Typed states for the displayed outbound observations.
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Paper Citation Record · LEDGER
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2506.14808.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
66 of 66 outbound references displayed
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External citation measurements
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Outbound references
Observation bf5e58a4-4034-4b85-8cab-541803cdf5b7 · outbound
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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Observation 24cd664a-fadf-4028-895a-aa72fbb661ae · outbound
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
Reference 2
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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Flamingo: A visual language model for few-shot learning
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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
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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
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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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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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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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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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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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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
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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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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Robustness of learn- ing from task instructions
Reference 15
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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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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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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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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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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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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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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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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Scaling up visual and vision-language representa- tion learning with noisy text supervision
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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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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 Consistency and uncertainty: Identi- fying unreliable responses from black-box vision-language models for selective visual question answering
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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models MLLM-CompBench: A comparative reasoning benchmark for multimodal llms
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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 MMBench: Is your multi-modal model an all-around player? In Proc
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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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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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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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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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PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Learn- ing transferable visual models from natural language super- vision
Reference 48
- Resolution
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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 8ad6fbe6-5e59-4089-878d-9a59ad4eb7f0 · outbound
This paper cites an unresolved cited work.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Unresolved cited work
Reference 49
- Resolution
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- raw_fallback, observed 2026-08-07T11:12:03.888634Z
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 d438b7e4-4234-4d54-9500-59e664bf2f71 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:12:03.877746Z
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 42c0a7fc-290c-4f9e-901d-cbb41850d275 · outbound
This paper cites Least ambiguous set-valued classifiers with bounded error levels.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Least ambiguous set-valued classifiers with bounded error levels
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:12:03.867343Z
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 a10d00c2-b4fe-43e0-8dbf-4cfca8eff2ee · outbound
This paper cites Robustness analysis of video- language models against visual and language perturbations.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Robustness analysis of video- language models against visual and language perturbations
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:12:03.856709Z
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 5a92393e-8c37-471c-8308-f799b1f2128b · outbound
This paper cites RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T11:12:03.653653Z
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 ca7e8491-134c-4409-adad-86a4f4d322d2 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:12:03.845437Z
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 e95702ed-ab7b-4397-9b4d-c26fd9e56ee9 · outbound
This paper cites BLEURT: Learning robust metrics for text generation.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models BLEURT: Learning robust metrics for text generation
Reference 55
- Resolution
- verified fuzzy
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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 79a5c8ba-22eb-4bc3-874b-74e8aa76e268 · outbound
This paper cites Lm- nav: Robotic navigation with large pre-trained models of language, vision, and action.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:12:03.823802Z
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 eb0a9f03-926e-4887-aeff-8b9ba6fad606 · outbound
This paper cites Indoor segmentation and support inference from rgbd images.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Indoor segmentation and support inference from rgbd images
Reference 57
- Resolution
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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 6962ef3d-226b-4a47-961a-35ab789a815c · outbound
This paper cites Evalu- ating the zero-shot robustness of instruction-tuned language models.
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
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 7beab218-020e-4508-af8f-7630e956cbaa · outbound
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
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 1e692db5-119c-40bd-9381-72c1009e3167 · outbound
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e282ceaf-355a-4265-80e9-498bb5de20e3 · outbound
This paper cites Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements
Reference 61
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdbfc555-c304-43c1-b803-1ffcfa6cf5aa · outbound
This paper cites BLINK: Multimodal large language models can see but not perceive.
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
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 5b0527b7-1994-4420-b8da-fbb965198ccf · outbound
This paper cites CogVLM: Visual Expert for Pretrained Language Models.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models CogVLM: Visual Expert for Pretrained Language Models
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:12:03.554805Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58640330-70a4-45ad-9bb3-43527e453b96 · outbound
This paper cites Benchmarking LLMs via Uncertainty Quantification.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Benchmarking LLMs via Uncertainty Quantification
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:12:03.559440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ba1fa1e-d3cc-410f-9028-8da0fda17d6d · outbound
This paper cites Be- yond positive scaling: How negation impacts scaling trends of language models.
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
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 422b8a24-5fda-4877-8187-2c3571d2df16 · outbound
This paper cites an unresolved cited work.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Unresolved cited work
Reference 2021
- Resolution
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- raw_fallback, observed 2026-08-07T11:12:04.221441Z
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.
Pith citing papers
No inbound Pith citation observations are available.