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Paper Citation Record · LEDGER

Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2209.03430.

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

pith.paper-citation-record.v1
2209.03430 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:40.521348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:26:00.069612Z

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 a18a3558-b526-4404-a362-657ea7b69717 · inbound

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer cites this paper.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T04:09:40.521348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:09:40.521348Z digest=sha256:3d4ddab691a832edf760433095d27bfc3684c8332b948b5b40c20e1179d92b20

Observation 918fadd6-df16-4a8c-b941-34f395c02c22 · inbound

Differential Attention for Multimodal Crisis Event Analysis cites this paper.

Differential Attention for Multimodal Crisis Event Analysis Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T19:34:16.768042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:34:16.768042Z digest=sha256:c013b495f27c1165c408dcb982a8d054102da0effc961a48b9cc28a7ad2aa59d

Observation 3c2809ff-fb03-4e27-9a7d-41830bbb6b52 · inbound

Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Making cites this paper.

Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Making Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T19:01:55.549050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:01:55.549050Z digest=sha256:b71abab067c75734683c2451349eba9d0e70eb6b84ad7d12aeca8b67832feb45

Observation 0c764899-eae0-4be5-b10b-2e5574ceb97f · inbound

A CLIP-based Uncertainty Modal Modeling (UMM) Framework for Pedestrian Re-Identification in Autonomous Driving cites this paper.

A CLIP-based Uncertainty Modal Modeling (UMM) Framework for Pedestrian Re-Identification in Autonomous Driving Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T20:08:07.326992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:08:07.326992Z digest=sha256:16ba9c7f149c7e743d812975d6fd2fb3d313d0bd30cccc22c2b0be2c6a99ac83

Observation 68d2d7b4-9316-418a-aac2-6439b461a975 · inbound

Purify-then-Align: Towards Robust Human Sensing under Modality Missing with Knowledge Distillation from Noisy Multimodal Teacher cites this paper.

Purify-then-Align: Towards Robust Human Sensing under Modality Missing with Knowledge Distillation from Noisy Multimodal Teacher Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:00:47.599567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T19:26:34.496169Z digest=sha256:7b260c48a36721ae42d7d31d7177269d5067ade7a2dbc734d101cf7ffce79a76

Observation 53c37751-5ed7-4bd8-9da6-7b611c3ac983 · inbound

Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site cites this paper.

Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:20:57.057403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T17:40:56.444027Z digest=sha256:a30fc0e488f0973dfc868a315cb799519bc889b2df67a86698eac01c3ace5ed3

Observation be16b90d-0863-47b3-9d5f-93431e462842 · inbound

Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site cites this paper.

Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-02T16:44:58.453175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:44:58.453175Z digest=sha256:3d8cfcba37058c127b877acb3e16028d78ddc13cd015ca229ff5773d673ba056

Observation 0b342c2a-04a5-408f-90d6-0d75c54a7cf6 · inbound

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations cites this paper.

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-10T10:24:21.458662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T10:21:36.624663Z digest=sha256:1abd8fb49adb29a57a2ee776398c9ea7bbe5980b9965b4c5dedf3ea64c404839

Observation 58e69b21-bf48-463d-908f-2776d478e5a8 · inbound

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations cites this paper.

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-02T16:15:50.183197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:15:50.183197Z digest=sha256:11767c72418c4e3464051e0ad0986d837a18cbb6131d5e69ed880e60d47090b0

Observation 57484a76-3e6a-4516-9b45-447bbb54e276 · inbound

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models cites this paper.

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:26:00.071019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T22:43:33.929871Z digest=sha256:931fc6e44c6ebf7919d5af638c87355aa03045a69d11036d5943230ab252245d

Observation 36f4398e-f047-4103-a7e2-c90a4afd9e42 · inbound

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention cites this paper.

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-01T13:33:14.272789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:33:14.272789Z digest=sha256:bab86095eea6c52ff6efd3c551d961cf8159758a2e53794c03237b0954a8671a