Contribute to the benchmark

Oct 13, 2025 by wbrum.org 3 views

Request to Add a New Difficult Reasoning Benchmark MM-IQ for Assessing MLLM's Reasoning Abilities

We are writing to request the addition of a new systematic benchmark, MM-IQ, to your repository for assessing the reasoning abilities of Multimodal Large Language Models (MLLMs). The MM-IQ benchmark is designed to evaluate the human-like abstraction and reasoning capabilities of MLLMs, and we believe it would be a valuable addition to your collection of benchmarks.

Multimodal Large Language Models (MLLMs) have made significant progress in recent years, demonstrating impressive capabilities in understanding and generating human-like text. However, evaluating the reasoning abilities of MLLMs remains a challenging task. Current benchmarks often focus on specific tasks such as question-answering or text classification, but they may not fully capture the complex reasoning abilities required for human-like intelligence.

The MM-IQ benchmark, introduced in the paper "MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models" [1], is designed to address this gap. The benchmark consists of a set of challenging reasoning tasks that require MLLMs to demonstrate human-like abstraction and reasoning capabilities. The tasks are designed to be difficult, yet solvable, and are intended to push the limits of current MLLMs.

Key Features of MM-IQ

Multimodal inputs: MM-IQ tasks involve multimodal inputs, including text, images, and videos, which require MLLMs to integrate information from multiple sources.
Abstract reasoning: MM-IQ tasks require MLLMs to perform abstract reasoning, including analogical reasoning, causal reasoning, and counterfactual reasoning.
Human-like intelligence: MM-IQ tasks are designed to evaluate the human-like intelligence of MLLMs, including their ability to reason, understand, and generate human-like text.

The MM-IQ benchmark uses a range of evaluation metrics to assess the performance of MLLMs. These metrics include:

Accuracy: The accuracy of the MLLM's response compared to the human-annotated answer.
F1-score: The F1-score of the MLLM's response compared to the human-annotated answer.
Human evaluation: Human evaluators assess the quality and coherence of the MLLM's response.

The MM-IQ benchmark is implemented in Python and is available on GitHub [2]. The code includes a range of tools and utilities for evaluating MLLMs on the MM-IQ tasks.

The MM-IQ leaderboard [3] provides a ranking of MLLMs based on their performance on the MM-IQ tasks. The leaderboard is updated regularly and provides a snapshot of the current state-of-the-art in MLLM research.

We believe that the MM-IQ benchmark is an important addition to your repository for assessing the reasoning abilities of MLLMs. The benchmark provides a challenging and comprehensive evaluation of MLLMs' human-like abstraction and reasoning capabilities. We hope that you will consider adding the MM-IQ benchmark to your repository and look forward to seeing the impact it will have on the field of MLLM research.

[1] Cai, H., Yang, Y., & Hu, W. (2025). MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models. arXiv preprint arXiv:2502.00698.

[2] AceCHQ. (n.d.). MM-IQ. Retrieved from https://github.com/AceCHQ/MMIQ/tree/main

[3] AceCHQ. (n.d.). MM-IQ Leaderboard. Retrieved from https://acechq.github.io/MMIQ-benchmark/#leaderboard
MM-IQ Q&A: Frequently Asked Questions about the MM-IQ Benchmark

The MM-IQ benchmark is a challenging and comprehensive evaluation of Multimodal Large Language Models' (MLLMs) human-like abstraction and reasoning capabilities. In this Q&A article, we will address some of the most frequently asked questions about the MM-IQ benchmark.

A: The MM-IQ benchmark is a systematic evaluation of MLLMs' human-like abstraction and reasoning capabilities. It consists of a set of challenging reasoning tasks that require MLLMs to demonstrate human-like intelligence.

A: The MM-IQ benchmark has several key features, including:

Multimodal inputs: MM-IQ tasks involve multimodal inputs, including text, images, and videos, which require MLLMs to integrate information from multiple sources.
Abstract reasoning: MM-IQ tasks require MLLMs to perform abstract reasoning, including analogical reasoning, causal reasoning, and counterfactual reasoning.
Human-like intelligence: MM-IQ tasks are designed to evaluate the human-like intelligence of MLLMs, including their ability to reason, understand, and generate human-like text.

A: The MM-IQ benchmark uses a range of evaluation metrics to assess the performance of MLLMs. These metrics include:

Accuracy: The accuracy of the MLLM's response compared to the human-annotated answer.
F1-score: The F1-score of the MLLM's response compared to the human-annotated answer.
Human evaluation: Human evaluators assess the quality and coherence of the MLLM's response.

A: To participate in the MM-IQ benchmark, you can:

Submit your model: Submit your MLLM to the MM-IQ leaderboard by following the instructions on the MM-IQ website.
Evaluate your model: Evaluate your MLLM on the MM-IQ tasks using the provided code and tools.
Contribute to the benchmark: Contribute to the development of the MM-IQ benchmark by providing feedback, suggestions, or new tasks.

A: Participating in the MM-IQ benchmark has several benefits, including:

Improved model performance: Participating in the MM-IQ benchmark can help you improve the performance of your MLLM by identifying areas for improvement.
Increased visibility: Participating in the MM-IQ benchmark can increase the visibility of your MLLM and your research.
Contribution to the community: Participating in the MM-IQ benchmark can contribute to the development of the MLLM community and the advancement of MLLM research.

A: You can get more information about the MM-IQ benchmark by:

Visiting the MM-IQ website: Visit the MM-IQ website for more information about the benchmark, including the tasks, evaluation metrics, and submission instructions.
Contacting the authors: Contact the authors of the MM-IQ paper for more information about the benchmark and to ask questions.
Joining the MM-IQ community: Join the MM-IQ community on GitHub or other platforms to connect with other researchers and developers working on the MM-IQ benchmark.

The MM-IQ benchmark is a challenging and comprehensive evaluation of MLLMs' human-like abstraction and reasoning capabilities. We hope that this Q&A article has provided you with more information about the MM-IQ benchmark and has answered some of your questions. If you have any further questions or would like to participate in the MM-IQ benchmark, please don't hesitate to contact us.