Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories into reusable memory ...

A research team from Zhejiang University and Alibaba Group has introduced Memp, a framework that gives large language model (LLM) agents a form of procedural memory designed to make them more ...

LLM agents have become powerful enough to handle complex tasks, ranging from web research and report generation to data analysis and multi-step software workflows. However, they struggle with ...

AI agent memory comprises multiple layers, each serving a distinct role in shaping the agent’s behavior and decision-making. By dividing memory into different types, it is better to understand and ...

Imagine having a conversation with someone who remembers every detail about your preferences, past discussions, and even the nuances of your personality. It feels natural, seamless, and, most ...

Memory formation involves complex processes within the brain. When you experience something, like placing your keys on a table, neurons in the brain activate in a specific pattern. The strength of ...

Imagine interacting with an AI assistant that not only remembers your preferences but also learns from past conversations to improve its responses over time. Whether it’s recalling your favorite ...

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