Paper Citation Record · LEDGER

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.25497.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.25497 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:19:58.338496Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c310b348-00b1-4ab4-aaae-ff53cdb4bde2 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models On the Opportunities and Risks of Foundation Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.218424Z

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Observation f3da3282-26a2-4549-8e35-7a5d6db3beab · outbound

This paper cites Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.Nature Medicine, 28(1):154–163, 2022.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.Nature Medicine, 28(1):154–163, 2022

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.237932Z

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Observation b12c96d4-2424-478c-a245-e4846aa14a4e · outbound

This paper cites A clinical benchmark of public self-supervised pathology foundation models.Nature Communications, 16(1):3640, 2025.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A clinical benchmark of public self-supervised pathology foundation models.Nature Communications, 16(1):3640, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.241374Z

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Observation 5ac2ed92-43db-4133-9b0d-4dc8b2d44494 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Emerging Properties in Self-Supervised Vision Transformers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.244652Z

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Observation 2edcd20e-f093-4ceb-a3e9-b508c90c1597 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology.Nat.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Towards a general-purpose foundation model for computational pathology.Nat

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.248162Z

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Observation 622751fa-50ee-4096-8b49-92e2c782b647 · outbound

This paper cites Current Pathology Foundation Models are unrobust to Medical Center Differences.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Current Pathology Foundation Models are unrobust to Medical Center Differences

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.251322Z

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Observation c5dabece-e315-4776-8c7e-db2e977fbf0e · outbound

This paper cites Biased data, biased AI: deep networks predict the acquisition site of TCGA images.Diagn.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Biased data, biased AI: deep networks predict the acquisition site of TCGA images.Diagn

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.254822Z

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Observation 00457883-66ac-4236-91be-79670ed8bbcf · outbound

This paper cites Wagner, Andrew H.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Wagner, Andrew H

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.257752Z

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Observation a333b67a-aba4-41b0-a84b-4e475a405866 · outbound

This paper cites Distill- ing foundation models for robust and efficient models in digital pathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Distill- ing foundation models for robust and efficient models in digital pathology

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.260552Z

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Observation 1ca1a292-31c9-42e3-8a5d-e51b46dae1fa · outbound

This paper cites Scaling self-supervised learning for histopathol- ogy with masked image modeling.medRxiv, 2023.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Scaling self-supervised learning for histopathol- ogy with masked image modeling.medRxiv, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.263327Z

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Observation c95a2769-f24c-495a-8bca-5da2b1acb191 · outbound

This paper cites Phikon-v2, A large and public feature extractor for biomarker prediction.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.266536Z

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Observation 9172f18c-31b5-4122-b902-3e206e43898f · outbound

This paper cites Shortcut learning in deep neural networks.Nat.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Shortcut learning in deep neural networks.Nat

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.269954Z

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Observation a3a7023e-bbdd-4fed-90d9-9f99a3b09741 · outbound

This paper cites Deep learning from routine histology improves risk stratification for biochemical re- currence in prostate cancer, 2026.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Deep learning from routine histology improves risk stratification for biochemical re- currence in prostate cancer, 2026

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.273314Z

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Observation 50aeda5f-a095-4b41-b9c2-97d0538d5b21 · outbound

This paper cites The impact of site-specific digital histology signatures on deep learning model accuracy and bias.Nat.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models The impact of site-specific digital histology signatures on deep learning model accuracy and bias.Nat

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.276015Z

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Observation 77c57b6c-a100-4cc0-940d-797fb6909363 · outbound

This paper cites Self-Supervised Visual Feature Learning With Deep Neu- ral Networks: A Survey .IEEE Transactions on Pattern Analysis & Machine Intelligence, 43(11):4037–4058, November 2021.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Self-Supervised Visual Feature Learning With Deep Neu- ral Networks: A Survey .IEEE Transactions on Pattern Analysis & Machine Intelligence, 43(11):4037–4058, November 2021

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.278777Z

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Observation 1e9ddaa8-957b-478b-83ae-1abf29c2d211 · outbound

This paper cites an unresolved cited work.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.281483Z

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Observation 744c3c9c-e6f3-44d1-bdaa-2a5f6652b56e · outbound

This paper cites Training state-of-the-art pathology foundation models with orders of magnitude less data.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Training state-of-the-art pathology foundation models with orders of magnitude less data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.284159Z

