MemAgent

It uses a structure that dynamically updates a fixed-length “memory” while processing the input in segments. Inspired by how we take notes and compress key information when reading long documents, MemAgent is an agent-based approach that can process arbitrarily long text with linear complexity ($O(n)$). The model actively overwrites and updates its memory (Overwrite) as a strategy.

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL based Memory Agent

https://memagent-sialab.github.io/

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation...

https://arxiv.org/abs/2507.02259

MemAgent

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