{"as_of":"2026-08-07T22:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f964ac4d53104ef0b16053353597bce6e8d1fe8b67517d5fed04b147c9d4315","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T17:15:35.029890Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:18:16.474432Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.23631","snapshot_observed_at":"2026-08-04T17:18:16.474432Z","title":"Pathselect: Sequential token selection for whole slide pathology.arXiv preprint arXiv:2607.23631, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01985","last_updated":"2026-08-03T09:46:32Z","snapshot_observed_at":"2026-08-07T19:51:18.997802Z","submitted_at":"2026-08-03T09:46:32Z","title":"DiffPrune: differentiable information throttling for token pruning in vision-language models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-04T17:18:16.474432Z"},"links":{"cited_paper":"/paper/2607.23631","citing_paper":"/paper/2608.01985"},"observation_digest":"sha256:1c5de748711d4fbbd68d12957f4418d74c9eddf47594fa702b73346c355cdb7d","observation_id":"00836b36-0e4f-4756-8dee-4246054c7f18","resolution":{"observed_at":"2026-08-04T17:18:16.474432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2607.23631/citation-record","integrity":"/paper/2607.23631/integrity","json":"/paper/2607.23631/citation-record.json","paper":"/paper/2607.23631"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T17:15:34.821813Z","title":"Scalar: Spatial- concept alignment for robust vision in harsh open world.Pattern Recognition, page 113203, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.821813Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:408994373d93de8b9292e0ee7fe97f84ada148da5676addc876bdc8e0bf891e6","observation_id":"37f944c3-2d8b-4b31-9cdc-39043201b558","resolution":{"observed_at":"2026-07-30T17:15:34.821813Z","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":"2026-07-30T17:15:34.827098Z","title":"A unified multi-task framework enables interpretable chest radiograph analysis.Med, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.827098Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:f38368b90c484568414a02939869fd17517288bc6dcc8b0277580f6b2cef2660","observation_id":"cd0f065b-7822-4ce7-a009-459d530a8451","resolution":{"observed_at":"2026-07-30T17:15:34.827098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08861","last_updated":"2024-10-11T14:41:27Z","snapshot_observed_at":"2026-07-06T19:31:53.397265Z","submitted_at":"2024-10-11T14:41:27Z","title":"A foundation model for generalizable disease diagnosis in chest X-ray images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08861","snapshot_observed_at":"2026-07-30T17:15:34.831153Z","title":"A foundation model for generalizable disease diagnosis in chest x-ray images.arXiv preprint arXiv:2410.08861, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.831153Z"},"links":{"cited_paper":"/paper/2410.08861","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:9b146d30adc9e543e96450d425918b40db5eda06f415d98f5a9064b7972d980f","observation_id":"1d477477-ff97-483b-990d-b28c42e1e526","resolution":{"observed_at":"2026-07-30T17:15:34.831153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.02695","last_updated":"2026-04-03T03:39:30Z","snapshot_observed_at":"2026-07-06T22:52:02.162758Z","submitted_at":"2026-04-03T03:39:30Z","title":"XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.02695","snapshot_observed_at":"2026-07-30T17:15:34.835274Z","title":"Xrayclaw: Cooperative-competitive multi-agent alignment for trustworthy chest x-ray diagnosis.arXiv preprint arXiv:2604.02695, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.835274Z"},"links":{"cited_paper":"/paper/2604.02695","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:984ce8c5fb91ef5e2935560751d643e183095d81b92a2423065f90e492e4e5bd","observation_id":"4c35d70b-73a9-4b61-8f92-48ca1f63994a","resolution":{"observed_at":"2026-07-30T17:15:34.835274Z","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":"2026-07-30T17:15:34.839937Z","title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.Nature medicine, 25(8):1301–1309, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.839937Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:52f8caa7042079b3ae37d092077ecda8b8e32eb7281a0037fb887079caa63061","observation_id":"beddba0a-40e4-471f-ba6b-0090638eb304","resolution":{"observed_at":"2026-07-30T17:15:34.839937Z","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":"2026-07-30T17:15:34.844020Z","title":"A