GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs

For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning days, and it will eventually lose the thread. Engineers refer to this phenomenon as “context rot,” and it has quietly become one of the most significant obstacles to building AI agents that can function reliably in the real world.

A research team from China and Hong Kong believes it has created a solution to context rot. Their new paper introduces general agentic memory (GAM), a system built to preserve long-horizon information without overwhelming the model. The core premise is simple: Split memory into two specialized roles, one that captures everything, another that retrieves exactly the right things at the right moment.

Early results are encouraging, and couldn’t be better timed. As the industry moves beyond prompt engineering ...