{"as_of":"2026-08-16T01:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8db3fecd83ba243885343ceafebd08f1663ed3c46525473dbd9eacb444f6f7a","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T00:38:27.205080Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2607.26196/citation-record","integrity":"/paper/2607.26196/integrity","json":"/paper/2607.26196/citation-record.json","paper":"/paper/2607.26196"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T00:38:25.770734Z","title":"Gqa: Training generalized multi-query transformer models from multi-head checkpoints","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:25.770734Z"},"links":{"citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:1d4c72e7639e1c779a532d20b269ea937d8aa11d700cc1634ca84cc92d8142f8","observation_id":"22b160d3-ff99-4bcc-8019-b083cedd31fd","resolution":{"observed_at":"2026-08-01T00:38:25.770734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.13315","last_updated":"2026-06-11T13:09:59Z","snapshot_observed_at":"2026-08-11T21:54:45.934668Z","submitted_at":"2026-06-11T13:09:59Z","title":"Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.13315","snapshot_observed_at":"2026-08-01T00:38:26.092799Z","title":"Masked and predictive self-supervised foun- dation models for 3d brain mri.arXiv preprint arXiv:2606.13315,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.092799Z"},"links":{"cited_paper":"/paper/2606.13315","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:1a85158c29dcb51ca63495f72deeade91bf1e971dff7f6dbb242ff55053bc090","observation_id":"3629b68a-4603-4fe1-8f3b-a87ebbd4923a","resolution":{"observed_at":"2026-08-01T00:38:26.092799Z","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":"2026-08-01T00:38:26.161401Z","title":"Li, Aravind R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.161401Z"},"links":{"citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:2fc00204a3eee86908b0c9e262c072242dbe7154ab0185737432fd5e753a2361","observation_id":"f479b64a-f925-4b5e-95c5-e3bf5b94d208","resolution":{"observed_at":"2026-08-01T00:38:26.161401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-01T00:38:26.331911Z","title":"Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.331911Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:3f44c86a4e71b0bacac1c66ce251b829693b141e4413d566162c7d830d0da223","observation_id":"08602dc4-92a1-432a-bba0-51a987d62e85","resolution":{"observed_at":"2026-08-01T00:38:26.331911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.14482","last_updated":"2026-06-11T10:07:56Z","snapshot_observed_at":"2026-08-06T07:23:27.418432Z","submitted_at":"2026-03-15T17:02:40Z","title":"V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.14482","snapshot_observed_at":"2026-08-01T00:38:26.561459Z","title":"V-jepa 2.1: Unlocking dense features in video self-supervised learning.arXiv preprint arXiv:2603.14482,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.561459Z"},"links":{"cited_paper":"/paper/2603.14482","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:70e0149134ef89b6341dbbba68c90730b92923dadd41b3f189b2566197892cb1","observation_id":"4de1f005-3e32-417e-910d-0d11d4c09e1a","resolution":{"observed_at":"2026-08-01T00:38:26.561459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.15710","last_updated":"2026-05-28T21:06:00Z","snapshot_observed_at":"2026-08-04T09:23:31.085616Z","submitted_at":"2025-10-17T14:54:58Z","title":"UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.15710","snapshot_observed_at":"2026-08-01T00:38:26.614608Z","title":"Unimedvl: Unifying medical multi- modal understanding and generation through observation- knowledge-analysis.arXiv preprint arXiv:2510.15710,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.614608Z"},"links":{"cited_paper":"/paper/2510.15710","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:74161f31cb7f4b9b369ea1a65b59bd2886eafa771394f6fc6551290ee245ab5c","observation_id":"6cbd6758-fb13-4717-a0a1-c0e98505b618","resolution":{"observed_at":"2026-08-01T00:38:26.614608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-01T00:38:26.683807Z","title":"Fernando P ´erez-Garc´ıa, Harshita Sharma, Sam Bond- Taylor, Kenza Bouzid, Valentina Salvatelli, Maxim- ilian Ilse, Shruthi Bannur, Daniel C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.683807Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:347244e3966690ed97b7172809839a51f8972890e0e76ea325cbb6ba2d9a0854","observation_id":"e7c890f4-09b1-4966-b476-37a81410d9d2","resolution":{"observed_at":"2026-08-01T00:38:26.683807Z","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":"2026-08-01T00:38:26.765295Z","title":"Arnold, and