VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models | Paper Citation Record

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Unresolved cited work

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This paper cites Princeton University Press, Princeton, NJ, January 2015.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Princeton University Press, Princeton, NJ, January 2015

Reference 2

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This paper cites The aesthetics of graph visualization.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models The aesthetics of graph visualization

Reference 3

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This paper cites Camilus and Govindan V K.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Camilus and Govindan V K

Reference 4

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This paper cites Graph matching based on similarities in structure and attributes.arXiv [cs.DS], September 2024.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Graph matching based on similarities in structure and attributes.arXiv [cs.DS], September 2024

Reference 5

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This paper cites A First Course in Graph Theory.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A First Course in Graph Theory

Reference 6

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This paper cites Vision-Language Models Can Self-Improve Reasoning via Reflection.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Vision-Language Models Can Self-Improve Reasoning via Reflection

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This paper cites Fast approximate isorank for scalable global alignment of biological networks.bioRxiv, 2023.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Fast approximate isorank for scalable global alignment of biological networks.bioRxiv, 2023

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

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites Benchmarking and Improving Detail Image Caption.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Benchmarking and Improving Detail Image Caption

Reference 10

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models The llama 3 herd of models, 2024

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This paper cites An introduction to graph theory.arXiv [math.HO], August 2023.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models An introduction to graph theory.arXiv [math.HO], August 2023

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models The False Promise of Imitating Proprietary LLMs

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Survey of Graph Analysis Applications

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This paper cites OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM

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This paper cites Towards Reasoning in Large Language Models: A Survey.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Towards Reasoning in Large Language Models: A Survey

Reference 17

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This paper cites Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs

Reference 18

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This paper cites Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors, 2024.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Llms meet vlms: Boost open vocabulary object detection with fine-grained descriptors, 2024

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Networkfailuredetectionandgraphconnectivity

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Large Language Models are Zero-Shot Reasoners

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This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking,.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking,

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models VisualBERT: A Simple and Performant Baseline for Vision and Language

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Evaluating Object Hallucination in Large Vision-Language Models

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Graph Matching Networks for Learning the Similarity of Graph Structured Objects

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Self-Refine: Iterative Refinement with Self-Feedback

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Revisiting link prediction: A data perspective, 2024

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Unichart: A univer- sal vision-language pretrained model for chart comprehension and reasoning

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Inverse Scaling: When Bigger Isn't Better

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Brain network similarity: methods and applications

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This paper cites Attacking shortest paths by cutting edges.arXiv [cs.SI], November 2022.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Attacking shortest paths by cutting edges.arXiv [cs.SI], November 2022

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models GPT-4 Technical Report

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Automatically correcting large language models: Surveying the landscape of diverse self-correction strategies

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Learning Transferable Visual Models From Natural Language Supervision

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Self-reflection in llm agents: Effects on problem-solving performance,

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

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This paper cites GraphPi: High performance graph pattern matching through effective redundancy elimination.arXiv [cs.DC], September 2020.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models GraphPi: High performance graph pattern matching through effective redundancy elimination.arXiv [cs.DC], September 2020

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A survey on graph matching in computer vision

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models LXMERT: Learning Cross-Modality Encoder Representations from Transformers

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This paper cites Introducing Claude 3.5 Sonnet — anthropic.com.https://www.anthropic.com/news/ claude-3-5-sonnet, 2024.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Introducing Claude 3.5 Sonnet — anthropic.com.https://www.anthropic.com/news/ claude-3-5-sonnet, 2024

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Kistowski, Jeremy A

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Attention Is All You Need

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Graph cut based image segmentation with connectivity priors

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This paper cites Surgical-lvlm: Learning to adapt large vision-language model for grounded visual question answering in robotic surgery, 2024.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Surgical-lvlm: Learning to adapt large vision-language model for grounded visual question answering in robotic surgery, 2024

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Llm-seg: Bridging image segmentation and large language model reasoning,

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VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

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Observation 00a246c3-6ebd-4316-9f69-e4964da1ed8e · outbound

This paper cites GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning

Reference 53

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Observation 5d10269e-e38e-457a-9fc7-c0afbb7b6a04 · outbound

