Paper Citation Record · LEDGER

Vector embedding of multi-modal texts: a tool for discovery?

As of 21 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2509.08216.

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

pith.paper-citation-record.v1
2509.08216 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:05:25.636429Z

measured 18 of 18 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T16:24:25.357338Z

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

17 of 17 outbound references displayed

verified exact1
verified fuzzy9
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 4dbf5bd8-c466-4761-b61c-fe300ccd6ebc · outbound

This paper cites Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design.

Vector embedding of multi-modal texts: a tool for discovery? Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:24.752409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b36cbc9-d9d0-4cac-8112-20416ff42d15 · outbound

This paper cites DocFormer: End-to-End Transformer for Document Understanding.

Vector embedding of multi-modal texts: a tool for discovery? DocFormer: End-to-End Transformer for Document Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:24.808270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d53cd979-2e3c-48af-a2eb-3d9cfd22d679 · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

Vector embedding of multi-modal texts: a tool for discovery? PaliGemma: A versatile 3B VLM for transfer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:24.843007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7706ddaa-e3ac-47aa-83ca-31637c08b17a · outbound

This paper cites Imagined visual representations as multimodal embeddings.Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), Feb.

Vector embedding of multi-modal texts: a tool for discovery? Imagined visual representations as multimodal embeddings.Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), Feb

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:27.872591Z

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 f235d7e2-b339-4ea0-a288-1ff315448f28 · outbound

This paper cites Introduction to and hands-on use cases with hathitrust research center’s extracted features 2.0 dataset.

Vector embedding of multi-modal texts: a tool for discovery? Introduction to and hands-on use cases with hathitrust research center’s extracted features 2.0 dataset

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:27.585005Z

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 cb28f645-d87b-4f65-93d9-0076f3ced7db · outbound

This paper cites ColPali: Efficient Document Retrieval with Vision Language Models.

Vector embedding of multi-modal texts: a tool for discovery? ColPali: Efficient Document Retrieval with Vision Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:25.027563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 575e62ab-ff59-4c99-bb81-616423c67854 · outbound

This paper cites Colpali: Efficient document retrieval with vision language models.

Vector embedding of multi-modal texts: a tool for discovery? Colpali: Efficient document retrieval with vision language models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:27.353117Z

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 1b37765a-afbd-4f95-814a-a82a19028506 · outbound

This paper cites LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.

Vector embedding of multi-modal texts: a tool for discovery? LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:25.170516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c81d5d-fffa-410f-93d8-425b97216ba5 · outbound

This paper cites Copyright violations and large language models.

Vector embedding of multi-modal texts: a tool for discovery? Copyright violations and large language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:27.170753Z

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 132a63ca-f9cf-455d-8695-338178b254ba · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.

Vector embedding of multi-modal texts: a tool for discovery? ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:25.292998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1302fd27-70f3-4ab6-a05b-d27359b95212 · outbound

This paper cites Plale and S.

Vector embedding of multi-modal texts: a tool for discovery? Plale and S

Reference 12

Resolution: verified exact; doi, observed 2026-08-04T21:05:25.787472Z

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 5dd96a60-4d0b-4eb7-8645-86503f4f0bf2 · outbound

This paper cites Plale, S.

Vector embedding of multi-modal texts: a tool for discovery? Plale, S

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:26.968920Z

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 9a21c1fb-6556-478b-894f-761b290d01dd · outbound

This paper cites Vector database, 2021.

Vector embedding of multi-modal texts: a tool for discovery? Vector database, 2021

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:26.779205Z

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 ae70d77e-c0d7-4ffe-a40b-49d0a588d36e · outbound

This paper cites Robertson, Steve Walker, Susan Jones, Micheline Hancock- Beaulieu, and Mike Gatford.

Vector embedding of multi-modal texts: a tool for discovery? Robertson, Steve Walker, Susan Jones, Micheline Hancock- Beaulieu, and Mike Gatford

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:26.525732Z

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 fa28f383-9906-47a1-9a35-b21741d4320c · outbound

This paper cites A Statistical Interpretation of Term Specificity and its Application in Retrieval.Journal of Documentation, 28(1):11–21, 1972.

Vector embedding of multi-modal texts: a tool for discovery? A Statistical Interpretation of Term Specificity and its Application in Retrieval.Journal of Documentation, 28(1):11–21, 1972

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:26.264741Z

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 cb18f6db-2825-445a-91d8-be020b8e52f9 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Vector embedding of multi-modal texts: a tool for discovery? Gemma: Open Models Based on Gemini Research and Technology

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T21:05:25.567704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56884bb7-a0a1-4e48-9507-eee0defc2dc2 · outbound

This paper cites Wilkinson, M.

Vector embedding of multi-modal texts: a tool for discovery? Wilkinson, M

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:05:26.104162Z

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.

Pith citing papers

Observation a3c14df4-9b6d-4524-9690-b1115ae936f9 · inbound

Qiskit Code Migration with LLMs cites this paper.

Qiskit Code Migration with LLMs Vector embedding of multi-modal texts: a tool for discovery?

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-06-26T16:29:35.655215Z

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.