28:53.816140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.073666Z","title":"Revisiting fundamentals of experience replay","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.073666Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:2eb777ddd7d7d672febb65093ab2141c1ef12a70889ab77be3e338c7138f812c","observation_id":"564a13e0-8694-4f03-84c0-fd2d1101cd9a","resolution":{"observed_at":"2026-08-15T19:28:52.073666Z","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":"2026-08-15T19:28:53.785863Z","title":"An equivalence between loss functions and non-uniform sampling in experience replay","venue":null,"work_id":"b23f81e1-c137-42b7-8218-322d1bf003f4","year":2020},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.078206Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:09124c2eff6a1b65a6d1e8be2d8bb2ccf36c8f8102f70e7d42376e4e9b34bdca","observation_id":"829ad42e-12bd-4e49-9c41-3fa4223b6187","resolution":{"observed_at":"2026-08-15T19:28:53.790443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.771327Z","title":"and Costa-Perez, X","venue":null,"work_id":"acea8cb2-d1c5-4093-a474-b09f7b238ab7","year":2021},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.083285Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:a64a3a3237fefdbfa7b89c3e17f9b1640f5ccba74f283d58a8a9d5aa43aec7d1","observation_id":"aee4b3cb-a320-4656-97c0-5478dfaa3fba","resolution":{"observed_at":"2026-08-15T19:28:53.776373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.605187Z","title":"K., Koutn \\' k, J., Steunebrink, B","venue":null,"work_id":"d92403fc-5d26-46ea-b7ee-d0df7eccb03a","year":2016},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.088113Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:beb5c198601d9a9ba0d9f28b275d8e365ee4dd07f0d196b92f109b6f2ee7db84","observation_id":"f396882f-f216-42ef-bf95-fa75aa64c298","resolution":{"observed_at":"2026-08-15T19:28:53.678526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.092337Z","title":"Transformer in transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.092337Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:f21c1e35669bd9698aae83ff7a632f929ccdde2ff27c413b723d2e12dfe3877d","observation_id":"d854c493-0a1d-4b07-9922-e92a1b7112ee","resolution":{"observed_at":"2026-08-15T19:28:52.092337Z","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":"2026-08-15T19:28:53.479682Z","title":"Quadriga: A 3-d multi-cell channel model with time evolution for enabling virtual field trials","venue":null,"work_id":"794502d6-0006-4404-ad71-86c8efef7dbf","year":2014},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.096544Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:38dd01e52b3927622dfa69688593004872deffb937dce9410263f950e2df756f","observation_id":"9b3ad20e-abfb-401e-bde6-9597acd65d27","resolution":{"observed_at":"2026-08-15T19:28:53.539197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.412406Z","title":null,"venue":null,"work_id":"cc41741b-da86-4036-a660-3864f8ddb07e","year":2022},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.100287Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:ed2a9c58993629e3a7bfc29a16ee46cb21546d30ef0c229f81efdb37bd1c9097","observation_id":"7626a13e-bd79-4184-8405-40b83f9d513b","resolution":{"observed_at":"2026-08-15T19:28:53.417221Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.396933Z","title":"and Schotten, H","venue":null,"work_id":"e0ef507f-7755-4e09-86f9-8c5542f2900b","year":2019},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.105369Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:2cd1c3e6f9ea2c53e3832139c960b2ce77c1aaef0991bb9d13c550868a73f7c4","observation_id":"7109d8b7-5811-4a30-965c-ccfe28dd7997","resolution":{"observed_at":"2026-08-15T19:28:53.402033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.381940Z","title":"and Schotten, H","venue":null,"work_id":"9ad068ad-84a8-4f1d-8f16-782fa6b73e77","year":2020},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.109150Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:64054251da6de290067b392f9837ce90111a71fe00c6801826bad565fae628be","observation_id":"8ffd4b70-733a-47de-bc74-6d472553815c","resolution":{"observed_at":"2026-08-15T19:28:53.387384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.367703Z","title":"C., Han, D","venue":null,"work_id":"3edf6eb2-df47-4a09-b85e-ecaeb03683ce","year":2019},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.113327Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:6dbdc4c693447b26dd6d419dfb8377df0e56b7bb21bea16c9323ea15369a3f81","observation_id":"7b54d8a1-6d50-4038-b8d9-2e196eabacfc","resolution":{"observed_at":"2026-08-15T19:28:53.372376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.351340Z","title":"Measuring