Paper Citation Record · LEDGER

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2606.19584.

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

pith.paper-citation-record.v1
2606.19584 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T20:54:48.203293Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:48:51.126553Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact23
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier3
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b409a6b4-2b1c-469c-a19c-41e9306614ba · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Exploring Visual Prompts for Adapting Large-Scale Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.219783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d801b32-0b86-45eb-a176-91299175cc09 · outbound

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

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception PaliGemma: A versatile 3B VLM for transfer

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.235400Z

Source-reported events for the cited work

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Observation f65f8726-a036-4254-9f67-75bccbe48d5f · outbound

This paper cites All You May Need for VQA are Image Captions.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception All You May Need for VQA are Image Captions

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:49:19.182325Z

Source-reported events for the cited work

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Observation 04765852-f0e7-47bf-a8a0-0e3d3f4d71f5 · outbound

This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.151225Z

Source-reported events for the cited work

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Observation af8d776a-b5dc-4e54-93ff-56c205391feb · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.186065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fe8d1ff0-b339-427f-a452-b870f0b87148 · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning.Advances in neural information processing systems, 36:49250–49267,.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Instructblip: Towards general-purpose vision-language models with instruction tuning.Advances in neural information processing systems, 36:49250–49267,

Reference 6

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Source-reported events for the cited work

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Observation d0a11f6c-9b33-4edd-a4bf-3dbaede5828b · outbound

This paper cites Selective Visual Representations Improve Convergence and Generalization for Embodied AI.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Selective Visual Representations Improve Convergence and Generalization for Embodied AI

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.230707Z

Source-reported events for the cited work

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Observation 74603111-ced3-4d18-9828-7fd57917637a · outbound

This paper cites Data Filtering Networks.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Data Filtering Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.225762Z

Source-reported events for the cited work

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Observation d0b878aa-956d-45f4-8003-365ac9d3cd15 · outbound

This paper cites The Llama 3 Herd of Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception The Llama 3 Herd of Models

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T00:49:19.239610Z

Source-reported events for the cited work

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Observation 500065c9-714e-448b-823f-4fa100ce157f · outbound

This paper cites E5-V: Universal Embeddings with Multimodal Large Language Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception E5-V: Universal Embeddings with Multimodal Large Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.214012Z

Source-reported events for the cited work

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Observation 0f3e7447-3d28-4bdd-bc44-5b2744a5d4ff · outbound

This paper cites Referitgame: Referring to objects in photographs of natural scenes.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Referitgame: Referring to objects in photographs of natural scenes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

Source-reported events for the cited work

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Observation 2f15f18c-c861-4fad-bd23-32145fe9126d · outbound

This paper cites Modeling Caption Diversity in Contrastive Vision-Language Pretraining.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Modeling Caption Diversity in Contrastive Vision-Language Pretraining

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.203184Z

Source-reported events for the cited work

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Observation eb94859a-361c-4f75-bf42-d1c570f5915a · outbound

This paper cites Vx2text: End-to-end learning of video-based text generation from multimodal inputs.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Vx2text: End-to-end learning of video-based text generation from multimodal inputs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

Source-reported events for the cited work

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Observation db8e253e-c488-4afd-b7a7-b04411b59a66 · outbound

This paper cites Training-free deep concept injection enables language models for video question answering.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Training-free deep concept injection enables language models for video question answering

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

Source-reported events for the cited work

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Observation 24da8478-d867-4092-ad40-ec9f08a7b5fd · outbound

This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.208818Z

Source-reported events for the cited work

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Observation 075770d9-5c3b-40e0-99be-b6180696ee69 · outbound

This paper cites Visual Classification via Description from Large Language Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Visual Classification via Description from Large Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.244441Z

Source-reported events for the cited work

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Observation 39dc97ab-cd15-4c86-b94d-147a7176662e · outbound

This paper cites Task Bias in Vision-Language Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Task Bias in Vision-Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.264204Z

Source-reported events for the cited work

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Observation 0ed28888-7932-4c8c-af28-f5be5c797b1d · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception DINOv2: Learning Robust Visual Features without Supervision

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.186873Z

Source-reported events for the cited work

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Observation b09248ad-332a-46dc-94bf-8d04a110f576 · outbound

