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Paper Citation Record · LEDGER

Text encoders bottleneck compositionality in contrastive vision-language models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:24:06.744349Z

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:e5fef002c3a29c11c0884823796b93a5f32c09014bff1496c2278e35c8ce72a2

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-19T13:19:20.215467Z digest=sha256:00889e50c31d922181ea2668665808e3c9dd8544f498a5ecaf8fde1a5b57ddc4

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:dd3d98227d20b5eadfec5c3767aa65b6ef6bce65ba4edc2370f5419254de811a

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-16T22:20:09.051957Z digest=sha256:c8810a543d4b5c8d27fe8da54032557764c2537b2144ee4003dc10c9ae49f071

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-09T14:36:29.666730Z digest=sha256:9846538253d56f546162bdca349c21ae361dededb6157859c337817bae491ee3

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-12T04:52:09.685243Z digest=sha256:1156830fb997a626e72f6dde40b4bf91d06f19583d27b2ef34dd693e32482b10

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-13T01:42:54.802658Z digest=sha256:8330b708bd2a83b08a4cc72eff938987850d6e6f3435f788f8f1928bd28b2746

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-06-27T07:03:50.311891Z digest=sha256:c5ed53e37d6b6c9eb84813448b55f1fd4931719cfff83eab76f026120ecd1178