{"as_of":"2026-08-08T10:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5f0b461dff8bec5db3eab37d2edd99ff81af2304412b313950569498a0a42918","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T04:29:56.468841Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.22824/citation-record","integrity":"/paper/2607.22824/integrity","json":"/paper/2607.22824/citation-record.json","paper":"/paper/2607.22824"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:29:51.276691Z","title":"Sidky and Xiaochuan Pan","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.276691Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:60a71d1f6b47c6024b834a8dbde28dfd0ef49faed5eceba8f3b7e132cc2ca1cf","observation_id":"8bc2619a-677f-48ef-8ab2-55def168e373","resolution":{"observed_at":"2026-08-01T04:29:51.276691Z","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":"2026-08-01T04:29:51.425343Z","title":"Learned primal-dual reconstruction.IEEE Transactions on Medical Imaging, 37(6): 1322–1332,2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.425343Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:64eb14d52d92dd07b6ebee26c482f8cd71f154bdf6cfc7f83551d16548145aa6","observation_id":"6b4fd01d-bb35-4e47-9a26-f6d1a0937e53","resolution":{"observed_at":"2026-08-01T04:29:51.425343Z","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":"2026-08-01T04:29:51.507136Z","title":"LearningavariationalnetworkforreconstructionofacceleratedMRIdata.MagneticResonanceinMedicine, 79(6):3055–3071,2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.507136Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:d686fbba54dbcb2b017e9a3d2a4db24bb1bc481e7d1f62132a3b1cf27725f977","observation_id":"d0225352-3d2c-40ff-95aa-f03c01a7df2f","resolution":{"observed_at":"2026-08-01T04:29:51.507136Z","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":"10.1007/978-3-319-46726-9_50","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ghesu, Vincent Christlein, and Andreas Maier","venue":"Lecture notes in computer science","work_id":"7de34c12-3220-4fc4-bad8-9a4ee1885974","year":2016},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.606304Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:9e8a2af7dbb27969f6771f204405b0d82e56bf89d2a8c41f2388efd67f5d546d","observation_id":"92cc3387-4877-40a1-a72a-6b75a4adf72b","resolution":{"observed_at":"2026-08-01T04:33:23.880466Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:26.927054+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:26.927054+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:29:51.659844Z","title":"Deeplearningcomputedtomography: Learningprojection-domainweightsfromimagedomain 14 in limited angle problems.IEEE Transactions on Medical Imaging, 37(6):1454–1463, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.659844Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:06703edb7a8001ddfb9821343d66ed8a83d79954c686f805fa4712be9a913da2","observation_id":"9542d10c-4e32-497e-b0cf-3857189a9753","resolution":{"observed_at":"2026-08-01T04:29:51.659844Z","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":"2026-08-01T04:29:51.750715Z","title":"Deeplearningtechniquesforinverseproblemsinimaging.IEEEJournalonSelectedAreasinInformationTheory,1 (1):39–56,2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.750715Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:53c3b947030d5a4aa44180efd017b88a90abf38c58b6408f3dc45affa8a46e7a","observation_id":"563ccb19-24a6-4c3d-9d9e-d78834c29d46","resolution":{"observed_at":"2026-08-01T04:29:51.750715Z","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":"2026-08-01T04:29:51.802697Z","title":"Some investi- gations on robustness of deep learning in limited angle tomography","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.802697Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:dd5de32886526a7c3556a98e197f4c60a725bd3d3fdfbf745188b390435bfb34","observation_id":"1fc001ce-2617-43b1-818f-a84af4733c0f","resolution":{"observed_at":"2026-08-01T04:29:51.802697Z","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":"2026-08-01T04:29:51.969847Z","title":"A gentle introduction to deep learning in medicalimageprocessing.ZeitschriftfürMedizinischePhysik,29(2):86–101,2019.doi: 10.1016/j.zemedi.2018.12.003","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:51.969847Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:4545926bf83b134ba39df14bd6dc4bfb5147feba0d2d48df7fcb10e7ee02830b","observation_id":"33cfb3db-a5a1-4820-b8c9-e79b94d628ee","resolution":{"observed_at":"2026-08-01T04:29:51.969847Z","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":"2026-08-01T04:29:52.124198Z","title":"Learning with known operators reduces maximum error