catastrophic forgetting in neural networks","venue":null,"work_id":"0761c8a3-6d2b-4bad-8121-a60b0483e287","year":2018},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.117512Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:be64d499bde4fe1e5ae442d324f66e9b7b1d624fe9e0796f32e65ad2eba3ee5f","observation_id":"5aec15d3-c855-4cee-acb1-76b69c84209f","resolution":{"observed_at":"2026-08-15T19:28:53.356780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.195751Z","title":"D., Jeong, J., and Kim, G","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.195751Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:efed7cad0ef318c50c8f5ae57367f5d25e0c5dfff196e113d03bb1f959db5cb8","observation_id":"3dddde91-ee5a-46e5-860f-2c558606a651","resolution":{"observed_at":"2026-08-15T19:28:52.195751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18196","last_updated":"2025-02-25T13:37:20Z","snapshot_observed_at":"2026-08-16T12:55:21.853989Z","submitted_at":"2025-02-25T13:37:20Z","title":"Machine Learning for Future Wireless Communications: Channel Prediction Perspectives","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18196","snapshot_observed_at":"2026-08-15T19:28:52.244000Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.244000Z"},"links":{"cited_paper":"/paper/2502.18196","citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:3cacbf902df77d33d517cfb59e3844c9dabb1a37d15dde7c0e4ec029d087eb7a","observation_id":"c03d2dea-7133-40a4-a0ed-a1e153328e5d","resolution":{"observed_at":"2026-08-15T19:28:52.244000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.248824Z","title":"A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.248824Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:55773974e23a0a6a9705cc4bd0d41af13b59d549a2b5c10bc12bc6a675ce1c2f","observation_id":"e4518689-1d87-4142-8801-026615f60f01","resolution":{"observed_at":"2026-08-15T19:28:52.248824Z","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":"2026-08-15T19:28:53.317821Z","title":"Batch sampling for experience replay","venue":null,"work_id":"b685c0a0-b2f4-455c-88e4-18d59a8234c3","year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.252809Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:cbf16e572245b3eb42e0cac8f668d14772e42c9246acd553ad71d4d2f03fdb71","observation_id":"159cbbc2-a486-48f6-801e-19a58fbf033c","resolution":{"observed_at":"2026-08-15T19:28:53.322601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.256635Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.256635Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:7c42bfa63718b95d0d6ac9d5a21faa7d2de3548474256cf36c59f6ad8210e374","observation_id":"e9760d2f-34f3-45eb-9966-0ea1b0bd3a5e","resolution":{"observed_at":"2026-08-15T19:28:52.256635Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.260678Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.260678Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:7c15bad5cfb0e96c78a52fb0e93b00ea73b14cbc50a1ac20981961dfb65f13fd","observation_id":"1f0a7a63-6128-432e-8135-49c1df1d6cfe","resolution":{"observed_at":"2026-08-15T19:28:52.260678Z","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":"2026-08-15T19:28:53.294274Z","title":"Impact of channel aging on massive mimo vehicular networks in non-isotropic scattering scenarios","venue":null,"work_id":"d7851362-38ad-404d-a43a-45d6b88f5739","year":2021},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.264533Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:d5eaf5b4ebdae0fc6b423dfe104acf6d12577fdb4e5058aee7c9223880e7d700","observation_id":"6fee09e9-2107-4016-ac2f-b83fce35f500","resolution":{"observed_at":"2026-08-15T19:28:53.298612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.269584Z","title":"and Hoiem, D","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.269584Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:210aa9beeabf1ef3ab2611718d2bad5f9a130189ba02d10f937fe112e0339430","observation_id":"bcc1f7f1-5530-476c-82f4-de58851356a5","resolution":{"observed_at":"2026-08-15T19:28:52.269584Z","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":"2026-08-15T19:28:53.218661Z","title":"Llm4cp: Adapting large language models for channel