{"as_of":"2026-08-14T12:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12f3d48bf8654087e27434f941bbd143e28db44dd3140e5affefeab2688729ed","coverage":[{"denominator":61,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:02:22.867266Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2412.09521/citation-record","integrity":"/paper/2412.09521/integrity","json":"/paper/2412.09521/citation-record.json","paper":"/paper/2412.09521"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.814230Z","title":"Edge-competing pathological liver vessel segmentation with limited labels,","venue":null,"work_id":"543f065c-787c-461d-a82d-4c6f8a8e2fe5","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.209212Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:0d182621baf040cedb59cff3be983bc4ce49e155d4223c2a675a585c00f922fb","observation_id":"5d0d3653-3d7a-4df1-a342-89d5e19f6b26","resolution":{"observed_at":"2026-08-11T17:02:24.821959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.791881Z","title":"Mutual-complementing framework for nuclei detection and segmentation in pathology image,","venue":null,"work_id":"df3e3c75-102d-4e1d-af16-8ac2515e8a23","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.217118Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:86e6614668c968f21e12ec23d730af2eb6ee6126a2635e77b84a6407ad69a76a","observation_id":"38951201-3471-4cd5-925f-e0a119f9997b","resolution":{"observed_at":"2026-08-11T17:02:24.797686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.748027Z","title":"A loopback network for explainable microvascular invasion classification,","venue":null,"work_id":"2faeb96c-3550-46af-a9f1-d3f6ed2f4e8e","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.223025Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:bb177184233f344ad52008ee2548b349f79700c539aa3600ad283ab2d8796a35","observation_id":"278b9c2c-9d05-4541-92cf-df2d3218df4a","resolution":{"observed_at":"2026-08-11T17:02:24.756887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.723721Z","title":"Scaling vision transformers to gigapixel images via hierar- chical self-supervised learning,","venue":null,"work_id":"8012bb5f-00c1-481d-add6-9ff6138f31be","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.231255Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:4adc6131461c38f7bfdcbda4b859603340c02a338e94717efe1f1aed2fa40bd8","observation_id":"c3f77198-6166-4652-927c-b3a07988d4b8","resolution":{"observed_at":"2026-08-11T17:02:24.730395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.692574Z","title":"Towards a general-purpose foundation model for computational pathology,","venue":null,"work_id":"15d4b652-3a1b-48b2-91d5-1c1e63d0fc3a","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.245446Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:12ac6de9281fa430202145e9bb510832d2e2b399058ed1fad4b074ae53963d25","observation_id":"75000f35-bd74-487a-a739-7c500f02af75","resolution":{"observed_at":"2026-08-11T17:02:24.700836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.667341Z","title":"A whole-slide foundation model for digital pathology from real-world data,","venue":null,"work_id":"612e4074-50e4-4de8-9ac0-699f3fb5aad3","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.255421Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:11e17188bb1617384835dd8002ddc15a056287c18d15881d77ad3c05497f8f03","observation_id":"bf14420d-b194-4a0e-96d6-24de6253523a","resolution":{"observed_at":"2026-08-11T17:02:24.675801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.636408Z","title":"A foundation model for clinical-grade computa- tional pathology and rare cancers detection,","venue":null,"work_id":"28b558f8-b387-4695-b7e4-a5053984adea","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.277099Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:b8b03b1150e45815d8c4f865ce2b919001b33eac30437a5eb437db80d4b2b3e8","observation_id":"ede887e8-3d1e-484e-962a-26b7405c363a","resolution":{"observed_at":"2026-08-11T17:02:24.650121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.607709Z","title":"A pathology foundation model for cancer diagnosis and prognosis prediction,","venue":null,"work_id":"78c001bf-f287-49bc-9920-cd0ffbb56e14","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.285499Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:19ced4741b3e48b13bcf422acaa3d93e41541352a5512824e9b68d22a7010036","observation_id":"94497e93-af58-4739-a1e3-73543347147a","resolution":{"observed_at":"2026-08-11T17:02:24.615335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T17:02:22.292744Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.292744Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:facbb7bc6109af93f272fb1af7a904335119ce047f14a5e866b37eb598b64887","observation_id":"56a02a1b-54e5-46d7-9622-2c2884de5865","resolution":{"observed_at":"2026-08-11T17:02:22.292744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T17:02:22.299191Z","title":"The