Conversational agents perform better with selective memory management than unlimited retention; a relevance-guided forgetting framework improves long-horizon reasoning while reducing false memories and context bloat.
This paper tackles a key problem in conversational AI: agents need to remember past interactions to reason coherently, but storing everything causes performance to degrade and creates false memories. The authors propose a smart forgetting system that decides which memories to keep based on relevance, recency, and frequency—like a selective filing system for an agent's brain.