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.

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Benchmarking Vision Language Models for Cultural Understanding (2024.emnlp-main)

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Challenge: Recent multimodal vision-language models have shown impressive performance in tasks such as image-to-text generation, visual question answering, and image captioning.
Approach: They propose a visual question-answering benchmark to assess VLMs' cultural understanding of various facets of culture from 11 countries across 5 continents.
Outcome: The visual question-answering benchmark aims to assess VLMs' cultural understanding across regions.
Jigsaw-Puzzles: From Seeing to Understanding to Reasoning in Vision-Language Models (2025.emnlp-main)

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Challenge: Existing vision-language models lack spatial reasoning capability, despite their ability to comprehend spatial arrangements and model structural relations.
Approach: They propose a benchmark to evaluate vision-language models' spatial perception, structural understanding, and reasoning capabilities by minimizing reliance on domain-specific knowledge.
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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.
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A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models (2025.naacl-long)

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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.
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities (2025.acl-long)

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Challenge: Vision-language Models have been shown to be highly capable but lacking basic visual understanding skills.
Approach: They propose to examine the limitations of vision-language models on visual tasks by constructing a series of tests that probe which components of design may be lacking.
Outcome: The proposed tests compare VLMs to other models on visual encoders, intermediate vision-language projection and LLM-decoder outputs.
MathSight: A Benchmark Exploring Have Vision-Language Models Really Seen in University-Level Mathematical Reasoning? (2026.acl-long)

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Challenge: Existing benchmarks rarely isolate how much visual information contributes to reasoning . a growing collection of benchmarks has catalyzed rapid progress in multimodal reasoning - but how much it contributes remains unclear .
Approach: They propose a university-level multimodal mathematical reasoning benchmark to quantify the effect of visual input.
Outcome: The proposed benchmark disentangles and quantifies the effect of visual input on multimodal reasoning models.
Enhancing Advanced Visual Reasoning Ability of Large Language Models (2024.emnlp-main)

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Challenge: Recent advances in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability.
Approach: They propose a novel multi-modal in-context learning methodology to enhance LLMs’ contextual understanding and reasoning.
Outcome: The proposed model achieves SOTA performance among all visual reasoning tasks and achieves a 'higher level of accuracy' than previous models.
TURTLEAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics (2026.findings-acl)

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Challenge: Vision-language models have been explored for visual programming, but performance is unclear . most prior work focuses on visual programming for productivity .
Approach: They propose a visual programming benchmark that uses visual programming to evaluate VLMs.
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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.
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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.

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