llama 3 herd of models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.299191Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:57f9677039865d52e7b6602b27ab094f49f2300826f2eaa9c487ebe1498c8d41","observation_id":"dd32b1e1-e59e-489c-91d5-eb40e6a7960c","resolution":{"observed_at":"2026-08-11T17:02:22.299191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-11T17:02:22.309605Z","title":"Qwen2.5 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.309605Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:488e6c252ccfffb1927bb10ca6c242e3dddace76eefad6e9d5485d2fcba96c3c","observation_id":"0c0907d0-23d5-48d3-b78b-4c0dce9ccdeb","resolution":{"observed_at":"2026-08-11T17:02:22.309605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-13T15:58:13.809876Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-11T17:02:22.316402Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.316402Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:409e2734108e77015794730cd5a992be71d3f7af52ae1e3e3b8af09f4a8db92c","observation_id":"95541a36-5f7f-4507-b053-0bc7b9b2e037","resolution":{"observed_at":"2026-08-11T17:02:22.316402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.579660Z","title":"Improved baselines with visual instruction tuning,","venue":null,"work_id":"2c734dd3-227e-494d-a13d-e5a02e75014a","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.332434Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:e475a8185c78ac6a23c8dfdfe2cedb1a45e5b31ca5721a806520d6845988b3a0","observation_id":"510081e1-9b1e-45df-a423-50bf716fb48e","resolution":{"observed_at":"2026-08-11T17:02:24.589221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-11T17:02:22.339252Z","title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.339252Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:232ec1ed23cfc816d352ce012e01b53f2a3da08385f8135b8e56c17f80aaa68c","observation_id":"c2d96673-c5d4-4469-ac6b-e03afedbbe50","resolution":{"observed_at":"2026-08-11T17:02:22.339252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15195","last_updated":"2023-07-03T16:08:00Z","snapshot_observed_at":"2026-08-13T14:42:26.982679Z","submitted_at":"2023-06-27T04:31:52Z","title":"Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15195","snapshot_observed_at":"2026-08-11T17:02:22.345595Z","title":"Shikra: Unleashing multimodal llm’s referential dialogue magic,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.345595Z"},"links":{"cited_paper":"/paper/2306.15195","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:6881b58134adb22829af1b7881eeadc5d2b512beec117eb97e3d45c3d391e55e","observation_id":"45b918e3-e93d-46f7-a3c7-5120618dd5bb","resolution":{"observed_at":"2026-08-11T17:02:22.345595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.552648Z","title":"Lisa: Reasoning segmentation via large language model,","venue":null,"work_id":"dd86a0aa-c344-456d-b807-50d10650033e","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.353064Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:a9f25e18780ce5f3c070941c6239a7bf14cb84d37435335677ad2ad9c7e431e4","observation_id":"b80e359b-759d-44b8-8687-24346fa11ee6","resolution":{"observed_at":"2026-08-11T17:02:24.561002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.484065Z","title":"Pathmmu: A massive multimodal expert-level benchmark for understanding and reasoning in pathology,","venue":null,"work_id":"aac8db2a-c27b-4590-b024-4c1b27cfdbc9","year":2025},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.366722Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:bc9554aaf2f7ee435a250f6ef9c93dfeb2e8d1706b1cf10d76164b21d79a524b","observation_id":"e89ca2cf-81ad-45ba-8eb4-7647b41653f5","resolution":{"observed_at":"2026-08-11T17:02:24.494615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.426935Z","title":"A multimodal generative ai copilot for human pathology,","venue":null,"work_id":"bda128d5-605e-49ed-99f1-bc73d0ace287","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.381409Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:57ef5560bf218deec8969b9b193845f800813bf5b5823b7028f4f2679653ba90","observation_id":"f4edeaff-70ef-41dd-afd3-1869951272c6","resolution":{"observed_at":"2026-08-11T17:02:24.434874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19578","last_updated":"2024-06-27T23:43:36Z","snapshot_observed_at":"2026-08-12T23:33:13.377614Z","submitted_at":"2024-06-27T23:43:36Z","title":"PathAlign: A vision-language model for whole slide images in histopathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19578","snapshot_observed_at":"2026-08-11T17:02:22.386731Z","title":"Pathalign: A vision-language model for whole slide images in histopathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.386731Z"},"links":{"cited_paper":"/paper/2406.19578","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:e5eb3d4881d1fdcd09cd43759e350b6290ca559c8126a9cd52ffee9bf9a15b71","observation_id":"113a1878-3d93-414b-9b2c-0b2333d7d273","resolution":{"observed_at":"2026-08-11T17:02:22.386731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09530","last_updated":"2024-08-18T16:30:32Z","snapshot_observed_at":"2026-08-12T23:01:52.605760Z","submitted_at":"2024-08-18T16:30:32Z","title":"PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09530","snapshot_observed_at":"2026-08-11T17:02:22.395370Z","title":"Pa-llava: A large language-vision assistant for human pathology image understanding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.395370Z"},"links":{"cited_paper":"/paper/2408.09530","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:a198d876c13059a85be94a9eb7d2d389cd1fe347bac08cf6ef07e71eecb62e95","observation_id":"fab01df4-a7c4-4938-a156-cbd0cddb77c8","resolution":{"observed_at":"2026-08-11T17:02:22.395370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00203","last_updated":"2024-06-28T19:18:09Z","snapshot_observed_at":"2026-08-12T23:32:25.801821Z","submitted_at":"2024-06-28T19:18:09Z","title":"PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00203","snapshot_observed_at":"2026-08-11T17:02:22.404852Z","title":"Pathgen-1.6 m: 1.6 million pathology image-text pairs generation through multi-agent collaboration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.404852Z"},"links":{"cited_paper":"/paper/2407.00203","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:5f6f162e270a7cfb73793493c02f3efc972773e5d335f4ac8e11936154434d74","observation_id":"1b8f6c1f-a85e-4052-8aa6-8d859c3bc1e7","resolution":{"observed_at":"2026-08-11T17:02:22.404852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10254","last_updated":"2024-05-22T17:22:32Z","snapshot_observed_at":"2026-08-14T07:50:43.939149Z","submitted_at":"2024-05-16T16:59:12Z","title":"PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10254","snapshot_observed_at":"2026-08-11T17:02:22.412089Z","title":"Prism: A multi-modal generative foundation model for slide-level histopathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.412089Z"},"links":{"cited_paper":"/paper/2405.10254","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:96d3caf083284054363a7eabafca55b58a3bae68445c0df7a52cd2075abc4b63","observation_id":"8927fd73-c638-4be0-9a5e-fb91fcdc28ca","resolution":{"observed_at":"2026-08-11T17:02:22.412089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.402911Z","title":"Multiple instance captioning: Learning representations from histopathology textbooks and articles,","venue":null,"work_id":"0af3e1d2-f50d-44e9-ace3-cd2d5b5350b2","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.420064Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:a23de71aa760cd90e7439f1621964e1cdba53984ede88537db70ee4baf2ae5a4","observation_id":"fc2bc8f2-8b12-4257-8897-8ad6e6f162b6","resolution":{"observed_at":"2026-08-11T17:02:24.410676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.374529Z","title":"Wsi-vqa: Interpreting whole slide images by generative visual question answering,","venue":null,"work_id":"6480f8e4-caf7-4d35-a11e-f2f71306ad37","year":2025},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.437897Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:1497589808efeac28e9aa4d133401d977d57751a7328033531e5be01dd770410","observation_id":"a2001eed-28bc-4625-8830-3a5c0b4e03fe","resolution":{"observed_at":"2026-08-11T17:02:24.380253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.339426Z","title":"A visual–language foun- dation model for pathology image analysis using medical twitter,","venue":null,"work_id":"3d3014d3-d155-47dd-95e4-39d70faf626f","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.444771Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:eeb1ea9d002392dd39d4e88b402610299b6a3621fcf644cb70d007b58842d0b9","observation_id":"972a90bc-ddfe-4f65-b2d5-c620a3064200","resolution":{"observed_at":"2026-08-11T17:02:24.347378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.317932Z","title":"Quilt-1m: One million image-text pairs for histopathology,","venue":null,"work_id":"f9e39b38-08a7-4d0c-9615-84e39edd4c3d","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.452354Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:7dc6b40d9f86ea4f95a2ad066c76a0f993c8ed1705584adfb7e5f980b22ac022","observation_id":"3729abe6-f3fc-4874-af46-2fbe61fa427c","resolution":{"observed_at":"2026-08-11T17:02:24.323593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.298278Z","title":"Attention-based