{"as_of":"2026-08-08T07:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ed04fede03777571b3b4e0156128bf10834cb7d984cbff59ca4377fafb75d6f","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:41:16.366691Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"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/2608.00195/citation-record","integrity":"/paper/2608.00195/integrity","json":"/paper/2608.00195/citation-record.json","paper":"/paper/2608.00195"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T00:41:14.746708Z","title":"U-net: Con- volutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:14.746708Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:3648ce92d4fff40292d6108c9b5dbe2deb75b1bd5e64defecd8e1c5c8ede1203","observation_id":"42790c9c-4401-4757-b787-ecb374b713c7","resolution":{"observed_at":"2026-08-04T00:41:14.746708Z","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":"2026-08-04T00:41:14.815775Z","title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:14.815775Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:c8267d90eacc9bf6bcd76c8180c7abd33e5940d84b520f9ec22c65a497d16e20","observation_id":"02dcc10f-687b-4516-89b4-c8b1486cf8ce","resolution":{"observed_at":"2026-08-04T00:41:14.815775Z","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":"2026-08-04T00:41:14.976485Z","title":"TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:14.976485Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:aeae7844bdf70865179d13093182bcb73617870aa770f3ebf6b25d0eb0f434f1","observation_id":"31537986-381b-4884-b813-823424cdc619","resolution":{"observed_at":"2026-08-04T00:41:14.976485Z","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":"2026-08-04T00:41:15.122673Z","title":"TotalSegmentator MRI: Robust sequence-independent segmentation of multiple anatomic structures in MRI,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.122673Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:d3cf03e703996e6eb097495add6a3d5045527030415a4a26b3c2f511fb21e957","observation_id":"25a87cf8-82da-430b-8132-d15873c7d0de","resolution":{"observed_at":"2026-08-04T00:41:15.122673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-04T00:41:15.332432Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.332432Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:5eee1e36f578f05e9536d4170c4175d916dcb8ecb16e69f253dce4c46cefe7f4","observation_id":"aef52602-b65c-4134-a5fa-2af27a7ebf6a","resolution":{"observed_at":"2026-08-04T00:41:15.332432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-04T00:41:15.535101Z","title":"Sam 2: Segment anything in images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.535101Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:c47f8b9043887ae4139df978c474df6409e082e4a23c77b21815a32435c2ee6c","observation_id":"c101501d-e2bd-4ae9-a50d-68af31e9f6a1","resolution":{"observed_at":"2026-08-04T00:41:15.535101Z","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":"2026-08-04T00:41:15.696034Z","title":"Seg- ment anything in medical images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.696034Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:dfc04694f581ee4ddfff01406c1c820c3dac83a7586f42305991cb7de4d45390","observation_id":"28a7cbbd-7bb9-464c-a9ae-b82d9760e71e","resolution":{"observed_at":"2026-08-04T00:41:15.696034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03322","last_updated":"2024-08-06T17:58:18Z","snapshot_observed_at":"2026-08-04T09:23:08.214972Z","submitted_at":"2024-08-06T17:58:18Z","title":"Segment Anything in Medical Images and Videos: Benchmark and Deployment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03322","snapshot_observed_at":"2026-08-04T00:41:15.779105Z","title":"MedSAM2: Segment anything in 3D medical images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.779105Z"},"links":{"cited_paper":"/paper/2408.03322","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:80aac5515cef62bcceb33cbe76da67c7deadda85b280dea5cd4288e3673bdeb6","observation_id":"d6cb62f5-b832-4365-900b-ff900c638c7b","resolution":{"observed_at":"2026-08-04T00:41:15.779105Z","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":"2026-08-04T00:41:15.855416Z","title":"SIT-SAM: A semantic-integration trans- former that adapts the segment anything model to medical