Researchers have identified a previously overlooked problem in agentic memory, which is a type of artificial intelligence that involves retaining and retrieving information. They found that when this AI has limited resources, it can inadvertently discard important information before even trying to retrieve it. This occurs through 'structurally indirect prerequisite eviction,' where upstream blocks related to the query are removed under budget constraints. The study proposes a solution called Dependency-aware Semantic Garbage Collection (DSGC), which improves retention rates from 0.03 to 0.90 in some cases. --- Why it matters: This matters because it highlights a critical failure mode in agentic memory, which can have significant implications for the development of more efficient and effective AI systems. Understanding this problem is essential for improving the performance and reliability of these systems. Source: https://arxiv.org/abs/2608.20400