prediction","venue":null,"work_id":"27d9535d-6fb3-4ce1-be6e-01f8d4c8fc1d","year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.273947Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:de6ba4139884918467967bd09f44738ef8253498217cd301c3b5b16c95c82ef7","observation_id":"8c54c77c-8180-47d2-b1bd-1e24db454a63","resolution":{"observed_at":"2026-08-15T19:28:53.246418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29354","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.686625Z","title":"Deep learning-based channel prediction for edge computing networks toward intelligent connected vehicles","venue":null,"work_id":"cd7b0e5b-8051-471f-9534-40b15cbfc9d5","year":2019},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.278374Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:efcd21fccc43097fd51088fab87ce9dfd31ee4572ed687633454e49b39e08b3d","observation_id":"bd44102b-c488-4bdc-bf09-5ff45d809c00","resolution":{"observed_at":"2026-08-15T19:28:52.752361Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.135509Z","title":"Wireless channel prediction of gru based on experience replay and snake optimizer","venue":null,"work_id":"49a0a222-5dc5-4749-9913-80cb58075d6b","year":2023},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.282765Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:4f4406ec3126447a80dd392d7cbd30893f998104145ca46a5300e7850f29784c","observation_id":"ca504431-3e18-4a7b-bf76-2679b6e1faa1","resolution":{"observed_at":"2026-08-15T19:28:53.153664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.122022Z","title":"R., Szepesv \\'a ri, C., Bhatnagar, S., and Sutton, R","venue":null,"work_id":"111692ab-d487-475d-aaca-2cedbd29d62b","year":2010},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.287280Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:e513fd9a325cb99755a206904daaba99e2c935a35c07f067ccb2ae54c4c95c46","observation_id":"f2234625-fc05-4323-8e21-0fda6c05625e","resolution":{"observed_at":"2026-08-15T19:28:53.126342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.108128Z","title":"Change-aware sampling and contrastive learning for satellite images","venue":null,"work_id":"9e8ebd69-329a-424a-a533-761b36d2ba98","year":2023},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.291905Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:b5de86af1ea2282fd839e696a7a4e28f3ed95190c0bd1b5ddc1373eb7c93df56","observation_id":"dd8ffef0-e8b4-4d41-b837-72b7296495de","resolution":{"observed_at":"2026-08-15T19:28:53.112686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.093284Z","title":"3gpp nr: the standard for 5g cellular networks","venue":null,"work_id":"10f7ef31-5122-4d9d-9e70-eb85d7b79825","year":2018},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.354055Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:f7bf43a1ab70dc93b44dab1142ca9a68b2a853c01e7e0d6b46d921269277b0a7","observation_id":"a1828c87-a6c8-4b39-bbe2-5b82f890a40f","resolution":{"observed_at":"2026-08-15T19:28:53.097856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.079380Z","title":null,"venue":null,"work_id":"bcfb98de-c78b-4db3-a4b9-aea62506b6ec","year":2022},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.374984Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:899d4c67fb01f285e74fc490d5f97b542592ed3d99bd8fde9b0d94794e1f4ff0","observation_id":"67bd18ce-e1e3-468b-bce4-41750c4b5d93","resolution":{"observed_at":"2026-08-15T19:28:53.083965Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.407892Z","title":"Experience replay for continual learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.407892Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:5829fe37e0e65794cdd7a9d32d433024c48fc350f77ca39e1bec8edd37e0d854","observation_id":"90a56303-d8c5-40ab-a450-b628303d151c","resolution":{"observed_at":"2026-08-15T19:28:52.407892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11756","last_updated":"2025-02-17T12:52:10Z","snapshot_observed_at":"2026-08-16T12:57:44.482041Z","submitted_at":"2025-02-17T12:52:10Z","title":"On the Computation of the Fisher Information in Continual Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11756","snapshot_observed_at":"2026-08-15T19:28:52.495310Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.495310Z"},"links":{"cited_paper":"/paper/2502.11756","citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:a94d0cc93fa924540d663f8e2802b2ea2bfa3b61412645c9802039e7d89e8e9d","observation_id":"f2716f22-19fe-488b-8d44-f1125531625d","resolution":{"observed_at":"2026-08-15T19:28:52.495310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07434","last_updated":"2024-07-10T07:40:40Z","snapshot_observed_at":"2026-08-16T13:35:32.372388Z","submitted_at":"2024-07-10T07:40:40Z","title":"Aging-Resistant