deep multiple instance learning,","venue":null,"work_id":"ec778b53-8663-4a23-8c45-97964ac07758","year":2018},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.460379Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:52e88d30949e06fcc7de65a779e251a543fc6f419adcd4bd0e0ea1760dccdd9b","observation_id":"1d005a73-7197-4320-9c18-aaefac81fb44","resolution":{"observed_at":"2026-08-11T17:02:24.304326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.279041Z","title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification,","venue":null,"work_id":"6ef0667e-a349-406b-af6d-142bce2b9a9f","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.476372Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:22741d26d00bc33c699afc6195acf6ab08ed715ffd756de7174f698d4d17af80","observation_id":"d339acec-81ba-40f5-a9f4-326eed152019","resolution":{"observed_at":"2026-08-11T17:02:24.285036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.256912Z","title":"Camel: A weakly supervised learning framework for histopathol- ogy image segmentation,","venue":null,"work_id":"3b61123a-4738-4c49-9c4b-09e502dc484d","year":2019},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.484985Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:86c8a28ecd1f88fa60dccca7e593b0b7256526a62368435d99baf1c48ba97b78","observation_id":"14327c20-ee6c-45d8-a375-e5a5a19b4da3","resolution":{"observed_at":"2026-08-11T17:02:24.262945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.236205Z","title":"Differentiable zooming for multiple instance learning on whole-slide images,","venue":null,"work_id":"b12fa888-82c7-4c09-99fe-62273387d2b8","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.496303Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:7235d05213c0535357025a680eb36d6c1db71ad015e92350268706dad9108f1d","observation_id":"0bac08d6-83d9-4066-bf62-f25a5addc6aa","resolution":{"observed_at":"2026-08-11T17:02:24.243369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07037","last_updated":"2024-08-13T17:05:06Z","snapshot_observed_at":"2026-08-12T23:04:08.967808Z","submitted_at":"2024-08-13T17:05:06Z","title":"PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology","version":1},"cited_work":{"arxiv_id":"2408.07037","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.07037","snapshot_observed_at":"2026-08-11T17:02:23.199545Z","title":"PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology","venue":"cs.CV","work_id":"dfaa01f4-f985-473e-9cf5-6877e4aa2b32","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.508910Z"},"links":{"cited_paper":"/paper/2408.07037","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:8b1dd9dca2620d96ea1f53ea83a6d969f790e4cc15e19902dbeec71d43ea8a2a","observation_id":"43e40cd5-b524-49d4-907f-d06bf8deb75a","resolution":{"observed_at":"2026-08-11T17:02:23.207075Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:22.519590Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.519590Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:5fedd4b950bffbd5f0b8e947bd56908c4f90dc7d5f627a46ae5b81e532c9bf14","observation_id":"9658d8e7-b283-4155-b97f-16ab9dd2f29b","resolution":{"observed_at":"2026-08-11T17:02:22.519590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.174573Z","title":"Learning transferable visual models from natu- ral language supervision,","venue":null,"work_id":"6ed89b1c-3ced-43be-860a-7a2262f73b83","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.530975Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:90c691e28a1399e6fb53c1c1cee5483b882a1c64bc14e4c9d3656f6797237b40","observation_id":"96defe81-3412-4d52-bdf2-e16c3e0eb53f","resolution":{"observed_at":"2026-08-11T17:02:24.204613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.155143Z","title":"Training language models to follow instructions with human feedback,","venue":null,"work_id":"b0bdde80-f31b-4e33-8ad0-364f6f358673","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.547415Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:936ef48f6a02881dbca377ce07263aeeecd3e95ce52dae4a3dd5ff1130be803c","observation_id":"9b569e5a-295f-49d0-a79a-82e2d42e4068","resolution":{"observed_at":"2026-08-11T17:02:24.161319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.128227Z","title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,","venue":null,"work_id":"2335b3ff-8526-43da-b460-30d30592c846","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.563944Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:879a2f154ca67e7f042473a0d39e87748d8906400b0988596dbb4f240f3f086e","observation_id":"47d74f6e-3736-43df-a0cc-db7de960a17f","resolution":{"observed_at":"2026-08-11T17:02:24.136026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-11T17:02:22.591694Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.591694Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:f099251fb3d7157fccb556fba5ddc446c209e85f7cee1055736dc0f96ca38e29","observation_id":"d5b83687-3f3f-4a0b-9569-a31f4ee07a21","resolution":{"observed_at":"2026-08-11T17:02:22.591694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.070326Z","title":"Sigmoid