William Speier","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.765295Z"},"links":{"citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:1fafbb4f92828cd777a2f97e9cc4631b066b91f58ee120a3b3d32508567e1152","observation_id":"ee444b32-ebe8-4741-a5d5-7a46070674e0","resolution":{"observed_at":"2026-08-01T00:38:26.765295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10104","last_updated":"2025-08-13T18:00:55Z","snapshot_observed_at":"2026-08-13T10:44:26.934833Z","submitted_at":"2025-08-13T18:00:55Z","title":"DINOv3","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10104","snapshot_observed_at":"2026-08-01T00:38:26.845070Z","title":"Dinov3.arXiv preprint arXiv:2508.10104,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.845070Z"},"links":{"cited_paper":"/paper/2508.10104","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:9ba1f2844b6c3677b70f10b83e0109115e1a08c100b284a2469f7a4bcca95c27","observation_id":"b5d0e467-933d-40e1-9b5e-76276c821962","resolution":{"observed_at":"2026-08-01T00:38:26.845070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07778","last_updated":"2026-05-25T01:18:26Z","snapshot_observed_at":"2026-08-13T10:13:36.600116Z","submitted_at":"2023-09-14T15:09:35Z","title":"Virchow: A Million-Slide Digital Pathology Foundation Model","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07778","snapshot_observed_at":"2026-08-01T00:38:26.969909Z","title":"Virchow: A million-slide digital pathology foun- dation model.arXiv preprint arXiv:2309.07778,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.969909Z"},"links":{"cited_paper":"/paper/2309.07778","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:b3ab2f2a3c99fa4d6863c7b071a2e993275d24bdc71ce42801519510c11929db","observation_id":"586d604d-cea0-4b85-9dab-430f9e9095dc","resolution":{"observed_at":"2026-08-01T00:38:26.969909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-01T00:38:27.036934Z","title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:27.036934Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:27a6934a02086790625854a1f1389f8249a7fee58ac98349fd90edc1e74557d7","observation_id":"3ab04933-22f3-4cd4-9ada-82d85d8031be","resolution":{"observed_at":"2026-08-01T00:38:27.036934Z","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":"2026-08-01T00:38:27.136877Z","title":"Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and St´ephane Deny","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:27.136877Z"},"links":{"citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:80419d46e8bd7154e7bfe709678a0e933f55a9fbb0c58a419f2957845f620f20","observation_id":"3d210b6c-45ca-4a18-99f4-61b72b4fe354","resolution":{"observed_at":"2026-08-01T00:38:27.136877Z","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":"2026-08-01T00:38:27.205080Z","title":"Gotway, and Jianming Liang","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:27.205080Z"},"links":{"citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:f2d2c5b23877bd4cf772bc614c19f3b2bf37fca09e3394cdeba0d712c59a682f","observation_id":"9711f7ee-6e67-4df9-80e8-40d8788d566f","resolution":{"observed_at":"2026-08-01T00:38:27.205080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.16775","last_updated":"2026-05-16T03:09:25Z","snapshot_observed_at":"2026-08-15T00:54:28.194459Z","submitted_at":"2026-05-16T03:09:25Z","title":"VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.16775","snapshot_observed_at":"2026-08-01T00:38:26.402664Z","title":"V olta-3d: Self-supervised learning for brain mri using 3d volumet- ric token alignment.arXiv preprint arXiv:2605.16775,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.402664Z"},"links":{"cited_paper":"/paper/2605.16775","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:4406844bf9f9784cd800af32d1861ae06fe56343f824f6bfdbb4e898fef7b8e9","observation_id":"3660fe2c-6f39-43cd-8d0d-2cd01756be6a","resolution":{"observed_at":"2026-08-01T00:38:26.402664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00578","last_updated":"2024-03-31T06:55:12Z","snapshot_observed_at":"2026-08-13T19:26:05.889698Z","submitted_at":"2024-03-31T06:55:12Z","title":"M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00578","snapshot_observed_at":"2026-08-01T00:38:25.843369Z","title":"M3d: Advancing 3d