graph-transformer for whole slide image classification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.844020Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:44fedd43505f3bb2a1c7f8aa51667785fb2ac886be35fde720835c96dded39c5","observation_id":"e792948d-29ef-44ee-9e69-3802b88f0931","resolution":{"observed_at":"2026-07-30T17:15:34.844020Z","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":"2026-07-30T17:15:34.848569Z","title":"Streaming convolutional neural networks for end-to- end learning with multi-megapixel images.IEEE transactions on pattern analysis and machine intelligence, 44(3):1581–1590, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.848569Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:e5ab8e684595511dcf6110cf0a866bd4b7cf110036179c5b25afccdaa77d02ce","observation_id":"808c7f7b-a765-43a4-9d5a-221e1b132d0a","resolution":{"observed_at":"2026-07-30T17:15:34.848569Z","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":"2026-07-30T17:15:34.852345Z","title":"Tc-ssa: Token compression via semantic slot aggregation for gigapixel pathology reasoning.MICCAI, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.852345Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:6a0f1b23fec7413f3e63d4d0b8ae70867b909813c32acc0db98a62c88ebf17ab","observation_id":"ee02baa1-7ccf-4663-b609-3eead23ab043","resolution":{"observed_at":"2026-07-30T17:15:34.852345Z","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":"2026-07-30T17:15:34.856301Z","title":"Scaling vision transformers to gigapixel images via hierar- chical self-supervised learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.856301Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:ec5b054ebc40a8fd27e92465dfa8e0c3abb934b60bd5a426bc60fa75cd26d81d","observation_id":"5762b530-9c63-49c9-9beb-8544da2c8c6c","resolution":{"observed_at":"2026-07-30T17:15:34.856301Z","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":"2026-07-30T17:15:34.859965Z","title":"Dynamicvit: Efficient vision transformers with dynamic token sparsification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.859965Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:76e2cd5e84a6b3d40630d0697e309c5366d188a773db6a6a3e45310be32b4137","observation_id":"1ef94d37-889f-4a55-be78-fa30c2b634f4","resolution":{"observed_at":"2026-07-30T17:15:34.859965Z","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":"2026-07-30T17:15:34.863673Z","title":"Token merging: Your ViT but faster","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.863673Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:6b5aa34fc05cce2724b2f0072e23a55e71c057ae5de3a98ac511c8b32949c69b","observation_id":"775111e9-5c66-45ba-a0bd-038cd0c77cbd","resolution":{"observed_at":"2026-07-30T17:15:34.863673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.28051","last_updated":"2026-05-27T06:52:08Z","snapshot_observed_at":"2026-08-03T00:42:32.667278Z","submitted_at":"2026-05-27T06:52:08Z","title":"Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.28051","snapshot_observed_at":"2026-07-30T17:15:34.867442Z","title":"Beyond surrogate gradients: Fully differentiable token pruning for vision-language models.arXiv preprint arXiv:2605.28051, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.867442Z"},"links":{"cited_paper":"/paper/2605.28051","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:539b1978bd88fedc9e702bd014a3ba312a5335c5ee6652a79b16d488bed135c5","observation_id":"be5f0f3d-3207-485d-95e0-62318cfe8873","resolution":{"observed_at":"2026-07-30T17:15:34.867442Z","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":"2026-07-30T17:15:34.871674Z","title":"The model knows which tokens matter:automatic token selection via noise gating.arXiv preprint arXiv:2603.07135, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.871674Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:dc037472f4b3a01d022b57fdfae786ec5e58951349319836c48dbcd4f1849662","observation_id":"b9700206-cd5e-4216-92d1-f50f7a4936b5","resolution":{"observed_at":"2026-07-30T17:15:34.871674Z","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":"2026-07-30T17:15:34.875359Z","title":"Stepwise token selection for efficient multimodal large language