Get the latest tech news
How procedural memory can cut the cost and complexity of AI agents
Memp takes inspiration from human cognition to give LLM agents "procedural memory" that can adapt to new tasks and environments.
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. To test the framework, the team implemented Memp on top of powerful LLMs like GPT-4o, Claude 3.5 Sonnet and Qwen2.5, evaluating them on complex tasks like household chores in the ALFWorld benchmark and information-seeking in TravelPlanner. This would make the entire learning loop more scalable and robust, marking a critical step toward building the resilient, adaptable and truly autonomous AI workers needed for sophisticated enterprise automation.
Or read this on Venture Beat