loss for language image pre- training,","venue":null,"work_id":"4487e715-e96f-47b5-9c55-8dd3f87aa74a","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.603007Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:1de880a0b7aa2fdbd5fd2c06c234f76e23a5e4251d88d3f79783139b0fff955d","observation_id":"523de67d-2db7-4887-a84c-22285706a938","resolution":{"observed_at":"2026-08-11T17:02:24.078417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.029492Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"d24bd270-b8f5-4f03-8f05-a8057b4fe1f0","year":2016},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.615880Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:be93416ccb28880b45db92e72b5855550fc23d8a2c39abecedd6e6b6290e8e5e","observation_id":"6fbf1ecf-92e1-456b-a472-a6209d3bb2e0","resolution":{"observed_at":"2026-08-11T17:02:24.041059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.999339Z","title":"Segment anything,","venue":null,"work_id":"b1856a19-a03c-4681-b5ea-26fc1c9dff41","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.625565Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:234c5a46b256fa5b558155ff3dab1942d6408d997f1e143ee91551bfd609114b","observation_id":"703ded8a-c120-45a0-8fd6-bc71ce895d07","resolution":{"observed_at":"2026-08-11T17:02:24.008849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.966983Z","title":"Integrative analysis of histological textures and lymphocyte infiltration in renal cell carcinoma using deep learning,","venue":null,"work_id":"1c966bb0-03f6-49df-9579-9c863589f066","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.636098Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:da88a4fff59e79a6dea303605513ec0d11b3d8da9de220291d510136e480c0ca","observation_id":"540a0554-2ca7-458a-ad88-f0e939d45fed","resolution":{"observed_at":"2026-08-11T17:02:23.980041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.929212Z","title":"A petri dish for histopathology image analysis,","venue":null,"work_id":"6f9aa7da-b6bb-418c-9483-2dc810c0434a","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.654815Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:291acd54965cc82e0247b81ebbf3e16b9501e8ec1ffb4ace13db0411aa8a83ec","observation_id":"be340a1f-47d3-4dcc-9de2-f3144be33b7b","resolution":{"observed_at":"2026-08-11T17:02:23.938594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:22.661257Z","title":"A method for normalizing histology slides for quantitative analysis,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.661257Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:ca875adc3e1fb1fb14c94459488a5472748beb345b0b23986311982a5b4a3f86","observation_id":"336d9364-0b1d-4fa6-841e-e40a6fafc691","resolution":{"observed_at":"2026-08-11T17:02:22.661257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.887838Z","title":"Artificial intelligence for diagnosis and gleason grading of prostate cancer: The panda challenge,","venue":null,"work_id":"b9343ae5-7735-406c-87b6-58fde19672d4","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.671652Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:1bdc29571c6e39c304f004a6e1795171d7b5c3e9fb92a3e380207fd610e75cdc","observation_id":"ad27ab90-d956-4304-a060-22fba89ead89","resolution":{"observed_at":"2026-08-11T17:02:23.904222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.856521Z","title":"Pathologist- level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks,","venue":null,"work_id":"fc74345c-40c1-4e9d-89f6-8a78426760d7","year":2019},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.679036Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:216db76b663458566ab55f4d7542741f85647d88bb288cb03fd491323a602d1b","observation_id":"aca5d412-4a06-4476-92c4-dedd6272467a","resolution":{"observed_at":"2026-08-11T17:02:23.865061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.823527Z","title":"1399 h&e-stained sentinel lymph node sections of breast cancer patients: The camelyon