models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.875359Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:c08630bae553bf6c3586d245f0774d6d9d405615302fe12df1efe13b3b319e23","observation_id":"80569dfd-0c58-4cab-99b1-9ec9bf1b5859","resolution":{"observed_at":"2026-07-30T17:15:34.875359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.08641","last_updated":"2026-06-07T14:07:28Z","snapshot_observed_at":"2026-07-06T23:48:07.065806Z","submitted_at":"2026-06-07T14:07:28Z","title":"Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.08641","snapshot_observed_at":"2026-07-30T17:15:34.879170Z","title":"Learnable token sparsification for efficient gigapixel whole slide image reasoning.arXiv preprint arXiv:2606.08641, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.879170Z"},"links":{"cited_paper":"/paper/2606.08641","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:94d7809abb2d60733f74e3b01edb31a7d61786f066995b53e19c982df38240a1","observation_id":"4752fe06-6890-4ce7-a867-c307d2f6667d","resolution":{"observed_at":"2026-07-30T17:15:34.879170Z","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":"2026-07-30T17:15:34.883168Z","title":"Stochastic beams and where to find them: The gumbel-top- k trick for sampling sequences without replacement","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.883168Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:f5622d4331c4108453439330af05db764a077b40ffec2139476aa909f94f366b","observation_id":"c0d4c6e3-6704-46a1-9307-380b494ac9d9","resolution":{"observed_at":"2026-07-30T17:15:34.883168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-07-30T17:15:34.886792Z","title":"Estimating or propagating gradients through stochastic neurons for conditional computation.arXiv preprint arXiv:1308.3432, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.886792Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:6101526d01358fbd1cef2e063a1d60a21c2a01af021f92f9bebe562670d3fade","observation_id":"07b2d4ae-a4e6-4797-8226-7fc2f6a80f51","resolution":{"observed_at":"2026-07-30T17:15:34.886792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.23950","last_updated":"2026-04-27T01:56:59Z","snapshot_observed_at":"2026-07-06T23:10:07.178566Z","submitted_at":"2026-04-27T01:56:59Z","title":"LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.23950","snapshot_observed_at":"2026-07-30T17:15:34.890803Z","title":"Learnpruner: Rethinking attention-based token pruning in vision language models.arXiv preprint arXiv:2604.23950, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.890803Z"},"links":{"cited_paper":"/paper/2604.23950","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:24816c4a528076f7f409bc0a625bfdf5db150e9b10c8f0a5a34c02c3eeeff44d","observation_id":"a85287ae-ca97-4de2-b044-2b472c7dd019","resolution":{"observed_at":"2026-07-30T17:15:34.890803Z","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":"2026-07-30T17:15:34.894616Z","title":"Slidechat: A large vision-language assistant for whole-slide pathol- ogy image understanding","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.894616Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:0306cd731b4a8ae2f8f679b453358cf0bc8c54a1b68bd4dcffd3be19e1b0180b","observation_id":"705c54dc-c369-4f30-9af0-c043ea22b1d1","resolution":{"observed_at":"2026-07-30T17:15:34.894616Z","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":"2026-07-30T17:15:34.899005Z","title":"One leaf reveals the season: Occlusion-based contrastive learning with semantic-aware views for efficient visual representation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.899005Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:db981223318a99f8a9dd4bdf087835e85408f76b6c1673931fedf1fb0b55b0ba","observation_id":"bb2f9732-e967-44f1-8717-f738bd46e17b","resolution":{"observed_at":"2026-07-30T17:15:34.899005Z","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":"2026-07-30T17:15:34.902851Z","title":"Fewer tokens, greater scaling: Self-adaptive visual bases for efficient and expansive representation learning.arXiv preprint arXiv:2511.19515, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.902851Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:a50ba22c2eec35d49e45590193648f101e5fcde48008bb79c39ae3d2154044d6","observation_id":"59177f7c-5626-4cf3-aeaf-0034eb63146f","resolution":{"observed_at":"2026-07-30T17:15:34.902851Z","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":"2026-07-30T17:15:34.906635Z","title":"Attention-based