Paper Citation Record · LEDGER

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets

As of 18 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 3 inbound Pith citation observations for arXiv:2506.04598.

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

pith.paper-citation-record.v1
2506.04598 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:45:57.335959Z

measured 53 of 53 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T21:10:10.548489Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:17:24.049592Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ca5c1e6-840d-40e1-b52c-126bfd34de4d · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets On the Opportunities and Risks of Foundation Models

Reference 1

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Observation acef53e8-30cd-401d-94f3-bc4fcd46c31f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

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Observation 44834df0-fa3f-46a6-9e79-9bdd727e1aa8 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:58.140930Z

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Observation 93bbbcde-60cd-480a-b574-a19636563501 · outbound

This paper cites Language models are few-shot learners.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Language models are few-shot learners

Reference 4

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Observation d0e7881d-5fcd-4737-b05d-b6f62186d298 · outbound

This paper cites Scaling Language-Free Visual Representation Learning.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling Language-Free Visual Representation Learning

Reference 5

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Observation 3f04def2-5605-41f7-b35e-5945e3d1e3c6 · outbound

This paper cites Clap learning audio concepts from natural language supervision.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Clap learning audio concepts from natural language supervision

Reference 6

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Observation 5d057258-a155-4ed5-b223-ee0eaf30e56d · outbound

This paper cites Learning transferable visual models from natural language supervision.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Learning transferable visual models from natural language supervision

Reference 7

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Source-reported events for the cited work

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Observation 2ce64a3f-8e20-42ec-92c7-f8d3f1c86a16 · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling Laws for Neural Language Models

Reference 8

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Observation 74f23fb8-75bc-409f-8765-228a7f5c653d · outbound

This paper cites An empirical analysis of compute-optimal large language model training.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets An empirical analysis of compute-optimal large language model training

Reference 9

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Observation 9ba7c892-b33e-4373-ba23-7edaca9fd5db · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Reproducible scaling laws for contrastive language-image learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:58.071917Z

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Observation f28d148b-66c4-4d1b-bc04-de9f2f290374 · outbound

This paper cites (mis)fitting scaling laws: A survey of scaling law fitting techniques in deep learning.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets (mis)fitting scaling laws: A survey of scaling law fitting techniques in deep learning

Reference 11

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Source-reported events for the cited work

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Observation 34c66f8c-70cf-4a75-ac21-256d108f93eb · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems , 36:34892–34916, 2023.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Visual instruction tuning.Advances in neural information processing systems , 36:34892–34916, 2023

Reference 12

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Observation 7baa0607-d258-4142-a008-83807da76d76 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 13

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Observation cd523d8d-47b7-44f3-b164-60810c9f9d45 · outbound

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

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 14

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Observation 2b6c118d-8856-4090-977d-15ee53ab484d · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 15

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Observation 637bdb7c-8ed5-4cca-9b5b-8a56cf18ab97 · outbound

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

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 16

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Observation a1de7b6c-b141-4c36-8c8f-6a4876133f14 · outbound

This paper cites Dai, Zhifeng Chen, Claire Cui, and Anelia Angelova.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Dai, Zhifeng Chen, Claire Cui, and Anelia Angelova

Reference 17

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Source-reported events for the cited work

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Observation bc8619a5-14eb-40f5-8f01-388d6c315622 · outbound

This paper cites Sigmoid loss for language image pre-training.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Sigmoid loss for language image pre-training

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:57.176558Z

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Observation bcc2ddf1-82ef-4148-a072-a3176b5e0937 · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Datacomp: In search of the next generation of multimodal datasets

Reference 19

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Observation 02ee07dd-7e94-477a-af97-e76ba7826336 · outbound

This paper cites Data filtering networks.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Data filtering networks

Reference 20

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Source-reported events for the cited work

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Observation 90134bb7-2314-4c08-a3a4-2c39816cae23 · outbound

This paper cites Releasing re-laion 5b: transparent iteration on laion-5b with additional safety fixes.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Releasing re-laion 5b: transparent iteration on laion-5b with additional safety fixes

Reference 21

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Observation 39de68dd-f663-42ad-86f2-c1c40c542384 · outbound

This paper cites Ilharco, M.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Ilharco, M

Reference 22

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Observation 93a1d00b-a6d2-48a6-bb85-95e4e2219f42 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Representation Learning with Contrastive Predictive Coding

Reference 23

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Observation 286f55c2-62df-45a9-b027-9c66a8a90d0a · outbound

This paper cites Decoupled Weight Decay Regularization.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Decoupled Weight Decay Regularization

