Proud of the team. 297 agent memory systems, 82 benchmarks, and 5 modalities. … · @GenAI_is_real · TwKit

@GenAI_is_real

Proud of the team. 297 agent memory systems, 82 benchmarks, and 5 modalities. Really impressive work by Minghao and the team! I've become increasingly interested in agent memory, especially as we move toward continuously interactive AI systems. An agent that can see, listen, speak, and operate software will accumulate enormous amounts of experience over time. But simply storing all those interactions is unlikely to be enough. The harder question is how an agent decides what is worth remembering, how to represent it, when to retrieve it, and how to update its memory as new experiences arrive. What I particularly appreciate about this survey is its distinction between static memory reuse, dynamic memory updates, and self-evolving memory management. The last category is especially interesting: an agent could potentially improve not only its decisions using past experience, but also the policies governing how it remembers and learns. I think this will become an increasingly important research direction for Personal Agents and Interactive Intelligence. Memory needs to be evaluated through its impact on downstream behavior and long-term task performance, rather than retrieval accuracy alone. Congratulations to Minghao and all the collaborators! Looking forward to reading more.

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