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 ## Benchmarks for Evaluation
 | Name | Paper | Link | Notes |
 |:-----|:-----:|:----:|:-----:|
+| **VisuLogic** | [VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models](https://arxiv.org/abs/2504.15279) | [Link](https://visulogic-benchmark.github.io/VisuLogic/) | A benchmark for evaluating visual reasoning, enhanced by additional training datasets from similar domains, corresponding code and models.  |
 | **Inst-IT Bench** | [Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning](https://arxiv.org/pdf/2412.03565) | [Link](https://github.com/inst-it/inst-it) | A benchmark to evaluate fine-grained instance-level understanding in images and videos |
 | **M<sup>3</sup>CoT** | [M<sup>3</sup>CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought](https://arxiv.org/pdf/2405.16473) | [Link](https://github.com/LightChen233/M3CoT) | A multi-domain, multi-step benchmark for multimodal CoT |
 | **MMGenBench** | [MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective](https://arxiv.org/pdf/2411.14062) | [Link](https://github.com/lerogo/MMGenBench) | A benchmark that gauges the performance of constructing image-generation prompt given an image |