Reference 24

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Observation 9cdb8025-f7cc-4dc0-9af1-fb372e83e20d · outbound

This paper cites How do we know how smart ai systems are? Science, 381(6654):eadj5957, 2023.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets How do we know how smart ai systems are? Science, 381(6654):eadj5957, 2023

Reference 25

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Observation 4e30626a-2374-407a-b168-c3d94509d8c0 · outbound

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Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Unresolved cited work

Reference 26

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Observation 4c41eb59-9ba9-4468-b18e-8b4c3854ff1e · outbound

This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389–5400.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389–5400

Reference 27

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Observation 14a83510-e1a2-46dd-a8b6-99f611a7bcc9 · outbound

This paper cites The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Reference 28

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Observation db1515a3-2ff0-48da-bc95-a2f76eb6b5bd · outbound

This paper cites Natural Adversarial Examples.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Natural Adversarial Examples

Reference 29

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Observation a29d9b32-b042-40c7-9fe7-aeaccd0e0cee · outbound

This paper cites Learning Robust Global Representations by Penalizing Local Predictive Power.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Learning Robust Global Representations by Penalizing Local Predictive Power

Reference 30

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This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 31

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This paper cites Clip benchmark.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Clip benchmark

Reference 32

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This paper cites Microsoft coco: Common objects in context.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Microsoft coco: Common objects in context

Reference 33

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This paper cites Scene Parsing through ADE20K Dataset.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scene Parsing through ADE20K Dataset

Reference 34

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Observation d47fe5ec-d400-498c-8cc9-2d1da58b8dad · outbound

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Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Unresolved cited work

Reference 35

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Observation ce68d3f6-4f71-4e1d-8d08-9436c2462f63 · outbound

This paper cites Your ViT is Secretly an Image Segmen- tation Model.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Your ViT is Secretly an Image Segmen- tation Model

Reference 36

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This paper cites Scaling vision transform- ers.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling vision transform- ers

Reference 37

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This paper cites Scaling Laws for Autoregressive Generative Modeling.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling Laws for Autoregressive Generative Modeling

Reference 38

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This paper cites Training Compute-Optimal Large Language Models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Training Compute-Optimal Large Language Models

Reference 39

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Observation ed4b4c7b-c2c2-480b-a9f8-f3de656ccbaf · outbound

This paper cites The skyline operator.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets The skyline operator

Reference 40

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Observation a99369db-6c2e-4c3b-8bd3-6ded42d0875c · outbound

This paper cites Scaling data-constrained language models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling data-constrained language models

Reference 41

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Observation 73ad35d4-0f16-4ed5-983e-78e37d1268ed · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:57.722430Z

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This paper cites Image captioners are scalable vision learners too.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Image captioners are scalable vision learners too

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:57.706928Z

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Observation 0e528714-4234-467f-ad89-41fe2cca0446 · outbound

This paper cites Resolving discrepancies in compute-optimal scaling of language models.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Resolving discrepancies in compute-optimal scaling of language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:57.691597Z

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Observation bd523a73-f559-4764-b8a4-5718bd18eab8 · outbound

This paper cites Demystifying CLIP Data.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Demystifying CLIP Data

Reference 45

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Source-reported events for the cited work

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Observation d69a5fdb-fed8-4b23-8590-b03f75812f51 · outbound

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

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 46

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Observation 1ca84f57-c161-401b-939f-f32b0ecf4a29 · outbound

This paper cites TULIP: Towards Unified Language-Image Pretraining.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets TULIP: Towards Unified Language-Image Pretraining

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:57.321888Z

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Observation 1ccba6ff-ad5d-47f6-83c9-b306beaa141c · outbound

This paper cites OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning

Reference 48

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Source-reported events for the cited work

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Observation 75ab3d08-8b34-412b-9e88-1b4add3782ba · outbound

This paper cites Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:57.331326Z

Source-reported events for the cited work

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Observation 984f70ed-4379-4900-9f0b-0a89e3be1cc5 · outbound

This paper cites Scaling laws for data filtering–data curation cannot be compute agnostic.

Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets Scaling laws for data filtering–data curation cannot be compute agnostic

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:57.675781Z

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Pith citing papers

Observation de8930b6-1dca-41ba-bb06-23dae956c33a · inbound

Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models cites this paper.

Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:25:18.542696Z

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Observation 541b989f-15f9-4436-abaf-d65ccac06b2e · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets

Reference 222

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:45:47.673551Z

Source-reported events for the cited work

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Observation 12abe144-1341-473c-a12a-8c4c8fea0a3d · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets

Reference 222

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:17:24.051026Z

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