Mixture of Recursion (MoR) {NLP}

Description

Mixture of Recursion (MoR) is an emerging neural network architecture designed to enhance sequence modeling by leveraging recursive computation and dynamic routing. MoR architectures combine the strengths of recursive neural networks with the scalability of mixture models, enabling efficient handling of hierarchical and structured data.

• Recursive Computation: Utilizes recursive structures to process sequences or trees, allowing the model to capture hierarchical relationships and compositional patterns in data.
• Dynamic Expert Selection: Similar to Mixture of Experts, MoR employs a gating mechanism to dynamically select among multiple recursive modules (experts) for each input, promoting specialization and efficient computation.
• Efficient Hierarchical Modeling: Excels at tasks requiring deep compositional reasoning, such as parsing, code analysis, and language understanding, by recursively combining information at multiple levels of abstraction.
• Scalable and Flexible: Supports sparse activation and modular design, making it suitable for large-scale models and adaptable to various data modalities, including language, graphs, and structured documents.
• State-of-the-Art Potential: MoR is a promising direction for advancing neural architectures in domains where recursive and hierarchical structures are fundamental.