dataset,","venue":null,"work_id":"0ac8a4dd-967a-45f1-b979-4a848f00596d","year":2018},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.689630Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:f1a7e47f4761ce3916ddc56d81cddcc14f8d40050f2bc4af1af8ab32b3893ff3","observation_id":"ffeb5248-e6e2-45ae-a18f-162ab81841fc","resolution":{"observed_at":"2026-08-11T17:02:23.829796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.104719Z","title":"Llava-med: Training a large language-and-vision assistant for biomedicine in one day,","venue":null,"work_id":"4ed1efe6-fe82-4d63-bb6e-b3655bd8d4d3","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.736364Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:996598bd76df49e386c6cdc6bc4abb631890dd2c43b2183e63e72303157bbd9f","observation_id":"bc55f360-03bc-4210-b685-ba538a651e7a","resolution":{"observed_at":"2026-08-11T17:02:24.111020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.456244Z","title":"Quilt-llava: Visual instruction tuning by extracting localized narratives from open-source histopathology videos,","venue":null,"work_id":"baaa2068-a774-4b95-90cc-3a2074999b64","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.745304Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:9656d36f1e025a0b7003b06373e96e4db8fe706c6877185af8910622b35133e0","observation_id":"7de88dfc-abaf-4a95-ac84-4ae1e95bf2bc","resolution":{"observed_at":"2026-08-11T17:02:24.463967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.739835Z","title":"Jocher and J","venue":null,"work_id":"f411dee1-cd91-41a8-abdc-e92cd74f87b6","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.754156Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:4e8369e870f6a809099300be608a12602eee863f45a5a1f671cf0463cfdb9954","observation_id":"d29c2615-92d9-4f2c-bac7-a1747951cc67","resolution":{"observed_at":"2026-08-11T17:02:23.751375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.713734Z","title":"Nnu-net: A self- configuring method for deep learning-based biomedical image segmentation,","venue":null,"work_id":"5bd350cd-83a9-453b-ac51-36669ad8589d","year":2021},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.767828Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:c5b4dce60f2b53ca0ca68ea7b372264738f9056a06ff51ff1b0d59772852b7fe","observation_id":"ce0750ff-05ce-49f5-811e-a6d5f1fe07cb","resolution":{"observed_at":"2026-08-11T17:02:23.722237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.799412Z","title":"A multi-organ nucleus segmentation challenge,","venue":null,"work_id":"ab60674c-e250-475a-85e6-d5421d18e17e","year":2019},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.779581Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:6f24c0ba5f51af47fde3e1ee3a8ab71fe002a64c398340893a021520ce45ddcf","observation_id":"9ba6823c-f4e5-4fc2-9583-5f190e4396b3","resolution":{"observed_at":"2026-08-11T17:02:23.805647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.688382Z","title":"Conic challenge: Pushing the frontiers of nu- clear detection, segmentation, classification and counting,","venue":null,"work_id":"12a7a1f0-cccb-4773-84b1-d3e3c50d455a","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.786318Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:d7e3190c17df5f77c022010d953b0550d6d0c60dc064beb1cb400c9e2b8cf00f","observation_id":"f29d7be6-b4a8-4032-96c5-e74e30367b7d","resolution":{"observed_at":"2026-08-11T17:02:23.695571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.2579118","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:22.964379Z","title":null,"venue":null,"work_id":"59727bb2-81a0-44f4-b347-96e7a5569832","year":2019},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.799686Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:1696494441af48fe932e49a6be38f88068d8ab232dc8ac6f34c56fda3312e3ba","observation_id":"76938bee-cd3b-42cf-9ca2-22734818130b","resolution":{"observed_at":"2026-08-11T17:02:22.979327Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.778841Z","title":"Nucls: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer,","venue":null,"work_id":"a703dc59-72f7-4ab2-84ab-2b502388767e","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.805587Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:5462eaf4b2f77dbb3f82523a6f6964a2a3b8f6f4002ad9828c5d80f9756973f4","observation_id":"c51a10e8-c278-444a-9f03-3c82ba8240eb","resolution":{"observed_at":"2026-08-11T17:02:23.785728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10778","last_updated":"2020-04-22T08:52:04Z","snapshot_observed_at":"2026-07-06T09:06:56.021993Z","submitted_at":"2020-03-24T11:25:12Z","title":"PanNuke Dataset Extension, Insights and Baselines","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10778","snapshot_observed_at":"2026-08-11T17:02:22.812731Z","title":"Pannuke dataset extension, insights and