# REBUS: A Robust Evaluation Benchmark of Understanding Symbols

arXiv: https://arxiv.org/abs/2401.05604 · HTML: https://mlsanity.com/paper/2401.05604/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 9 of 9)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://mlsanity.com/paper/2312.14135/) (2023)
- [Faithful Reasoning Using Large Language Models](https://mlsanity.com/paper/2208.14271/) (2022)
- [Solving and Generating NPR Sunday Puzzles with Large Language Models](https://mlsanity.com/paper/2306.12255/) (2023)

## Built on by (most cited first, 8 of 8)

- [The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles](https://mlsanity.com/paper/2502.01081/) (2025)
- [Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint](https://mlsanity.com/paper/2505.23759/) (2025)
- [Throttling Web Agents Using Reasoning Gates](https://mlsanity.com/paper/2509.01619/) (2025)
- [Reasoning or Pattern Matching? Probing Large Vision-Language Models with Visual Puzzles](https://mlsanity.com/paper/2601.13705/) (2026)
- [Reasoning Riddles: How Explainability Reveals Cognitive Limits in Vision-Language Models](https://mlsanity.com/paper/2510.02780/) (2025)
- [left|,circlearrowright,boxedBUS,right|: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles](https://mlsanity.com/paper/2511.01340/) (2025)
- [Eye-Q: A Multilingual Benchmark for Visual Word Puzzle Solving and Image-to-Phrase Reasoning](https://mlsanity.com/paper/2601.03400/) (2026)
- [Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding](https://mlsanity.com/paper/2608.06501/) (2026)

## Abstract (arXiv)

We propose a new benchmark evaluating the performance of multimodal large language models on rebus puzzles. The dataset covers 333 original examples of image-based wordplay, cluing 13 categories such as movies, composers, major cities, and food. To achieve good performance on the benchmark of identifying the clued word or phrase, models must combine image recognition and string manipulation with hypothesis testing, multi-step reasoning, and an understanding of human cognition, making for a complex, multimodal evaluation of capabilities. We find that GPT-4o significantly outperforms all other models, followed by proprietary models outperforming all other evaluated models. However, even the best model has a final accuracy of only 42%, which goes down to just 7% on hard puzzles, highlighting the need for substantial improvements in reasoning. Further, models rarely understand all parts of a puzzle, and are almost always incapable of retroactively explaining the correct answer. Our benchmark can therefore be used to identify major shortcomings in the knowledge and reasoning of multimodal large language models.