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Observation 9e677715-2603-4c63-b4a0-0dd6ce8ea80c · outbound

This paper cites MOOZY: A Patient-First Foundation Model for Computational Pathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models MOOZY: A Patient-First Foundation Model for Computational Pathology

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.287663Z

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Observation 5f78a882-7163-45d4-83a3-095ed2522f07 · outbound

This paper cites Towards Robust Foundation Models for Digital Pathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Towards Robust Foundation Models for Digital Pathology

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.290831Z

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Observation 1816a462-06ad-4340-83a5-763dff6b2db1 · outbound

This paper cites Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.294733Z

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Observation d8928f50-0daa-4149-bb54-1b67a61232f2 · outbound

This paper cites Lu, Bowen Chen, Drew F.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Lu, Bowen Chen, Drew F

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.297872Z

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Observation f5d43742-2ddb-4e7d-a9ab-3b02a4ad1fff · outbound

This paper cites A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature Biomedical Engineering, 10(3):545–564, 2026.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature Biomedical Engineering, 10(3):545–564, 2026

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.300773Z

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Observation f722d43c-f563-41f3-94fa-32cef17098e0 · outbound

This paper cites A benchmarking crisis in biomedical machine learning.Nature Medicine, 31(4):1060, April 2025.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A benchmarking crisis in biomedical machine learning.Nature Medicine, 31(4):1060, April 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.303782Z

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Observation aec642e5-bfed-4266-b598-8afdbff1839e · outbound

This paper cites MahmoodLab/UNI2-h.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models MahmoodLab/UNI2-h

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.306495Z

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Observation fa18bd6d-d27e-41ac-824e-4cf83c0b7b3c · outbound

This paper cites Hibou: A Family of Foundational Vision Transformers for Pathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Hibou: A Family of Foundational Vision Transformers for Pathology

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.309449Z

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Observation d6c97bc8-924f-4bf0-8f19-095b4f3215be · outbound

This paper cites Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nat.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nat

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.312259Z

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Observation ec10f019-1dac-4c74-854c-8a42a6d4862e · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.314970Z

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Observation 28e89bda-f9a0-492b-b2b1-ec2485528581 · outbound

This paper cites H-optimus-0.https://github.com/bioptimus/releases/ tree/main/models/h-optimus/v0, 2024.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models H-optimus-0.https://github.com/bioptimus/releases/ tree/main/models/h-optimus/v0, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.318459Z

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Observation 86175b6b-83e3-43a9-bd33-d9914c202f42 · outbound

This paper cites Mariet, and Rodolphe Jenatton.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Mariet, and Rodolphe Jenatton

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.321325Z

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Observation a882806a-0791-44b9-a05f-e3b822301859 · outbound

This paper cites PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.324062Z

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Observation 3b826fa3-921a-4843-870b-88d8ce99ce79 · outbound

This paper cites A foundation model for clinical-grade computational pathology and rare cancers detection.Nat.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A foundation model for clinical-grade computational pathology and rare cancers detection.Nat

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.327197Z

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Observation e51a52d5-3ba1-4eca-a3a0-20ccd9ae6c6b · outbound

This paper cites Nirschl, Joel Neal, Maximilian Diehn, Sen Yang, and Rui- jiang Li.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Nirschl, Joel Neal, Maximilian Diehn, Sen Yang, and Rui- jiang Li

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.329888Z

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Observation 998831ca-b8cb-4865-96ba-b1fe8497811f · outbound

This paper cites A whole-slide foundation model for digital pathology from real-world data.Nature, 630(8015):181–188, June 2024.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A whole-slide foundation model for digital pathology from real-world data.Nature, 630(8015):181–188, June 2024

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.332879Z

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Observation 05279c4c-b867-465d-9247-9ab8b8f399c6 · outbound

This paper cites A multi- modal knowledge-enhanced whole-slide pathology foundation model.Nature Communications, 16(1):11406, 2025.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models A multi- modal knowledge-enhanced whole-slide pathology foundation model.Nature Communications, 16(1):11406, 2025

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T02:19:58.335644Z

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Observation 400efdc6-f583-4d5d-a7f3-5d50c4150ef1 · outbound

This paper cites Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology.

Beyond Counts: A Distributional Robustness Margin For Pathology Foundation Models Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 35

Resolution: malformed identifier; no resolver link, observed 2026-08-01T02:19:58.338496Z

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Pith citing papers

No inbound Pith citation observations are available.