bounds.NatureMachineIntelligence,1(8):373–380,2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.124198Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:dc67d99272ff60af5b82068c57fd3d5a65d1c055fb0792e97236f1b3ad79f0fd","observation_id":"366aa960-5a78-4f5e-abba-9cbb357c48da","resolution":{"observed_at":"2026-08-01T04:29:52.124198Z","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":"2026-08-01T04:29:52.243916Z","title":"Known operator learning and hybridmachinelearninginmedicalimaging—areviewofthepast,thepresent,andthefuture.ProgressinBiomedical Engineering,4(2):022002,2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.243916Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:54aff36519d6b6e419e9e76990f4ce97525bd960b2d398a5c0ce004783dd73a3","observation_id":"6c3c0c45-2675-41c2-9363-51b6f6e42f2b","resolution":{"observed_at":"2026-08-01T04:29:52.243916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.08517","last_updated":"2026-05-08T21:58:30Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T21:58:30Z","title":"A Deep Risk Estimator for Known Operator Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.08517","snapshot_observed_at":"2026-08-01T04:29:52.358771Z","title":"Adeepriskestimatorforknownoperator learning.arXivpreprintarXiv:2605.08517,2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.358771Z"},"links":{"cited_paper":"/paper/2605.08517","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:382ad075514b937bf01e08991e38e47cc93926441029e7642551ea3870b60dba","observation_id":"3f4625c5-19db-45d5-99bd-1d9c6854489c","resolution":{"observed_at":"2026-08-01T04:29:52.358771Z","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":"2026-08-01T04:29:52.504246Z","title":"autoresearch: AI agents running research automatically","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.504246Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:8f1f52414ebaf942b8826f9c9d753ca813155a63edd108ff68cf9b361cea4395","observation_id":"04328075-c788-4283-ab0e-861bc05d1b0f","resolution":{"observed_at":"2026-08-01T04:29:52.504246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06292","last_updated":"2024-09-01T00:41:18Z","snapshot_observed_at":"2026-07-06T18:59:43.564435Z","submitted_at":"2024-08-12T16:58:11Z","title":"The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06292","snapshot_observed_at":"2026-08-01T04:29:52.662897Z","title":"The AI scientist: Towards fully automatedopen-endedscientificdiscovery.arXivpreprintarXiv:2408.06292,2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.662897Z"},"links":{"cited_paper":"/paper/2408.06292","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:56f3d53bf3b065de102157de1ff4fc042a5574f8c52d24587694a18fa9ea3b24","observation_id":"c1cb1739-17b2-4f90-bf24-7c67a28daa6f","resolution":{"observed_at":"2026-08-01T04:29:52.662897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15793","last_updated":"2024-11-11T20:01:15Z","snapshot_observed_at":"2026-07-06T18:19:29.996982Z","submitted_at":"2024-05-06T17:41:33Z","title":"SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15793","snapshot_observed_at":"2026-08-01T04:29:52.842526Z","title":"Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.842526Z"},"links":{"cited_paper":"/paper/2405.15793","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:00688b27ddd706611f0abb963e2b8a4fef516454a8fdf1539ac6dd00344e3dad","observation_id":"225ac8d6-5518-4239-bb39-01f2cd65739b","resolution":{"observed_at":"2026-08-01T04:29:52.842526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06770","last_updated":"2024-11-11T23:05:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T16:47:29Z","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06770","snapshot_observed_at":"2026-08-01T04:29:52.995111Z","title":"SWE- bench: Can language models resolve real-world GitHub issues? InInternational Conference on Learning Represen- tations(ICLR),pages54107–54157,2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:52.995111Z"},"links":{"cited_paper":"/paper/2310.06770","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:8ff0d2b26c1c32b5b097996caa286546b460d7d48d1ee15eabdf1c763620f205","observation_id":"0b92ba9c-8183-41c9-aaf7-190cee970a01","resolution":{"observed_at":"2026-08-01T04:29:52.995111Z","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":"2026-08-01T04:29:53.087169Z","title":"LectureNotesinComputerScience.Springer,2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.087169Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:eff422e42e6b005825809764f69d23ca8d460b3d09282519b2377ba2d640a91f","observation_id":"9a55038d-9eff-4ee9-921d-ae878f33904f","resolution":{"observed_at":"2026-08-01T04:29:53.087169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.13282","last_updated":"2026-04-14T20:17:47Z","snapshot_observed_at":"2026-07-06T23:01:19.191464Z","submitted_at":"2026-04-14T20:17:47Z","title":"Agentic MR sequence development: leveraging LLMs with MR skills for automatic physics-informed sequence development","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.13282","snapshot_observed_at":"2026-08-01T04:29:53.204972Z","title":"Agentic