imaging,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.855416Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:b1212ece3929809c76455d7fcccfa98eaf225bf1488cea5effa6fea0d31f190d","observation_id":"6c0d6e8f-45f1-4370-8556-ceeae469863c","resolution":{"observed_at":"2026-08-04T00:41:15.855416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12974","last_updated":"2024-01-23T18:59:25Z","snapshot_observed_at":"2026-08-05T01:27:30.973789Z","submitted_at":"2024-01-23T18:59:25Z","title":"SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12974","snapshot_observed_at":"2026-08-04T00:41:15.939497Z","title":"SegmentAnyBone: A universal model that segments any bone at any location on MRI,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:15.939497Z"},"links":{"cited_paper":"/paper/2401.12974","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:ba5925ed71da96425e855de367d9e809bfde365337b1cfdbdda575a560f12dbd","observation_id":"f843e248-78a1-4aa4-a532-bb30b1e49d53","resolution":{"observed_at":"2026-08-04T00:41:15.939497Z","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":"2026-08-04T00:41:16.019650Z","title":"Foundation models for generalist medical artificial intelligence,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.019650Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:bf69c62906f107eced495bd2ec5b4db680245c39f0602eee0c1f374948c6f7f7","observation_id":"040b39dd-9746-4362-ade3-ec7bf2e5fa85","resolution":{"observed_at":"2026-08-04T00:41:16.019650Z","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":"2026-08-04T00:41:16.072266Z","title":"Generalist mod- els in medical image segmentation: A survey and compar- ison,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.072266Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:4ce532ed7c7cc4164d9219bc1d03229ec6226a578fe2552fa2cc6d052c403cca","observation_id":"a3aeb96a-0a9d-4735-90b6-4b6c7f6be4fa","resolution":{"observed_at":"2026-08-04T00:41:16.072266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.08373","last_updated":"2025-03-11T12:30:34Z","snapshot_observed_at":"2026-08-07T17:13:08.738831Z","submitted_at":"2025-03-11T12:30:34Z","title":"nnInteractive: Redefining 3D Promptable Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.08373","snapshot_observed_at":"2026-08-04T00:41:16.151395Z","title":"nnInteractive: Redefining 3D promptable segmentation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.151395Z"},"links":{"cited_paper":"/paper/2503.08373","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:1c9a2fbab5925158bd84fd3e4e78d6623b74dc24e057a4f53416f880ef37885f","observation_id":"de94c243-5721-47cc-8d5c-19c3b7eb4ad7","resolution":{"observed_at":"2026-08-04T00:41:16.151395Z","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":"2026-08-04T00:41:16.226530Z","title":"A multiclass radiomics method-based WHO severity scale for improving COVID- 19 patient assessment and disease characterization from CT scans,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.226530Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:1720bb39ffe3263406104cde406a6f98dd3ea53a571fe28509114cb8e50301df","observation_id":"ea113533-b00c-4ba2-9d02-974abe94fd98","resolution":{"observed_at":"2026-08-04T00:41:16.226530Z","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":"2026-08-04T00:41:16.293357Z","title":"V oxTell: Free-text promptable universal 3D medical image segmen- tation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.293357Z"},"links":{"citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:a023d6834e72143f1d623d29f25bd3827d8a8efa902c716debf402251ea0049f","observation_id":"f555ecfd-2abe-473b-8cc9-bf7d3f5d2bdf","resolution":{"observed_at":"2026-08-04T00:41:16.293357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.05081","last_updated":"2026-05-01T19:02:06Z","snapshot_observed_at":"2026-08-06T09:30:02.681376Z","submitted_at":"2026-04-06T18:35:57Z","title":"MedGemma 1.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.05081","snapshot_observed_at":"2026-08-04T00:41:16.366691Z","title":"MedGemma 1.5 technical report,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T00:41:16.366691Z"},"links":{"cited_paper":"/paper/2604.05081","citing_paper":"/paper/2608.00195"},"observation_digest":"sha256:0dad7ec988e352abae6b3e834cebb92f635f075de528bf6d06f5b6f57420a854","observation_id":"5c4ec2c6-9d1c-4dc5-b150-7d36ea942a2c","resolution":{"observed_at":"2026-08-04T00:41:16.366691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.00195","last_updated":"2026-07-31T18:23:32Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-06T23:12:06.082559Z","submitted_at":"2026-07-31T18:23:32Z","title":"MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":16},"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 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2608.00195."}