One of the biggest selling points for modern AI systems is their ability to adapt to users. Every time an AI assistant takes on a task for you, it’s also adapting to your style and preferences, which are incorporated as context for future tasks. With more context and a better understanding of the user, the model can get better every time you use it — or at least that’s the theory.
New research suggests that models’ adaptive abilities might be a mixed blessing. On Wednesday, researchers at the AI company Writer published two papers showing how popular memory systems can make models worse, pulling them toward misconceptions or misunderstandings introduced by the user. As user input fills up more of the model’s context window, the model grows more sycophantic — and less committed to accuracy.
“We wanted to be able to characterize how often a model is going to be usefully paying attention to user preferences versus giving a potentially wrong answer,” said Dan Bikel, Writer’s head of AI, who worked on the papers. As Bikel told TechCrunch, “with every additional storing of user preferences and retrieving of them, you’re running an increasing risk.”
In one variation, researchers tested AI models by recording that a user’s favorite book was Station Eleven, then asking the model to name a best-selling dystopian book. Models became far more likely to name Station Eleven in their response, even though the question didn’t relate to the user’s favorite book. The tendency increased when using memory compression tools like Mem0 and Zep.
As the paper puts it, “all memory systems fundamentally struggle to distinguish relevant context from irrelevant anchor …