deep multiple instance learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.906635Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:eff2a46cede614d2c6ba5b8ff97d6179ba8ab1ecb6e1177bcfad1020a632fa64","observation_id":"82c76b2d-80bb-411a-8ff9-2252a79d1a50","resolution":{"observed_at":"2026-07-30T17:15:34.906635Z","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":"2026-07-30T17:15:34.910637Z","title":"Data-efficient and weakly supervised computational pathology on whole-slide images.Nature biomedical engineering, 5(6):555–570, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.910637Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:1899bcb4f6ca389d3c0974afed17cc73ec12a2983a9f079d40fecbb665266418","observation_id":"5d3b36ea-3a78-4ad8-ac29-3fc48b4b4ed4","resolution":{"observed_at":"2026-07-30T17:15:34.910637Z","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":"2026-07-30T17:15:34.914563Z","title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in neural information processing systems, 34:2136–2147, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.914563Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:04d3b1645a4f43179ff2ec2b339f58c587d80b5f1929243018fa38e67948a123","observation_id":"8cd9c1d3-456c-40e9-8fe9-6e19f65c5451","resolution":{"observed_at":"2026-07-30T17:15:34.914563Z","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":"2026-07-30T17:15:34.918170Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.918170Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:d30c9244c2eaf54347f19d4d6ad0e92419361890b0e66489e41de32707110aba","observation_id":"748798ee-6243-4c73-a36e-192c07c52b65","resolution":{"observed_at":"2026-07-30T17:15:34.918170Z","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":"2026-07-30T17:15:34.921692Z","title":"Learning heterogeneous tissues with mixture of experts for gigapixel whole slide images","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.921692Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:bd33456b603b15ce67fda7ef78190446d2d55605eafab15e681c8a5cb75fa513","observation_id":"fdf081da-70e7-4b3d-8319-f3a0f99f211b","resolution":{"observed_at":"2026-07-30T17:15:34.921692Z","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":"2026-07-30T17:15:34.925276Z","title":"Multi-modal gated mixture of local-to-global experts for dynamic image fusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.925276Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:a9dc12b11d79b2a94199705ed8df56c1586d104364172ea3ca84c02837cd83bc","observation_id":"3449b44b-22e3-4d51-8bce-58031100c450","resolution":{"observed_at":"2026-07-30T17:15:34.925276Z","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":"2026-07-30T17:15:34.928690Z","title":"Feature re-embedding: Towards foundation model-level performance in computational pathology","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.928690Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:c239173b97ec536c739a6ff4f816ffe6bd3abf49ab7267e047e17b887b3c590b","observation_id":"48a37495-aec1-4070-a986-9f9c08a6167b","resolution":{"observed_at":"2026-07-30T17:15:34.928690Z","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":"2026-07-30T17:15:34.932468Z","title":"Revisiting end-to-end learning with slide-level supervision in computational pathology.Advances in Neural Information Processing Systems, 38:160279–160312, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.932468Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:1662f6aaa18af0d2ed38c692b16dc0cfb2b4d892e211bd470f794f9cae06444e","observation_id":"0673f8d5-f108-4117-b7ba-fede84709146","resolution":{"observed_at":"2026-07-30T17:15:34.932468Z","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":"2026-07-30T17:15:34.936149Z","title":"Towards a general-purpose foundation model for computational pathology.Nature medicine, 30(3):850–862, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.936149Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:cae37c10b86b206061f88ff5e52436aaf18b333659f310bae16c4be9c02155e0","observation_id":"5062f046-4cdd-4079-8f8c-9fe2bc556c5c","resolution":{"observed_at":"2026-07-30T17:15:34.936149Z","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":"2026-07-30T17:15:34.939778Z","title":"A whole-slide foundation model for digital