| Challenge: | Vision-Language Models (VLMs) perform well on textual equations, but fail on visually grounded counterparts. |
| Approach: | They propose to decompose visual equation solving into symbolic equation solving and visual recognition into two core components to understand this gap. |
| Outcome: | The proposed models perform well on textual equations, but fail on visual grounded ones. |
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| Challenge: | Vision Language Models struggle with visual arithmetic, seemingly simple tasks like object counting or length comparison, which are essential for relevant complex tasks like chart understanding and geometric reasoning. |
| Approach: | They propose a novel post-training strategy inspired by Piaget’s theory of cognitive development that trains VLMs to recognize invariant properties under visual transformations. |
| Outcome: | The proposed approach outperforms supervised fine-tuning methods while requiring 60% less training data. |
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. |
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Can Vision-Language Models Evaluate Handwritten Math? (2025.acl-long)
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| Challenge: | Recent advances in Vision-Language Models (VLMs) have significantly enhanced the ability to interpret both textual and visual data. |
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Can VLMs Actually See and Read? A Survey on Modality Collapse in Vision-Language Models (2025.findings-acl)
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| Challenge: | Vision-language models integrate textual and visual information, enabling them to process visual inputs and generate predictions. |
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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. |
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MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |
| Approach: | They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities. |
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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. |
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The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)
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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai
| Challenge: | Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning . |
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What’s Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning (2026.eacl-long)
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| Challenge: | Existing benchmarks often include a mixture of reasoning questions, making it difficult to truly assess VLMs’ causal reasoning abilities. |
| Approach: | They propose two new benchmarks specifically designed to isolate and rigorously evaluate VLMs’ causal reasoning abilities. |
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Puzzled by Puzzles: When Vision-Language Models Can’t Take a Hint (2025.emnlp-main)
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| Challenge: | rebus puzzles encode language through imagery, spatial arrangement, and symbolic substitution. |
| Approach: | They construct a benchmark of rebus puzzles in english language to test their ability to interpret and solve them. |
| Outcome: | The proposed model performs well on a set of english-language rebus puzzles. |