baselines,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.812731Z"},"links":{"cited_paper":"/paper/2003.10778","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:4ffd412efb5a928f2347d237f1e94efe45208600a867ec5620137f70be0420d5","observation_id":"c2bdbc17-f18a-4172-a36b-f1ec5ef3926b","resolution":{"observed_at":"2026-08-11T17:02:22.812731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.655311Z","title":"Nuinsseg: A fully annotated dataset for nuclei instance segmentation in h&e-stained histological images,","venue":null,"work_id":"a0ce8494-4bda-4487-9329-6f376ba2c88a","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.823960Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:feb11fa93ffe6c1870386851f40469180b08112c98d5b0eee31177f206a0ad68","observation_id":"cb8521c8-68c1-4f6f-b4d4-2df074059ce8","resolution":{"observed_at":"2026-08-11T17:02:23.666276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.628154Z","title":"Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: A retrospective algorithm development and validation study,","venue":null,"work_id":"85c0eda9-1623-4d8f-87a7-f53e7092ad10","year":2023},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.831303Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:5d92fe139206f7c7108c4428b352f3e17ba9577c551b1b68efc710dc421fd972","observation_id":"1264d6fd-5d98-4795-8d08-de4eca02157b","resolution":{"observed_at":"2026-08-11T17:02:23.638185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.603616Z","title":"Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer,","venue":null,"work_id":"b55ddf82-586c-4037-9715-f7f5189a8786","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.838655Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:a0f66944c1e39e039590ff7f8391b01096eb4a4f89f694a984849934a52a3cc5","observation_id":"da13e288-1b2d-45d1-887e-3ba423af97b0","resolution":{"observed_at":"2026-08-11T17:02:23.612279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.3832231","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:22.931970Z","title":null,"venue":null,"work_id":"ec844faf-60c5-49e3-a7eb-8db961dadc62","year":2020},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.846406Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:26839e78bdce8b8298447f1872b1eb0f967d817c3b0cc711ade14e5e101ec60d","observation_id":"54120600-4ca3-4791-b22c-3cbbb765bf10","resolution":{"observed_at":"2026-08-11T17:02:22.944185Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:23.582014Z","title":"Universal encoding of pan-cancer histology by deep texture representations,","venue":null,"work_id":"e6a04664-a999-4690-943d-11df04cbf912","year":2022},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.854677Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:f2952a0c796ad900e3171a49d7815a7ff14a272aa2daca0f411cec94882d65ed","observation_id":"60194f76-27fc-493b-99f5-afb6dacbdf1d","resolution":{"observed_at":"2026-08-11T17:02:23.588501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:02:24.514263Z","title":"Pathasst: A generative foundation ai assistant towards artificial general intelligence of pathology,","venue":null,"work_id":"4e1da7a5-2780-4e69-9614-cef097f8c7bd","year":2024},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.862225Z"},"links":{"citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:2e5c02159355c9ffc3173bf4d965d8999ae8336549c56b13cbc21f90bac59136","observation_id":"f02330c9-cf88-46a8-917d-e066dfc85b03","resolution":{"observed_at":"2026-08-11T17:02:24.523857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10286","last_updated":"2020-03-07T17:55:41Z","snapshot_observed_at":"2026-07-06T09:06:42.071993Z","submitted_at":"2020-03-07T17:55:41Z","title":"PathVQA: 30000+ Questions for Medical Visual Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10286","snapshot_observed_at":"2026-08-11T17:02:22.867266Z","title":"Pathvqa: 30000+ questions for medical visual question answering,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T17:02:22.867266Z"},"links":{"cited_paper":"/paper/2003.10286","citing_paper":"/paper/2412.09521"},"observation_digest":"sha256:4bbdd3d71f9c43147061796b7384103150c48a09c8c98f877aae50156d0df244","observation_id":"894fdf4d-eb97-4e67-8f56-7c3d5b83bf74","resolution":{"observed_at":"2026-08-11T17:02:22.867266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.09521","last_updated":"2025-05-16T10:17:33Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T07:51:00.877100Z","submitted_at":"2024-12-12T18:07:23Z","title":"Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis"},"reference_resolution":{"displayed":61,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":3,"verified_fuzzy":43},"total_outbound_references":61},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2412.09521."}