pathology from real-world data.Nature, 630(8015):181–188, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.939778Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:33495d17bf1b3d61a1afa988a5a033c6365a5f252ebe44f38c35ce70c6c35c6b","observation_id":"c979e19c-e6a2-46d8-a9ae-cd87710dac64","resolution":{"observed_at":"2026-07-30T17:15:34.939778Z","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":"2026-07-30T17:15:34.943530Z","title":"Multimodal model for computational pathology: Representation learning and image compression.arXiv preprint arXiv:2603.18660, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.943530Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:53f3225b2f15ded18457441f5ff8d3624f8d3d0786f0ee6f26ea9c654f19f3cb","observation_id":"4d383d96-19c3-4ecf-9473-d60d5b85fca7","resolution":{"observed_at":"2026-07-30T17:15:34.943530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19684","last_updated":"2024-09-29T12:23:10Z","snapshot_observed_at":"2026-07-06T19:24:03.133851Z","submitted_at":"2024-09-29T12:23:10Z","title":"MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19684","snapshot_observed_at":"2026-07-30T17:15:34.947525Z","title":"Medvilam: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation.arXiv preprint arXiv:2409.19684, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.947525Z"},"links":{"cited_paper":"/paper/2409.19684","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:c0dd7317fe7fd3ba65a0261c9219bc0e93f329f60ad3e267df0ad3e1e0a7232f","observation_id":"66c42414-cee6-4ad4-b5b2-795048e0f53e","resolution":{"observed_at":"2026-07-30T17:15:34.947525Z","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":"2026-07-30T17:15:34.951427Z","title":"A visual–language foun- dation model for pathology image analysis using medical twitter.Nature medicine, 29(9):2307–2316, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.951427Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:6f3f1fd4a72d1c7d565fbe09c37408a10fd892de0708d4987dbe074b8fae49bb","observation_id":"f7f8bff8-4763-4af1-a463-06deceeaa43d","resolution":{"observed_at":"2026-07-30T17:15:34.951427Z","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":"2026-07-30T17:15:34.954838Z","title":"A visual-language foundation model for computational pathology.Nature medicine, 30(3):863–874, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.954838Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:ed671ea973702b3610978ae4a4fc69f19570e0c7115cef676f82ff48bba5748d","observation_id":"ef09edb5-4adc-4d92-a05a-8ddd29f82c7d","resolution":{"observed_at":"2026-07-30T17:15:34.954838Z","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":"2026-07-30T17:15:34.958548Z","title":"Cpath-omni: A unified multimodal foundation model for patch and whole slide image analysis in computational pathology","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.958548Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:3d95013dbdcdf6af25faaa9f71f97bba17aa2560ae99258abc6c3889f858bf5f","observation_id":"890723f1-3a52-4f86-9e55-0709b71b92d2","resolution":{"observed_at":"2026-07-30T17:15:34.958548Z","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":"2026-07-30T17:15:34.963488Z","title":"Segmentation and vascular vectorization for coronary artery by geometry-based cascaded neural network.IEEE Transactions on Medical Imaging, 44(1):259–269, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.963488Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:bacc460b70693ad4afdc3504ffd8716c0c10d292de822dee226d992f25bba4a2","observation_id":"76437f9d-c954-44b3-8a66-bd8d9210fa8e","resolution":{"observed_at":"2026-07-30T17:15:34.963488Z","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":"2026-07-30T17:15:34.967331Z","title":"Geometry-based end- to-end segmentation of coronary artery in computed tomography angiography","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.967331Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:390e1c141ecfdb0c4088746ce47b0e9aa3f9ad79015152bab79cbc6e79546a2e","observation_id":"b891a22c-8b9e-4bcb-a7df-06ca697ebddf","resolution":{"observed_at":"2026-07-30T17:15:34.967331Z","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":"2026-07-30T17:15:34.970932Z","title":"Efficient chest x-ray representation learning via semantic- partitioned contrastive learning.arXiv preprint