Challenge: Large Vision-Language Models (LVLMs) are hardly comprehensively evaluated for their cognitive abilities.
Approach: They propose to evaluate high-level cognitive abilities of Large Vision-Language Models (LVLMs) using images with rich semantics.
Outcome: The proposed evaluation benchmark consists of 251 images along with comprehensive annotations.

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Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)

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Challenge: Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains.
Approach: They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks.
Outcome: Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information.
NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models (2025.findings-naacl)

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Challenge: Recent advances in large language models have demonstrated their strong performance on IQ test questions, achieving high scores across many languages.
Approach: They propose a dataset to evaluate cognitive multimodal reasoning and problem-solving skills of large models.
Outcome: The proposed dataset contains 2,728 multiple-choice questions and 4,642 images spanning 26 categories.
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning (2024.findings-emnlp)

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Challenge: Recent studies have demonstrated that large vision language models (LVLMs) are not multi-modal and lack multi-tasking capabilities.
Approach: They evaluate the performance of large vision language models (LVLMs) for chart understanding and reasoning tasks and compare them to open-source models.
Outcome: The proposed models demonstrate impressive abilities in generating fluent texts covering high-level data insights, but they also encounter common problems like hallucinations, factual errors, and data bias.
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? (2025.acl-industry)

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Challenge: Large Vision-Language Models (LVLMs) are expensive and time-consuming to evaluate . however, they are limited in their use in industrial settings due to their limited availability and limited resources.
Approach: They evaluate 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks.
Outcome: The proposed models can be used to assess chart comprehension and reasoning tasks, but they are expensive and time-consuming.
Beyond Visual Understanding Introducing PARROT-360V for Vision Language Model Benchmarking (2025.coling-industry)

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Challenge: Current benchmarks for evaluating Vision Language Models (VLMs) often fail to thoroughly assess these models’ abilities to understand complex visual and textual content.
Approach: They propose a benchmark that features 2487 visual puzzles designed to test VLMs on complex visual reasoning tasks.
Outcome: The PARROT-360V Benchmark features 2487 visual puzzles designed to test VLMs on complex visual reasoning tasks.
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

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Challenge: Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach: They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome: The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.
MVP-Bench: Can Large Vision-Language Models Conduct Multi-level Visual Perception Like Humans? (2024.findings-emnlp)

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Challenge: Existing LVLMs perform visual perception at multiple levels, but they are not able to perform multi-level tasks.
Approach: They propose a visual–language benchmark to evaluate LVLMs' perceptions . they use manipulated images to examine how LVLs can perform multi-level tasks .
Outcome: The proposed model performs poorly on high-level perception tasks, the authors show . they also show that current models do not generalize in understanding semantics of synthetic images .
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models (2025.emnlp-main)

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Challenge: Large vision-language models have shown impressive ability in various language tasks, especially with their emergent in-context learning capability.
Approach: They propose a causal reasoning benchmark for multi-modal in-context learning from large vision-language models that incorporates visual inputs.
Outcome: The proposed model outperforms existing models on three visual causal reasoning tasks and demonstrates their strengths and weaknesses.
VLind-Bench: Measuring Language Priors in Large Vision-Language Models (2025.findings-naacl)

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Challenge: Large Vision-Language Models suffer from a problem known as language prior . such language priors can lead to undesirable biases and hallucinations when dealing with images that are out of distribution.
Approach: They propose a benchmark to measure the language priors of Large Vision-Language Models.
Outcome: The proposed benchmark is the first specifically designed to measure the language priors, or blindness, of LVLMs.
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)

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Challenge: Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics.
Approach: They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty.
Outcome: The proposed framework offers insights into optimal strategies for accelerating LVLMs.

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