Titans Architecture and the MIRAS Framework

Understanding how Titans and MIRAS redefine efficiency and memory in modern AI systems.

Introduction

Traditional Transformers face challenges when scaling to very long sequences due to quadratic attention costs. Titans architecture and the MIRAS framework were introduced to overcome these limitations by combining efficiency, scalability, and continuous learning.

Titans Architecture

Titans is a new AI architecture designed to address the context length problem in Transformers. It merges the speed of recurrent neural networks with the accuracy of Transformers.

• Neural long-term memory module: Helps the model memorize historical context while focusing on current input.
• Efficient scaling: Handles extremely long contexts such as full documents, genome sequences, or extended video streams.
• Real-time adaptability: Updates memory dynamically during inference, enabling continuous adaptation.

MIRAS Framework

"MIRAS provides the foundation for continuously learning AI models with functional long-term memory."

The MIRAS (Memory-Informed Recurrent Attention System) framework complements Titans by enabling continuous learning and efficient handling of massive contexts.

• Continuous learning: Models can keep learning during use, not just during training.
• Efficient memory updates: Allows AI to update its core memory dynamically while processing data streams.
• Balance between compression and detail: Retains fine-grained information across long sequences, unlike RNNs that compress context into a single hidden state.

Why They Matter

Traditional Transformers are powerful but limited by quadratic scaling with sequence length. Titans and MIRAS offer a path toward scalable, memory-efficient, and continuously learning AI.

Applications include:

• Full-document understanding
• Genomic analysis
• Long video or multimodal sequence processing

Conclusion

Titans is the architecture that implements efficient long-term memory, while MIRAS is the framework that formalizes how models can continuously update and use that memory in practice. Together, they represent a significant step forward in building AI systems that are scalable, adaptable, and capable of handling long-term dependencies.

As AI evolves, architectures like Titans and frameworks like MIRAS will shape the future of intelligent systems.