Wideband Precoding in 5G and Beyond Using 3D Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":"2407.07434","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.07434","snapshot_observed_at":"2026-08-15T19:28:52.576602Z","title":"Aging-Resistant Wideband Precoding in 5G and Beyond Using 3D Convolutional Neural Networks","venue":"cs.IT","work_id":"daa65c89-51aa-4f93-a1af-7d4765eee772","year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.500033Z"},"links":{"cited_paper":"/paper/2407.07434","citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:f87280d446f62fb17b0bf0ae74d7ba5af182987e87f587c3bfcce3b9bf65961d","observation_id":"2b329156-8df3-4473-bb6e-36ec99cdf178","resolution":{"observed_at":"2026-08-15T19:28:52.583656Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:53.055132Z","title":"Elastic weight consolidation continual learning based signal detection in multiple channel mimo system","venue":null,"work_id":"c90f6ba1-74b9-4b5a-9a78-6ca920f39269","year":2021},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.504218Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:842926efcdc5aa3efeeee9c985c7007e1991bd5ae19fa953a68d521af544f075","observation_id":"b34be32b-9185-461e-9a2f-e1b30b398153","resolution":{"observed_at":"2026-08-15T19:28:53.060062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:28:52.508236Z","title":"Continual learning through synaptic intelligence","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.508236Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:27371c693dd990bdfce15d624bc240a70f50ea0a5d4bddf0f61730a830535dac","observation_id":"04223f52-0d8c-44c8-b0a6-5a5a9333218e","resolution":{"observed_at":"2026-08-15T19:28:52.508236Z","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":"2026-08-15T19:28:53.032325Z","title":"K., Clerckx, B., and Quek, T","venue":null,"work_id":"577cef08-34b9-48b3-a3be-bcb4a6b58eaf","year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.512564Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:5b6103387220ebb5ff938f0ed76db0fc40db0d6fd3c90e0c2190923db098d513","observation_id":"df942086-c1df-489f-90a3-42c49bfc1978","resolution":{"observed_at":"2026-08-15T19:28:53.036431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06213","last_updated":"2024-06-10T12:25:13Z","snapshot_observed_at":"2026-08-18T10:33:45.073041Z","submitted_at":"2024-06-10T12:25:13Z","title":"A Statistical Theory of Regularization-Based Continual Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06213","snapshot_observed_at":"2026-08-15T19:28:52.516206Z","title":"A statistical theory of regularization-based continual learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.516206Z"},"links":{"cited_paper":"/paper/2406.06213","citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:757606bc3e8309c753e9fe1facd51471347f374fc56b9108f8f512118b8f656c","observation_id":"23dc0fab-5e3e-4a25-a323-9bc420a2afc9","resolution":{"observed_at":"2026-08-15T19:28:52.516206Z","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":"2026-08-15T19:28:52.962902Z","title":"Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling","venue":null,"work_id":"ea289871-6a71-4e60-a30b-0b02f73ff61e","year":2022},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.521016Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:3ae55fec190508ef5ac75753e1ed350ab600faf3e4e06effdcb4a59da21c113b","observation_id":"ed8c52f9-4cf3-43e2-8648-14327b052a38","resolution":{"observed_at":"2026-08-15T19:28:53.003347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-15T19:28:52.948070Z","title":"Joint channel estimation and data detection in massive mimo systems based on diffusion models","venue":null,"work_id":"263bdf35-3dd8-4be6-8d2f-b8c2b158a755","year":2024},"citing_paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T19:28:52.525376Z"},"links":{"citing_paper":"/paper/2506.22471"},"observation_digest":"sha256:6f27ec0915ef5f0012ec37752e9dbbf02744a2443dcc43d293bfe2e4e7ce1a31","observation_id":"c02a3cc3-1510-45af-8ed8-3b29907338cf","resolution":{"observed_at":"2026-08-15T19:28:52.952916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.22471","last_updated":"2025-06-19T19:13:58Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-18T10:38:26.908091Z","submitted_at":"2025-06-19T19:13:58Z","title":"Continual Learning for Wireless Channel Prediction"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":39},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.22471."}