Titans: Learning to Memorize at Test Time

The paper introduces a novel neural long-term memory module that learns to memorize and forget at test time. It addresses the challenges of existing models like RNNs and Transformers in handling long-range dependencies by incorporating dynamic memory updates based on surprise and forgetting mechanisms.
Machine Learning
Artificial Intelligence
Neural Networks
Memory Modules
Published

January 18, 2025

The key takeaways for engineers/specialists are that effective memory models need to be dynamic, surprise-driven, and have mechanisms to forget the past. The research showcases how incorporating a neural long term memory module that continuously learns at test time can lead to higher performance in language modeling, common-sense reasoning, needle-in-a-haystack tasks, DNA modeling, and time-series forecasting. By introducing the Titans architecture, the paper provides a framework for effectively integrating such memory modules into various tasks.

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