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Paper Citation Record · LEDGER

Text encoders bottleneck compositionality in contrastive vision-language models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.14897.

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

pith.paper-citation-record.v1
2305.14897 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:43:11.274153Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:31.490745Z

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 7f7f135c-6b80-4c70-b083-f897bfa98c23 · inbound

Enhancing CLIP Conceptual Embedding through Knowledge Distillation cites this paper.

Enhancing CLIP Conceptual Embedding through Knowledge Distillation Text encoders bottleneck compositionality in contrastive vision-language models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T22:24:06.744349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T22:24:06.744349Z digest=sha256:aacd5fb7aeb1ccd89f21746a9ef5c491fc724847e4872cc3793544dba46cd26f

Observation bff2697a-ae28-445d-b6ff-f7dc8153592f · inbound

Multi-Modal Language Models as Text-to-Image Model Evaluators cites this paper.

Multi-Modal Language Models as Text-to-Image Model Evaluators Text encoders bottleneck compositionality in contrastive vision-language models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-16T04:43:11.274153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:43:11.274153Z digest=sha256:46904c0d3b0edccdcb3a66cb3f4c144c2f4c1f4c0cdd9c7c95860312ae291f02

Observation 858dea81-53cb-4e5f-b983-c9db8c974bc0 · inbound

Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning cites this paper.

Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning Text encoders bottleneck compositionality in contrastive vision-language models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-19T13:22:19.274790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-19T13:19:20.215467Z digest=sha256:a27090c3b746f5d3fa7efa4d2eb5b71e413b8e8d62f7a2484d36a258e1a4de11

Observation 11d76865-c971-4cba-a5c2-91f8aee6264d · inbound

Multi-Rationale Explainable Object Recognition via Contrastive Conditional Inference cites this paper.

Multi-Rationale Explainable Object Recognition via Contrastive Conditional Inference Text encoders bottleneck compositionality in contrastive vision-language models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T18:43:33.154054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:43:33.154054Z digest=sha256:d98640fa5aced9fb8fde879a9e03fc81bdd01bae23701fe23eb4f2e90e2f817c

Observation 0971d8b4-3b7d-414a-b8c3-f01abe16fd3c · inbound

Adapting MLLMs for Nuanced Video Retrieval cites this paper.

Adapting MLLMs for Nuanced Video Retrieval Text encoders bottleneck compositionality in contrastive vision-language models

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:21:18.898490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T22:20:09.051957Z digest=sha256:e1ebe3c83465e655abb39b14927a8187b7deff6f171da318166fb2a6be98a740

Observation 2de4813f-fb98-44b5-a3a0-0f5f4f1644fb · inbound

Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression cites this paper.

Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression Text encoders bottleneck compositionality in contrastive vision-language models

Reference 138

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:51:09.401843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-09T14:36:29.666730Z digest=sha256:b1b19838d056eb8f1747033923c64de6b789d0249cb568a1d7934d41d060c6c3

Observation 351dfa23-e58c-4fc1-976c-a11fddc3717a · inbound

Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression cites this paper.

Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression Text encoders bottleneck compositionality in contrastive vision-language models

Reference 138

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:51:26.145791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-12T04:52:09.685243Z digest=sha256:962564963770ae5e8f975de60889141b5cea65000eabb51695ad13735acd9232

Observation 9e199ed7-6ce6-4840-9487-8a7edede7e32 · inbound

LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer? cites this paper.

LatentRouter: Can We Choose the Right Multimodal Model Before Seeing Its Answer? Text encoders bottleneck compositionality in contrastive vision-language models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:47:04.352447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-13T01:42:54.802658Z digest=sha256:022b4dfd6ceb23a78a2c78ec8494f81aa6e0726b9be16c875fcde5a6b0557cee

Observation 1e0461b1-660d-4907-b56a-9a332f56f2e4 · inbound

Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality cites this paper.

Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality Text encoders bottleneck compositionality in contrastive vision-language models

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:28:31.492022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T07:03:50.311891Z digest=sha256:14996d5ec91d756256c3d7ebc7dd2f0a5f635c5e4fdc5dae29e562caa2d1bc2f