This repository is a curated collection of papers, code, datasets, and benchmarks focused on multimodal reasoning within Large Language Models (MLLMs). It serves researchers and developers aiming to advance MLLM capabilities in areas like commonsense, spatial, temporal, mathematical, and visual reasoning, providing a centralized resource for the latest advancements and tools.

How It Works

The collection categorizes research by modality (Image, Video, Audio, Omni) and reasoning task (Commonsense, Spatial, Temporal, Math, Chart, Generation, Segmentation, Detection). Each entry typically includes links to papers, code repositories, and datasets, allowing users to explore specific MLLM reasoning techniques and their implementations. The emphasis is on identifying and cataloging novel approaches, particularly those leveraging reinforcement learning (RL) and chain-of-thought (CoT) prompting.

Quick Start & Requirements

This repository is a collection of links and does not have a direct installation or execution command. Users will need to follow the links provided for individual papers and projects to access their specific requirements, which may include Python environments, deep learning frameworks (PyTorch, TensorFlow), specific hardware (GPUs), and large datasets.

Highlighted Details

• Extensive coverage of Reinforcement Learning (RL) and Chain-of-Thought (CoT) techniques applied to MLLM reasoning.
• Categorization across Image, Video, Audio, and Omnimodal MLLMs, with detailed sub-categories for specific reasoning tasks.
• Includes a dedicated section for benchmarks evaluating various aspects of multimodal reasoning.
• Features a list of open-source projects, many related to the "R1" paradigm for reasoning enhancement.

Maintenance & Community

The repository is actively maintained, with recent entries dated as of July 2024. It encourages community contributions and provides a link for contribution guidelines.

Licensing & Compatibility

The repository itself is a collection of links and does not specify a license. Individual projects linked within will have their own licenses, which users must consult for compatibility and usage restrictions.

Limitations & Caveats

As a curated list, the repository does not host any code or data directly. Users must navigate to external links for each resource, and the availability or maintenance status of those external resources is not guaranteed by this collection.