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Paper Citation Record · LEDGER

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2407.18449.

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

pith.paper-citation-record.v1
2407.18449 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:01:09.613634Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T05:12:05.117643Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c5245d8f-041a-428d-b687-deb579ddc2df · inbound

FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification cites this paper.

FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-12T15:01:09.613634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:01:09.613634Z digest=sha256:97c1b6db2cf565eeb9d33471eb5be966f4cf7acad2383647ed3bfb4ccc9d39ec

Observation d80a72b9-99af-4cc9-a59a-4f94b4f8c065 · inbound

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks cites this paper.

A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-10T14:02:28.651907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:02:28.651907Z digest=sha256:67402805d0b94f85a29c88ce5443a0102bb13ad0ea76e6ba0e631378b4a91785

Observation 58d29b4e-482d-49e5-92c3-e0677e81fb27 · inbound

Distilling foundation models for robust and efficient models in digital pathology cites this paper.

Distilling foundation models for robust and efficient models in digital pathology Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T13:40:24.361769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:40:24.361769Z digest=sha256:476a0e9d6677efa6fef7fbf539aad6a2f949461f41d8323266dbac7d012913ca

Observation 8a23b808-3c98-4700-83b7-ea50c1dd0c51 · inbound

Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation cites this paper.

Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-08T05:54:57.597378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:54:57.597378Z digest=sha256:7b060a350402ea11700100f9491da748a640c441a134d9897d9d210e916f5fdc

Observation 66eeabb7-a71b-4f89-82a0-afebed8733c6 · inbound

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning cites this paper.

Discovering Pathology Rationale and Token Allocation for Efficient Multimodal Pathology Reasoning Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T15:16:20.032641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:16:20.032641Z digest=sha256:52599ee46a41ed0e079e4e6fa9773d6687a5d18eb7db51d7fd2b3cc0df1d8e2a

Observation 2cdd0f00-a40c-4745-8725-6f2303ba184c · inbound

Large-scale Self-supervised Video Foundation Model for Intelligent Surgery cites this paper.

Large-scale Self-supervised Video Foundation Model for Intelligent Surgery Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T11:22:59.524728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:22:59.524728Z digest=sha256:5e53ddf980553a2006dcf3306ebf5d4cb46dc5040c12a5d2ac1d1d5e38f0b67c

Observation 4cda2c33-f601-48c1-a6fb-2ee3d9c5a091 · inbound

Segment Anything in Pathology Images with Natural Language cites this paper.

Segment Anything in Pathology Images with Natural Language Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T22:42:11.695856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:42:11.695856Z digest=sha256:07b2ba3de3ef1be4a736a8ab3df6ff3ba2e899fd893fc22c77c506c2ef96981d

Observation d443be83-406b-49ec-8052-b02a05f65205 · inbound

Emerging AI Approaches for Cancer Spatial Omics cites this paper.

Emerging AI Approaches for Cancer Spatial Omics Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T21:32:56.811142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:56.811142Z digest=sha256:dca88a3a812d84688ada8c16038bf8d7ee8b6eaaf95b77d216af0f27964f1236

Observation e93aae72-69b8-4de6-877e-fd9d0de65fdb · inbound

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research cites this paper.

From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 157

Resolution
verified exact
arxiv_id, observed 2026-05-19T05:12:05.119327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T05:10:23.963494Z digest=sha256:c3deaafe28c33151faaa06973c3eaad65d6c8cd40bf1df1121dd216bb20671e4

Observation cf06b662-94da-48ea-8019-810fbc4f1056 · inbound

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping cites this paper.

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T17:45:05.490246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:45:05.490246Z digest=sha256:a9299599f405041017263517f84aaf49962a4854c70c940d18758f507c8e1efc