{"ID":2846564,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01340","arxiv_id":"2511.01340","title":"$\\left|\\,\\circlearrowright\\,\\boxed{\\text{BUS}}\\,\\right|$: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles","abstract":"Understanding Rebus Puzzles (Rebus Puzzles use pictures, symbols, and letters to represent words or phrases creatively) requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, multi-step reasoning, image-based wordplay, etc., making this a challenging task for even current Vision-Language Models. In this paper, we present $\\left|\\,\\circlearrowright\\,\\boxed{\\text{BUS}}\\,\\right|$, 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, finance, entertainment, etc. We also propose $RebusDescProgICE$, a model-agnostic framework which uses a combination of an unstructured description and code-based, structured reasoning, along with better, reasoning-based in-context example selection, improving the performance of Vision-Language Models on $\\left|\\,\\circlearrowright\\,\\boxed{\\text{BUS}}\\,\\right|$ by $2.1-4.1\\%$ and $20-30\\%$ using closed-source and open-source models respectively compared to Chain-of-Thought Reasoning.","short_abstract":"Understanding Rebus Puzzles (Rebus Puzzles use pictures, symbols, and letters to represent words or phrases creatively) requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, multi-step reasoning, image-based wordplay, etc., making this a challenging task for even current Visio...","url_abs":"https://arxiv.org/abs/2511.01340","url_pdf":"https://arxiv.org/pdf/2511.01340v1","authors":"[\"Trishanu Das\",\"Abhilash Nandy\",\"Khush Bajaj\",\"Deepiha S\"]","published":"2025-11-03T08:42:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}