This Perspective proposes three principles—substrate proportionality, temporal defeasibility, and auditable continuity—so memory-enabled LLM agents can decide which substrate a new piece of evidence belongs in, or whether to write nothing at all.
This Perspective addresses LLM agents that accumulate state across sessions, tools, users, and changing environments, and proposes a framework of controlled knowledge updating: treating an update as a decision to select the least invasive substrate sufficient for the claim's scope—among context, external memory, model parameters, activation states, and tool or workflow definitions—including the option not to write persistent state, organized by three principles (substrate proportionality, temporal defeasibility, auditable continuity) and motivating evaluation criteria covering update selection, temporal consistency, interference, reversibility, efficiency, and robustness, together with a four-phase minimal benchmark unit.