AutoMem: Automated Learning of Memory as a Cognitive Skill

Imagine you're building a system that needs to remember a lot of information over time, like a game character that has to keep track of its inventory and past actions. The challenge is that as the character interacts with the game world, it can easily forget important details or make poor decisions based on its memory. This is especially tricky in long games where decisions made early on can have consequences much later. When memory management isn't handled well, it can lead to mistakes that are hard to trace back, making it difficult to improve the system's performance. This is what's called memory mistakes.

To tackle these issues, the authors propose a new approach where memory management becomes a skill that the model can learn and improve over time. Instead of relying on fixed memory structures, the model can adapt its memory usage based on its experiences. They introduce a framework called AutoMem, which automates the process of reviewing and revising how the model interacts with its memory. This means the model can learn from its own good memory decisions and refine its memory structure without needing constant human oversight.

The results are promising: by focusing solely on optimizing memory management, the model's performance improved significantly, making it competitive with leading systems. This means that for builders, having a system that can autonomously manage its memory could lead to much better performance in complex tasks without needing to change the core task actions.

The approach introduces a novel framework for automating memory management in LLMs, which is a significant extension of existing methods.

The results are supported by experiments across multiple long-horizon tasks, demonstrating solid performance improvements.