This paper cites Multimodal LLMs struggle with basic visual network analysis: A VNA benchmark.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Multimodal LLMs struggle with basic visual network analysis: A VNA benchmark

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.165631Z

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Observation e4cf4322-eb21-4810-bfea-1b5d150ed34a · outbound

This paper cites LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.259310Z

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Observation 4d82075b-bcd7-41c2-8612-52866056bf8d · outbound

This paper cites Qwen2 Technical Report.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Qwen2 Technical Report

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.303977Z

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Observation 00597e96-5cad-4259-bb8c-bad50e87d1d1 · outbound

This paper cites A Survey on Multimodal Large Language Models.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A Survey on Multimodal Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.314162Z

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Observation 2a4d48f2-5dd1-490e-81ab-0a7fb8dc4b69 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Florence: A New Foundation Model for Computer Vision

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.320860Z

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Observation 6393b5fd-af2c-483f-bb4e-852502383b96 · outbound

This paper cites GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.291073Z

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Unavailable: canonical work link unavailable.

Observation d1b3795c-8220-4ab1-85f2-91740f068ac2 · outbound

This paper cites Vision-Language Models for Vision Tasks: A Survey.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Vision-Language Models for Vision Tasks: A Survey

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.333418Z

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Unavailable: canonical work link unavailable.

Observation 993bc039-f3f8-4724-9d70-c8c8fc43ace0 · outbound

This paper cites Link Prediction Based on Graph Neural Networks.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Link Prediction Based on Graph Neural Networks

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.338769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb2b9bb-2758-4d91-bdc8-85307ab7a811 · outbound

This paper cites Why are Visually-Grounded Language Models Bad at Image Classification?.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Why are Visually-Grounded Language Models Bad at Image Classification?

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.345045Z

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Unavailable: canonical work link unavailable.

Observation 685f869f-e7bc-4de9-9059-9fbdf4b8a96e · outbound

This paper cites Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.327620Z

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Unavailable: canonical work link unavailable.

Observation 329d2f69-bb79-4b6a-913e-2bc282d57a48 · outbound

This paper cites DynaMath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models.arXiv [cs.CV], October 2024.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models DynaMath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models.arXiv [cs.CV], October 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.139893Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba719b03-d1d1-4b97-812a-16b2a7b00899 · outbound

This paper cites A Survey on the Memory Mechanism of Large Language Model based Agents.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models A Survey on the Memory Mechanism of Large Language Model based Agents

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.350538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44b43596-1568-4c1a-84f5-61edf5eaa651 · outbound

This paper cites The goal is to answer the question: How many nodes and edges are in the image?↪→.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models The goal is to answer the question: How many nodes and edges are in the image?↪→

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.117234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5757b3c8-e9ba-44e0-b1de-3dadd0e2c314 · outbound

This paper cites total_nodes.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models total_nodes

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.097833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17cb1f26-a80e-4888-b62c-4052540af32f · outbound

This paper cites - Count each unique node you see.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models - Count each unique node you see

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.076054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5cc73319-0e81-443d-970f-ab9dec0e62b6 · outbound

This paper cites - Count each unique edge you see.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models - Count each unique edge you see

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.057144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b264ca9-3a2f-492d-bedb-e3b09427f016 · outbound

This paper cites - Report the total number of edges.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models - Report the total number of edges

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.031051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9ec624b2-4b0d-4b4a-bf81-ebb276150abc · outbound

This paper cites total_nodes.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models total_nodes

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:59.005023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89a42431-d45b-4d96-beb3-3cfec4197648 · outbound

This paper cites shortest_path.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models shortest_path

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:52:58.982970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f597332-b73e-4daf-aaa1-21cbad76937b · outbound

This paper cites an unresolved cited work.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Unresolved cited work

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.234236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e618a56-0347-42f7-aeec-bbbd87a2317c · outbound

This paper cites Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:52:58.699023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0e8c0c39-0080-43a6-ba10-0c582ecd4dd2 · outbound

This paper cites Self-Reflection in LLM Agents: Effects on Problem-Solving Performance.

VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models Self-Reflection in LLM Agents: Effects on Problem-Solving Performance

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T14:52:57.161584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

Outbound references

Pith citing papers