arXiv:2603.07113, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.970932Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:bce9cd67763733fd90c3045ee257e9ece7cf219c28fa490ff7d50a03be8f9a3f","observation_id":"8f83c328-8222-4b5a-a616-debdb9b0c9a9","resolution":{"observed_at":"2026-07-30T17:15:34.970932Z","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":"2026-07-30T17:15:34.974578Z","title":"When tokens talk too much: A survey of multimodal long-context token compression across images, videos, and audios.arXiv preprint arXiv:2507.20198, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.974578Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:92e631bfe63fe2b2830ce56d3371a58fb24a1367dc75230fa759f604d415b886","observation_id":"616ed26f-a1a6-405e-ae3f-080df971676b","resolution":{"observed_at":"2026-07-30T17:15:34.974578Z","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":"2026-07-30T17:15:34.978093Z","title":"Mmtok: Multimodal coverage maximization for efficient inference of vlms.International Conference on Learning Representations, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.978093Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:d99a50b7e559b52ea610b2c0fa20d2a9a5aefe33ad04d34f336a62c5a6993c33","observation_id":"0b617662-6b96-4d9f-a558-779cb4a86209","resolution":{"observed_at":"2026-07-30T17:15:34.978093Z","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":"2026-07-30T17:15:34.981704Z","title":"Beyond attention or similarity: Maximizing conditional diversity for token pruning in mllms.Advances in Neural Information Processing Systems, 38:25438–25468, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.981704Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:2e980bb95b78016b3bb443aa24ea7f3bb9478ba5fa13b890168e250a7cc94311","observation_id":"14b27d67-d230-43a2-a126-7686e3456498","resolution":{"observed_at":"2026-07-30T17:15:34.981704Z","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":"2026-07-30T17:15:34.985518Z","title":"Dynamic-llava: Efficient multimodal large language models via dynamic vision-language context sparsifi- cation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.985518Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:fc715f76c0ab0bc45eaca0b521555fdcff4bc1a40b57d2a49c67b1cee2143515","observation_id":"7ca0456d-682b-417e-9254-31ee16082514","resolution":{"observed_at":"2026-07-30T17:15:34.985518Z","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":"2026-07-30T17:15:34.989435Z","title":"Hidrop: Hierarchical vision token reduction in mllms via late injection, concave pyramid pruning, and early exit.International Conference on Learning Representations, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.989435Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:25129d1893488b95b77bcb9a3f362e7fa6e950a70e6e9a48b54a4c6933757d9c","observation_id":"f48d75ad-bc87-4939-bab3-af3c1723f750","resolution":{"observed_at":"2026-07-30T17:15:34.989435Z","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":"2026-07-30T17:15:34.993629Z","title":"Visionzip: Longer is better but not necessary in vision language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.993629Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:25f9465971a0627a3e36c3096a754094cb643544e805ecb714adced13c87aeeb","observation_id":"e3bd1bb0-e612-4e7a-a913-0b5927b1daab","resolution":{"observed_at":"2026-07-30T17:15:34.993629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-01T18:34:23.156273Z","submitted_at":"2016-11-03T19:48:08Z","title":"Categorical Reparameterization with Gumbel-Softmax","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01144","snapshot_observed_at":"2026-07-30T17:15:34.997280Z","title":"Categorical reparameterization with gumbel-softmax.arXiv preprint arXiv:1611.01144, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:34.997280Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:6616bfaf919417cb34141907107ce89e4a32a0fa168a747d92b69577a2ff3b88","observation_id":"a4f5c122-f8de-42e9-8f01-f03c05e79e9c","resolution":{"observed_at":"2026-07-30T17:15:34.997280Z","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":"2026-07-30T17:15:35.001832Z","title":"Loc-path: Learning to compress for pathology multimodal large language models.arXiv preprint