Paper Citation Record · LEDGER

Multimodal Deep Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2301.04856.

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

pith.paper-citation-record.v1
2301.04856 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:41:28.462937Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

9
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 10f072bb-9a43-4c40-a0c9-ca3056f7feb9 · inbound

Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services cites this paper.

Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services Multimodal Deep Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:06:53.263889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0867f3ca-0589-4565-8da8-7dfd6f62c1f0 · inbound

NanoVLMs: How small can we go and still make coherent Vision Language Models? cites this paper.

NanoVLMs: How small can we go and still make coherent Vision Language Models? Multimodal Deep Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T13:35:05.039509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b39cd44b-0db2-4b61-a67d-2cf7d9d43855 · inbound

Multilingual Vision-Language Models, A Survey cites this paper.

Multilingual Vision-Language Models, A Survey Multimodal Deep Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:02:37.329801Z

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.

Observation a11ee470-c5d9-4beb-87c5-224c99a5d9e5 · inbound

Information Router for Mitigating Modality Dominance in Vision-Language Models cites this paper.

Information Router for Mitigating Modality Dominance in Vision-Language Models Multimodal Deep Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:43:01.143127Z

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.

Observation f2d1802b-d78c-40e6-8b33-08f265c50c47 · inbound

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data cites this paper.

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data Multimodal Deep Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:41:28.462937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.