Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning biologically relevant features in a pathology foundation model using sparse autoencoders
As of 31 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.10785.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-31T06:34:12.847434+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-10T15:09:29.672351Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T11:11:06.202518Z
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 fd3d75c6-ad10-4d96-b7f0-ed87d15344f9 · inbound
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models cites this paper.
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:06:05.076945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.
Observation b32ce3f1-6458-4fc8-9f6d-e9e1d46c77b8 · inbound
Sparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction cites this paper.
Sparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:11:06.207341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.