Asynchronous Multi-Modal Model

# Asynchronous Multi-Modal Model ## Description This project explores asynchronous reasoning using rotary embeddings to improve the interactivity of multi-modal models. The goal is to enhance real-time applications such as video analysis and robotic control by enabling more efficient processing of diverse data streams. ## Research Hypothesis Asynchronous reasoning using rotary embeddings can be extended to improve the interactivity of multi-modal models, enhancing real-time applications like video analysis and robotic control. ## Implementation Approach The project involves developing a multi-modal model with rotary embeddings for asynchronous reasoning. The model will be tested on tasks requiring real-time decision-making, such as video stream analysis and robotic navigation, and compared against baseline models. ## Setup Instructions 1. Clone the repository: ```bash git clone https://github.com/yourusername/asynchronous_multimodal.git cd asynchronous_multimodal ``` 2. Install the required packages: ```bash pip install -r requirements.txt ``` 3. Prepare the datasets (Kinetics-700, COCO, etc.) and update the data paths in the configuration files. ## Usage Examples - To train the model: ```bash python src/train.py ``` - To evaluate the model: ```bash python src/evaluate.py ``` ## Expected Results The model is expected to demonstrate improved response time and accuracy in real-time multi-modal tasks compared to baseline models. ## References - [Asynchronous Reasoning: Training-Free Interactive Thinking LLMs](http://arxiv.org/abs/2512.10931v1)