This paper cites Pre-training image-language transformers for open-vocabulary tasks.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Pre-training image-language transformers for open-vocabulary tasks

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:49:19.212641Z

Source-reported events for the cited work

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Observation 67f47f13-970e-4356-a8aa-cecdc6daa478 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.177192Z

Source-reported events for the cited work

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Observation b75a33ca-9a94-4554-8f86-a03749aacd6a · outbound

This paper cites X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.166875Z

Source-reported events for the cited work

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Observation 650ab919-5ed6-4535-aa2b-f874432f07df · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.230531Z

Source-reported events for the cited work

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Observation 6969ea20-7901-4607-abca-b9018cfccf80 · outbound

This paper cites Gemma 3 Technical Report.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Gemma 3 Technical Report

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.190898Z

Source-reported events for the cited work

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Observation abfccb1c-3e97-4a79-b498-cd004b12dba1 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.172207Z

Source-reported events for the cited work

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Observation bcf2fc55-31ca-4fa8-abe4-c2168bb3593c · outbound

This paper cites Scaling Pre-training to One Hundred Billion Data for Vision Language Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Scaling Pre-training to One Hundred Billion Data for Vision Language Models

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.195832Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b3c985a-f1f3-4b88-b7af-7c74314198ab · outbound

This paper cites Demystifying CLIP Data.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Demystifying CLIP Data

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.181436Z

Source-reported events for the cited work

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Observation cf4f49d8-1f70-489b-8683-ce1c00aa6e29 · outbound

This paper cites Large Language Models as Optimizers.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Large Language Models as Optimizers

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.235077Z

Source-reported events for the cited work

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Observation 950179fd-155c-499d-88e5-8da671c702dd · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:19.259387Z

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Observation 34451644-0e85-47e9-980a-7eefb87abe42 · outbound

This paper cites Scaling vision transformers.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Scaling vision transformers

Reference 29

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Observation 43eea2a8-91f0-4808-b12d-7c5e1088cdb6 · outbound

This paper cites MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.250279Z

Source-reported events for the cited work

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Observation c764cc1d-0431-4631-ac7a-56970e5dd1b3 · outbound

This paper cites VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.255293Z

Source-reported events for the cited work

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Observation c24c57e1-e3a7-4ef1-b4f8-eda8f79322fb · outbound

This paper cites However, its practical application and further development are subject to certain limitations, which also open avenues for future research.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception However, its practical application and further development are subject to certain limitations, which also open avenues for future research

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

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Observation 3026f273-31af-4762-aecd-544733f4dfb6 · outbound

This paper cites (2021), SigLip Zhai et al.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception (2021), SigLip Zhai et al

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

Source-reported events for the cited work

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Observation b31ba3f7-e307-4343-9c7d-5eac1b8b6016 · outbound

This paper cites Interestingly, by leveraging Gemini to evolve and generate different text prompts Yang et al.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Interestingly, by leveraging Gemini to evolve and generate different text prompts Yang et al

Reference 34

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Observation 4afd2aa5-0bde-41c6-a55b-97b803725cd8 · outbound

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Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Unresolved cited work

Reference 35

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Source-reported events for the cited work

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Observation f6d734c5-ba85-4df6-843c-595b9a9c37be · outbound

This paper cites The resulting distribution, visualized in Figure 13, reveals significant variations in instruction fre- quency.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception The resulting distribution, visualized in Figure 13, reveals significant variations in instruction fre- quency

Reference 36

Resolution: malformed identifier; no resolver link, observed 2026-06-26T20:54:48.203293Z

Source-reported events for the cited work

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Observation 1f6a59b5-a165-44a2-99d0-43b7855c8550 · outbound

This paper cites an unresolved cited work.

Language-Instructed Vision Embeddings for Controllable and Generalizable Perception Unresolved cited work

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T20:54:48.203293Z

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Pith citing papers

Observation bfdd4eff-2c2d-400e-bbbe-6f7e1f2e63ef · inbound

HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models cites this paper.

HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models Language-Instructed Vision Embeddings for Controllable and Generalizable Perception

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T14:48:51.126553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.