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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 2026-08-13T06:32:02.005865+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:e2b2605f62cd49bd98f15ed6c96533a5fae480477a0fb501386ec62330601508

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-19T13:19:20.215467Z digest=sha256:3bfa77a4efd02908e050703fd21c2f24ac7ae3d0e7619affe5f0192f32cb3e40

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:adf33917466bc7d5e75aa45d275a86f4abf40c92ab7025950ca85b764b11ce39

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-16T22:20:09.051957Z digest=sha256:09dd46fc4a1421a758bf04038f7fcb979a610915a7b78aca5233cf78a05201e9

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-09T14:36:29.666730Z digest=sha256:07608f7a69bacb18f432ad82a76ad9cc9bedd81c5571c8b9644eac81c742d77e

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-12T04:52:09.685243Z digest=sha256:c033feb8d343e8c7722d677facc28ebbc40a3dec4ff9589be2ce2cab1a211748

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-13T01:42:54.802658Z digest=sha256:a929aa68653f6f24bab03247bae27b5990a5abf769f6a42315f5538e29b27baa

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-27T07:03:50.311891Z digest=sha256:658824e309b5eac89bc468334d5fc16454edf94940e7eeec3e25ccc96eb42343