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Co-authored-by: Abhilash Nandy <[email protected]>

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- ### **Re-Bus: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles**
 
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  Understanding Rebus Puzzles requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, and multi-step reasoning, making this a challenging task for current Vision-Language Models. In this paper, we present Re-Bus, a large and diverse benchmark of 1,333 English Rebus Puzzles containing different artistic styles and levels of difficulty, spread across 18 categories such as food, idioms, sports, and entertainment. We also propose REBUSDESCPROGICE, a model-agnostic framework that improves the performance of Vision-Language Models on Re-Bus by combining unstructured and structured reasoning.
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+ # |🔄 🚍| [Re-Bus: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles](https://arxiv.org/abs/2511.01340)
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  Understanding Rebus Puzzles requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, and multi-step reasoning, making this a challenging task for current Vision-Language Models. In this paper, we present Re-Bus, a large and diverse benchmark of 1,333 English Rebus Puzzles containing different artistic styles and levels of difficulty, spread across 18 categories such as food, idioms, sports, and entertainment. We also propose REBUSDESCPROGICE, a model-agnostic framework that improves the performance of Vision-Language Models on Re-Bus by combining unstructured and structured reasoning.
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