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A new technique from Zhejiang University and Alibaba Group gives large language model (LLM) agents a dynamic memory, making them more efficient and effective at complex tasks. The technique, called Memp, provides agents with a “procedural memory” that is continuously updated as they gain experience, much like how humans learn from practice.
Memp creates a lifelong learning framework where agents don’t have to start from scratch for every new task. Instead, they become progressively better and more efficient as they encounter new situations in real-world environments, a key requirement for reliable enterprise automation.
The case for procedural memory in AI agents
LLM agents hold promise for automating complex, multi-step business processes. In practice, though, these long-horizon tasks can be fragile. The researchers point out that unpredictable events like network glitches, user interface changes or shifting data schemas can derail the entire process. For current agents, this often means starting over every time, which can be time-consuming and costly.
Meanwhile, many complex tasks, despite surface differences, share deep structural commonalities. Instead of relearning these patterns every time, an agent should be able to extract and reuse its experience from past successes and failures, the researchers point out. This requires a specific “procedural memory,” which in humans is the long-term memory responsible for skills like typing or riding a bike, that become automatic with practice.
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Starting from scratch (top) vs using procedural memory (bottom) (source: arXiv)
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