arXiv:2512.05391, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.001832Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:8a15548b0dbf9dd9a212bbb6c9a9253f45e04f342f5a7835e3bbadae69c65697","observation_id":"0365e8b6-17ef-46ee-b311-2efba3a20474","resolution":{"observed_at":"2026-07-30T17:15:35.001832Z","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":"2026-07-30T17:15:35.005766Z","title":"Wsisum: Wsi summarization via dual-level semantic reconstruc- tion.Medical Image Analysis, page 103970, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.005766Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:8acd8d0bb6233bee30c3cbd755408ac24e34ffc9ffe9c58c24be6f35a403bce9","observation_id":"d1aa1123-8326-4a18-97ba-1b9cb74b1230","resolution":{"observed_at":"2026-07-30T17:15:35.005766Z","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":"2026-07-30T17:15:35.009459Z","title":"Focus: Knowledge-enhanced adaptive visual compression for few-shot whole slide image classification","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.009459Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:2a95adfc21c9fbf0d2f2446b307dfb730e06c76b9b0cecc23434194e5ae987fb","observation_id":"580ff95d-62bc-4ad0-8bbe-66b97609dcb1","resolution":{"observed_at":"2026-07-30T17:15:35.009459Z","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":"2026-07-30T17:15:35.013536Z","title":"Wsi-vqa: Interpreting whole slide images by generative visual question answering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.013536Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:0f59482f9663b564de0eb892fa6f0ac8e0e0191cdf3f9280f96aa54c4ad3a8af","observation_id":"2d2670b3-c3d4-4300-a33d-c8020e92eb63","resolution":{"observed_at":"2026-07-30T17:15:35.013536Z","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":"2026-07-30T17:15:35.017469Z","title":"Llava-med: Training a large language-and-vision assistant for biomedicine in one day.Advances in Neural Information Processing Systems, 36:28541–28564, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.017469Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:5fe064398d2928e6c1f209f30cdfa9425021f96729c247bf0464125b019cfd6a","observation_id":"08847522-c34b-4e59-82e9-e35a39c32fff","resolution":{"observed_at":"2026-07-30T17:15:35.017469Z","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":"2026-07-30T17:15:35.021240Z","title":"Quilt-llava: Visual instruction tuning by extracting localized narratives from open-source histopathology videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.021240Z"},"links":{"citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:20ca44b3654810f694d8ca5a0c8242628edddc7061e8fd7990f2bbaf61c2a55f","observation_id":"8143a059-8863-4c64-b865-2aa3866c490b","resolution":{"observed_at":"2026-07-30T17:15:35.021240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15127","last_updated":"2024-11-04T15:54:21Z","snapshot_observed_at":"2026-07-06T18:04:26.177088Z","submitted_at":"2024-04-23T15:27:19Z","title":"GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15127","snapshot_observed_at":"2026-07-30T17:15:35.025610Z","title":"Meddr: Diagnosis-guided bootstrapping for large-scale medical vision-language learning.arXiv preprint arXiv:2404.15127, 1(3):6, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.025610Z"},"links":{"cited_paper":"/paper/2404.15127","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:347e07e92db3da6d761d0583d928e630bc7a556cd01950b777443e181db91292","observation_id":"cbaa40b7-f1fe-4f7f-85c2-4d7f38beff23","resolution":{"observed_at":"2026-07-30T17:15:35.025610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-07-30T17:15:35.029890Z","title":"Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-30T17:15:35.029890Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2607.23631"},"observation_digest":"sha256:8ef405e6cfae18d9e9435c3ba549b7edd75a43c7e79415fe54d499229b46731f","observation_id":"3b943081-3dca-4ef8-9978-77b3b63b1063","resolution":{"observed_at":"2026-07-30T17:15:35.029890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.23631","last_updated":"2026-07-26T12:36:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T17:24:39.187276Z","submitted_at":"2026-07-26T12:36:16Z","title":"PathSelect: Sequential Token Selection for Whole Slide Pathology"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":54,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":54},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2607.23631."}