MR sequence development: Leveraging LLMs with MR skills for automatic physics-informed sequence development","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.204972Z"},"links":{"cited_paper":"/paper/2604.13282","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:dc8a40909958b270b7917c624766bca95373c9610408340b818a27c231e1ba0e","observation_id":"eccf4347-fd6d-423d-8c42-b54ea7c403e4","resolution":{"observed_at":"2026-08-01T04:29:53.204972Z","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":"10.1002/mp.12345","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"HolmesIII,AliceE.Huang,FarhanaKhan,ShuaiLeng,KyleL.McMillan,GregoryJ.Michalak,KristinaM.Nunez, LifengYu,andJoelG.Fletcher","venue":"Medical Physics","work_id":"04deaa44-d017-4830-bc1e-5c3e067706e1","year":2017},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.347635Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:2529e760f47807804638779ec992f741e483964f8d395544a68ca5ac5e0035bb","observation_id":"e29ddb05-5882-477c-8793-3b4ade30e005","resolution":{"observed_at":"2026-08-01T04:33:23.841527Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:28.021126+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:28.021126+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:29:53.473624Z","title":"Techni- calnote: PYRO-NN:Pythonreconstructionoperatorsinneuralnetworks.MedicalPhysics,46(11):5110–5115,2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.473624Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:348e0404ed451512eeecf442bcc42819128372cff2a8e6b345986dc7003697c9","observation_id":"b1cdeba1-5e24-461a-8f46-a9fc44168631","resolution":{"observed_at":"2026-08-01T04:29:53.473624Z","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":"2026-08-01T04:29:53.568943Z","title":"Reconstruct anything model: A lightweight general model for computational imaging","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.568943Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:7f0a90b1021e0779250f494ad7ef52ee62e917f5f25058907dc2dd1ae769cea6","observation_id":"942a134d-cfd5-4d53-9093-18c40550602d","resolution":{"observed_at":"2026-08-01T04:29:53.568943Z","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":"2026-08-01T04:29:53.749870Z","title":"Sidky and Xiaochuan Pan","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.749870Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:6f854fda51e7c0adca15f4168a282abb9f5cf1c9c7f95ddc82f27037fa85ea9a","observation_id":"7c8988cb-513e-4355-b0d2-8f56441406f3","resolution":{"observed_at":"2026-08-01T04:29:53.749870Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:29:53.921394Z","title":"A novel filtered backprojection-based algorithm for sparse-view CT image reconstruction","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:53.921394Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:db63d63e622ef2df644cc6bbe404c71f192648b7dff16c5f754f26e8a313784a","observation_id":"6d7722cb-7a51-456a-9def-d1eeb9622d6f","resolution":{"observed_at":"2026-08-01T04:29:53.921394Z","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":"2026-08-01T04:29:54.024217Z","title":"ProjectionspacedenoisingwithbilateralfilteringandCTnoisemodelingfordosereductioninCT","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.024217Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:4da04548d14e836a99798f248f87646e10aa4791a4a53ba9a5d0f4ac4cf5bae4","observation_id":"1359eccc-2047-4d90-99cc-9fd0af3c54fb","resolution":{"observed_at":"2026-08-01T04:29:54.024217Z","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":"2026-08-01T04:29:54.146269Z","title":"Ultralow-parameterdenoising: Trainable bilateral filter layers in computed tomography.MedicalPhysics, 49(8):5107–5120, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.146269Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:23aa78bb1edcdbb7e66495a4a87b0b0d99817fbdbcafc451c95abd04df5dd056","observation_id":"0edc9d01-5708-4a07-b838-eeaf3bca2145","resolution":{"observed_at":"2026-08-01T04:29:54.146269Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-01T04:29:54.269417Z","title":"Maltz, and Hengyong Yu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.269417Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:04729f22a63cc19fc22333dae5c99995698703f209ee94662207e06b651179ec","observation_id":"597713ea-4b9f-49c7-a780-522a14190fb8","resolution":{"observed_at":"2026-08-01T04:29:54.269417Z","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":"2026-08-01T04:29:54.496865Z","title":"SwinIR: Image restora- tion using Swin transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.496865Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:46d69d77b0110b0c47bf7dc008ec889f67727a52ec5918ec7bdf92a5e89c2dbd","observation_id":"ec5538c7-957b-4b60-9a31-024ae1398c05","resolution":{"observed_at":"2026-08-01T04:29:54.496865Z","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":"2026-08-01T04:29:54.609343Z","title":"Swintransformer: Hierarchicalvisiontransformerusingshiftedwindows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.609343Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:6cf60be525a1fc58da655384a20328f8d38ed2d7e2da3ac9c04343c66166fc3b","observation_id":"f14f0a9b-6f93-47c8-bc51-c7ee60e0880a","resolution":{"observed_at":"2026-08-01T04:29:54.609343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00634","last_updated":"2021-07-05T06:24:14Z","snapshot_observed_at":"2026-08-04T05:49:48.408648Z","submitted_at":"2021-02-28T21:46:54Z","title":"TransCT: Dual-path Transformer for Low Dose Computed Tomography","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00634","snapshot_observed_at":"2026-08-01T04:29:54.793228Z","title":"TransCT:Dual-pathtransformerforlow-dose computed tomography","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.793228Z"},"links":{"cited_paper":"/paper/2103.00634","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:b4beb25f1e6551261c8c4e92ddaad24e6ea84f17b8220355d481c5b64683ac35","observation_id":"4cbbd7db-e7dc-411b-8965-c96e8803fdf8","resolution":{"observed_at":"2026-08-01T04:29:54.793228Z","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":"10.1007/978-3-662-54345-0_25","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A deep learning architecture for limited- angle computed tomography reconstruction","venue":"Informatik aktuell","work_id":"f20bb106-35d5-4aef-8632-8e0fe63b6640","year":2017},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.976145Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:292e21db43d72ba49c155c418e2199aeadd2837ac8e1330e024117cd0ddd4c9a","observation_id":"f0e32eff-da68-46d9-aacc-54bc9b71a130","resolution":{"observed_at":"2026-08-01T04:33:23.805073Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:28.667245+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:28.667245+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:29:55.153648Z","title":"On the benefit of dual-domain denoisinginaself-supervisedlow-doseCTsetting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.153648Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:7418fa34595a28f5bad1097a97a827395257d17204a3a2a92c5fa2a1f1546e28","observation_id":"4e4cf01e-658a-488f-aace-a5c5b2236232","resolution":{"observed_at":"2026-08-01T04:29:55.153648Z","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":"10.1109/tci.2020","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:33:23.793361Z","title":"Hendriksen, Daniël M","venue":null,"work_id":"b998f2f0-cfb9-4145-b8d0-9ada59e6de3b","year":2020},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.278530Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:29500fc6d6fd3ec5645fee7467e1651c0dca3aa520f352d568d7d3d37d8804d4","observation_id":"37399888-23a6-4669-863f-53f6c62efbe1","resolution":{"observed_at":"2026-08-01T04:33:23.796211Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:28.854796+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:28.854796+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T04:29:55.532268Z","title":"DM4CT:Benchmarkingdiffusionmodelsforcomputedtomog- raphyreconstruction","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.532268Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:44c0b5cde49bb14b5d7d0266b6e86f7ad51f602868722631d90ce725eaf78844","observation_id":"b10c8026-a854-4d63-8932-f44e0de7c99c","resolution":{"observed_at":"2026-08-01T04:29:55.532268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19043","last_updated":"2024-07-19T09:42:22Z","snapshot_observed_at":"2026-08-06T17:07:50.510135Z","submitted_at":"2024-02-29T11:11:05Z","title":"WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19043","snapshot_observed_at":"2026-08-01T04:29:55.649055Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.649055Z"},"links":{"cited_paper":"/paper/2402.19043","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:0e5bd4c0013ebf4ba1b38605a0e27daf3588aece7b4547ab72b4742ee7109f65","observation_id":"ee71779b-a933-4f31-9e34-e3aa302f8b9a","resolution":{"observed_at":"2026-08-01T04:29:55.649055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08005","last_updated":"2022-06-16T00:01:39Z","snapshot_observed_at":"2026-08-06T05:01:09.365741Z","submitted_at":"2021-11-15T05:41:12Z","title":"Solving Inverse Problems in Medical Imaging with Score-Based Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08005","snapshot_observed_at":"2026-08-01T04:29:55.742631Z","title":"Solvinginverseproblemsinmedicalimagingwithscore-based