medical image analysis with multi-modal large language models.arXiv preprint arXiv:2404.00578,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:25.843369Z"},"links":{"cited_paper":"/paper/2404.00578","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:94ff760f64c74bf121b52a5b508f92a364f56161c8278892fda40fe2925d08c5","observation_id":"c0c13d68-673d-4e3d-8620-2bbc419e8d3d","resolution":{"observed_at":"2026-08-01T00:38:25.843369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.13800","last_updated":"2026-07-16T07:01:10Z","snapshot_observed_at":"2026-08-15T01:37:58.723147Z","submitted_at":"2026-03-14T07:17:45Z","title":"Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.13800","snapshot_observed_at":"2026-08-01T00:38:26.906488Z","title":"Beyond medical diagnostics: How medical multimodal large language models think in space","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.906488Z"},"links":{"cited_paper":"/paper/2603.13800","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:541659cc6d72b19905f0a9d8474abad5f60c1fecca882a45fee8ab71f0a819bb","observation_id":"bd428541-2b5b-49af-8af2-ac17ec3a2430","resolution":{"observed_at":"2026-08-01T00:38:26.906488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09985","last_updated":"2025-06-11T17:57:09Z","snapshot_observed_at":"2026-08-15T13:40:01.739055Z","submitted_at":"2025-06-11T17:57:09Z","title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09985","snapshot_observed_at":"2026-08-01T00:38:25.818751Z","title":"V-jepa 2: Self-supervised video models enable understanding, pre- diction and planning.arXiv preprint arXiv:2506.09985, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:25.818751Z"},"links":{"cited_paper":"/paper/2506.09985","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:0e9a26d81ea97346fffd36cbe360de2db04fbd05e49830ce8919af10523aa469","observation_id":"54a568ed-1bf0-4268-b853-ac9cb0fa00f5","resolution":{"observed_at":"2026-08-01T00:38:25.818751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08471","last_updated":"2024-02-15T18:59:11Z","snapshot_observed_at":"2026-08-14T14:35:29.440022Z","submitted_at":"2024-02-15T18:59:11Z","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08471","snapshot_observed_at":"2026-08-01T00:38:25.974820Z","title":"Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:25.974820Z"},"links":{"cited_paper":"/paper/2404.08471","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:e780308a5fad7169e88d271ecdea521ccae9b2b5c3634fae149a092c3d5792be","observation_id":"8b220415-dab6-4638-a79d-7d8b53285a55","resolution":{"observed_at":"2026-08-01T00:38:25.974820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.09838","last_updated":"2025-02-21T17:39:29Z","snapshot_observed_at":"2026-08-14T04:57:56.034309Z","submitted_at":"2025-02-14T00:42:36Z","title":"HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09838","snapshot_observed_at":"2026-08-01T00:38:26.226015Z","title":"Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.226015Z"},"links":{"cited_paper":"/paper/2502.09838","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:1048a025aed1d0989e491d95d7d250d2baec2b32a064a50e429d9f7f2528bd38","observation_id":"5a7c62c7-d235-4cac-aa6d-b5a4b9193e54","resolution":{"observed_at":"2026-08-01T00:38:26.226015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.08787","last_updated":"2026-06-19T11:21:43Z","snapshot_observed_at":"2026-08-14T08:00:09.299101Z","submitted_at":"2026-05-09T08:16:00Z","title":"Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.08787","snapshot_observed_at":"2026-08-01T00:38:26.497823Z","title":"Lost in volume: The ct- spatialvqa benchmark for evaluating semantic-spatial un- derstanding of 3d medical vision-language models.arXiv preprint arXiv:2605.08787,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-01T00:38:26.497823Z"},"links":{"cited_paper":"/paper/2605.08787","citing_paper":"/paper/2607.26196"},"observation_digest":"sha256:599403ebf195c5013acc733dc7b8b86e4b9107cbda3509bc2dbc55a73e345855","observation_id":"731d6474-ec27-4be8-b630-91e848c08512","resolution":{"observed_at":"2026-08-01T00:38:26.497823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.26196","last_updated":"2026-07-28T19:00:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T08:00:57.731706Z","submitted_at":"2026-07-28T19:00:17Z","title":"Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":20},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.26196."}