PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models | Paper Citation Record

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This paper cites 3, 9, 11.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models 3, 9, 11

Reference 1

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This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

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This paper cites Flamingo: A visual language model for few-shot learning.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Flamingo: A visual language model for few-shot learning

Reference 3

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This paper cites How susceptible are LLMs to influence in prompts? In Proc.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models How susceptible are LLMs to influence in prompts? In Proc

Reference 4

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This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 5

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This paper cites This is not correct! Negation-aware evaluation of lan- guage generation systems.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models This is not correct! Negation-aware evaluation of lan- guage generation systems

Reference 6

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This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 7

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This paper cites Benchmarking robustness of adaptation methods on pre-trained vision-language models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Benchmarking robustness of adaptation methods on pre-trained vision-language models

Reference 8

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This paper cites PaLI: A jointly-scaled multilingual language- image model.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models PaLI: A jointly-scaled multilingual language- image model

Reference 9

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

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 10

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This paper cites Measuring and improving consistency in pre- trained language models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Measuring and improving consistency in pre- trained language models

Reference 11

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This paper cites IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

Reference 12

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This paper cites Sensitivity and ro- bustness of large language models to prompt template in Japanese text classification tasks.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Sensitivity and ro- bustness of large language models to prompt template in Japanese text classification tasks

Reference 13

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This paper cites Demystifying prompts in language models via perplexity estimation.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Demystifying prompts in language models via perplexity estimation

Reference 14

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This paper cites Robustness of learn- ing from task instructions.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Robustness of learn- ing from task instructions

Reference 15

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This paper cites Benchmarking neu- ral network robustness to common corruptions and perturba- tions.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Benchmarking neu- ral network robustness to common corruptions and perturba- tions

Reference 16

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This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models CogVLM2: Visual Language Models for Image and Video Understanding

Reference 17

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This paper cites Under- standing by understanding Not: Modeling negation in lan- guage models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Under- standing by understanding Not: Modeling negation in lan- guage models

Reference 18

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This paper cites A tutorial on calibration measurements and calibration models for clinical prediction models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models A tutorial on calibration measurements and calibration models for clinical prediction models

Reference 19

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This paper cites GQA: A new dataset for real-world visual reasoning and composi- tional question answering.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models GQA: A new dataset for real-world visual reasoning and composi- tional question answering

Reference 20

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This paper cites Dis- covering states and transformations in image collections.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Dis- covering states and transformations in image collections

Reference 21

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This paper cites BECEL: Benchmark for consistency evaluation of language models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models BECEL: Benchmark for consistency evaluation of language models

Reference 22

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 23

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This paper cites Fashionpedia: Ontology, segmentation, and an at- tribute localization dataset.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Fashionpedia: Ontology, segmentation, and an at- tribute localization dataset

Reference 24

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This paper cites Evaluating VLMs for score-based, multi-probe annotation of 3D objects.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Evaluating VLMs for score-based, multi-probe annotation of 3D objects

Reference 25

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models 3D common corruptions and data augmentation

Reference 26

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Consistency and uncertainty: Identi- fying unreliable responses from black-box vision-language models for selective visual question answering

Reference 27

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This paper cites MLLM-CompBench: A comparative reasoning benchmark for multimodal llms.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models MLLM-CompBench: A comparative reasoning benchmark for multimodal llms

Reference 28

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Uncertainty-Aware Evaluation for Vision-Language Models

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Conformal Prediction with Large Language Models for Multi-Choice Question Answering

Reference 30

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 31

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation

Reference 32

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models External validation of four dementia prediction models for use in the general community-dwelling population: a comparative analysis from the Rotterdam Study

Reference 33

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Improved baselines with visual instruction tuning

Reference 34

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models LLaV A-NeXT: Improved reasoning, OCR, and world knowledge, 2024

Reference 35

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Visual instruction tuning

Reference 36

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models MMBench: Is your multi-modal model an all-around player? In Proc

Reference 37

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Ex- ploring the sensitivity of LLMs’ decision-making capabili- ties: Insights from prompt variations and hyperparameters

Reference 38

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models MathVista: Evaluating mathemat- ical reasoning of foundation models in visual contexts

Reference 39

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitiv- ity

Reference 40

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Ecker, Matthias Bethge, and Wieland Brendel

Reference 41

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models State of what art? A call for multi-prompt LLM evaluation

Reference 43

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This paper cites Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift

Reference 44

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Learning to pre- dict visual attributes in the wild

Reference 45

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This paper cites What is the limitation of multimodal LLMs? A deeper look into multimodal LLMs through prompt probing.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models What is the limitation of multimodal LLMs? A deeper look into multimodal LLMs through prompt probing

Reference 46

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This paper cites Med- ical image understanding with pretrained vision language models: A comprehensive study.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Med- ical image understanding with pretrained vision language models: A comprehensive study

Reference 47

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This paper cites Learn- ing transferable visual models from natural language super- vision.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Learn- ing transferable visual models from natural language super- vision

Reference 48

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Unresolved cited work

Reference 49

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This paper cites MultiMedEval: A benchmark and a toolkit for evaluating medical vision-language models.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models MultiMedEval: A benchmark and a toolkit for evaluating medical vision-language models

Reference 50

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Least ambiguous set-valued classifiers with bounded error levels

Reference 51

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Robustness analysis of video- language models against visual and language perturbations

Reference 52

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo

Reference 53

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This paper cites Quantifying language models’ sensitivity to spurious fea- tures in prompt design or: How i learned to start worrying about prompt formatting.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Quantifying language models’ sensitivity to spurious fea- tures in prompt design or: How i learned to start worrying about prompt formatting

Reference 54

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models BLEURT: Learning robust metrics for text generation

Reference 55

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Lm- nav: Robotic navigation with large pre-trained models of language, vision, and action

Reference 56

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Indoor segmentation and support inference from rgbd images

Reference 57

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Evalu- ating the zero-shot robustness of instruction-tuned language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:03.800404Z

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This paper cites Measuring ro- bustness to natural distribution shifts in image classification.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Measuring ro- bustness to natural distribution shifts in image classification

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:03.788748Z

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This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 60

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements

Reference 61

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models BLINK: Multimodal large language models can see but not perceive

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:03.777865Z

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models CogVLM: Visual Expert for Pretrained Language Models

Reference 63

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Benchmarking LLMs via Uncertainty Quantification

Reference 64

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Be- yond positive scaling: How negation impacts scaling trends of language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:12:03.765526Z

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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Unresolved cited work

Reference 2021

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