Paper Citation Record · LEDGER

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models

As of 21 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2608.06659.

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

pith.paper-citation-record.v1
2608.06659 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:07:39.590285Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

12 of 12 outbound references displayed

verified exact1
verified fuzzy4
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0285e187-165f-47d5-a691-69ee3e4b9b6a · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.538678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03530221-0bb3-4fdd-b9cc-9b5b8b616352 · outbound

This paper cites When Does LeJEPA Learn a World Model?.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models When Does LeJEPA Learn a World Model?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.548880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2bbc4fa-b9ce-4c7d-b9b8-94e21e41453e · outbound

This paper cites Madhu, H.; Rocha, J.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models Madhu, H.; Rocha, J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:07:39.876944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c5f5c89-9fe9-4489-82cb-ba5415304825 · outbound

This paper cites HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.559252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63d7cdf4-9425-4d17-9b79-259028a9ec7b · outbound

This paper cites V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.564364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 148be9ba-6adf-47c9-b2c4-08fc4509c53b · outbound

This paper cites biorxiv, 2025–02.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models biorxiv, 2025–02

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:07:39.844328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a5b5d587-4bdd-47ee-8fe9-aa19b1965e97 · outbound

This paper cites Zhao, S.; Luo, Y.; Yang, G.; Zhong, Y.; Zhou, H.; and Nie, Z.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models Zhao, S.; Luo, Y.; Yang, G.; Zhong, Y.; Zhou, H.; and Nie, Z

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:07:39.827778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 44327ee3-36de-4fb7-838d-25e10e7f0cee · outbound

This paper cites SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:07:39.634399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7cff356b-3d6c-462d-8cd6-872ba1fd636d · outbound

This paper cites Science, 353(6294): 78–82.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models Science, 353(6294): 78–82

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:07:39.860608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ebf9d24-ab6f-47ec-8549-07def4220fee · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.569433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24025f7b-89dd-445b-b64f-e2e26e9719e2 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.532102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76eaed83-46db-49e9-a204-501a1c0cd3ed · outbound

This paper cites Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P.; Buchatskaya, E.; Doersch, C.; Avila Pires, B.; Guo, Z.; GheshlaghiAzar,M.;etal.2020.

CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P.; Buchatskaya, E.; Doersch, C.; Avila Pires, B.; Guo, Z.; GheshlaghiAzar,M.;etal.2020

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-10T23:07:39.543790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.