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 ## Benchmarks for Evaluation
 | Name | Paper | Link | Notes |
 |:-----|:-----:|:----:|:-----:|
+| **VCRBench** | [VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models](https://arxiv.org/pdf/2505.08455) | [Link](https://github.com/pritamqu/VCRBench) | A benchmark to evaluate video-based multi-step causal reasoning capabilities of MLLMs/LMMs. |
 | **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 |