generativemodels","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.742631Z"},"links":{"cited_paper":"/paper/2111.08005","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:4865f878fdde9dd82f44a896b0344aaa49feb1577de745efcef0bb2bc61c4e2b","observation_id":"4b5ec4cc-61be-442f-8329-31c76ecd6626","resolution":{"observed_at":"2026-08-01T04:29:55.742631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14687","last_updated":"2024-05-20T04:23:45Z","snapshot_observed_at":"2026-08-03T03:59:22.374270Z","submitted_at":"2022-09-29T11:12:27Z","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14687","snapshot_observed_at":"2026-08-01T04:29:55.882651Z","title":"McCann, Marc L","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:55.882651Z"},"links":{"cited_paper":"/paper/2209.14687","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:40a04b9c9504f75bf1f33277cbc1159ce123a7661d4f129845722608d19a54fa","observation_id":"d43620c6-e240-4cf0-a9c8-65893db2505e","resolution":{"observed_at":"2026-08-01T04:29:55.882651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14540","last_updated":"2022-09-29T04:06:00Z","snapshot_observed_at":"2026-08-05T02:52:39.769508Z","submitted_at":"2022-09-29T04:06:00Z","title":"NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14540","snapshot_observed_at":"2026-08-01T04:29:56.037265Z","title":"doi: 10.1007/978-3-031-16446-0_42","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:56.037265Z"},"links":{"cited_paper":"/paper/2209.14540","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:74462b24f919663a1d722b4ea731f88495682c5581829c18d76a649a3296eea6","observation_id":"6b7f69fc-5b66-45cb-b87b-da15b1a496d8","resolution":{"observed_at":"2026-08-01T04:29:56.037265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20693","last_updated":"2024-10-27T05:42:54Z","snapshot_observed_at":"2026-07-06T18:23:10.807498Z","submitted_at":"2024-05-31T08:39:02Z","title":"R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20693","snapshot_observed_at":"2026-08-01T04:29:56.196791Z","title":"R2-Gaussian: Rectifying radia- tive Gaussian splatting for tomographic reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:56.196791Z"},"links":{"cited_paper":"/paper/2405.20693","citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:a6ba54ab3291e8018e39c96ace78af910bb39f9ce71692b71b610c7093b64053","observation_id":"9bc81cff-46e5-4f30-82fd-eaa52fb9ac4b","resolution":{"observed_at":"2026-08-01T04:29:56.196791Z","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":"10.1007/s10462-025-11171-4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A review on 3D Gaussian splattingforsparseviewreconstruction.ArtificialIntelligenceReview,58:215,2025.doi: 10.1007/s10462-025-11171-4","venue":"Artificial Intelligence Review","work_id":"575e6671-c3d2-44ba-8427-e3dfdd1d0357","year":2025},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:56.318546Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:0c8aafc973852b585491692ce9ccfd65f16282f8754ff0f273df769b30dc71d7","observation_id":"b52979c5-a62f-42ef-8789-823dbd083963","resolution":{"observed_at":"2026-08-01T04:33:23.787575Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:29.083659+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:29.083659+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/0031-9155/44/2/019","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Single-slicerebinningmethodforhelicalcone-beamCT.Physics inMedicine&Biology,44(2):561–570,1999","venue":"Physics in Medicine and Biology","work_id":"4bb699e1-fadf-4145-82b3-28f5cad98c95","year":1999},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:56.468841Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:abc400c6334cfbfbd588112532da830b7adb10e9ff25db9dd620ae264e9639cf","observation_id":"8afa0b79-6cfe-498f-8a9c-fc57d798169d","resolution":{"observed_at":"2026-08-01T04:33:23.778423Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:29.299942+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:29.299942+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1361-6560/ad3db9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Physics in Medicine and Biology","work_id":"060f7f98-14bc-41c6-a85a-a21f290e4095","year":null},"citing_paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T04:29:54.365740Z"},"links":{"citing_paper":"/paper/2607.22824"},"observation_digest":"sha256:e6c82de3bf485d93665df04574528cb6acc7a23214af24255e1e2789a700bc99","observation_id":"8a1b5e40-1fd6-43a8-8127-1b42ca931116","resolution":{"observed_at":"2026-08-01T04:33:23.813956Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:39:29.479373+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:39:29.479373+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.22824","last_updated":"2026-07-24T18:03:21Z","latest_version":1,"primary_category":"physics.med-ph","snapshot_observed_at":"2026-08-08T08:20:47.716330Z","submitted_at":"2026-07-24T18:03:21Z","title":"Agentic Autoresearch for CT Reconstruction"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":7,"verified_fuzzy":0},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.22824."}