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The recent advent of large language models (LLM) has resulted inhigh-performing conversational agents such as chatGPT. These agents mustremember key information from an ongoing conversation to provide responses thatare contextually relevant to the user. However, these agents have limitedmemory and can be distracted by irrelevant parts of the conversation. Whilemany strategies exist to manage conversational memory, users currently lackaffordances for viewing and controlling what the agent remembers, resulting ina poor mental model and conversational breakdowns. In this paper, we presentMemory Sandbox, an interactive system and design probe that allows users tomanage the conversational memory of LLM-powered agents. By treating memories asdata objects that can be viewed, manipulated, recorded, summarized, and sharedacross conversations, Memory Sandbox provides interaction affordances for usersto manage how the agent should `see' the conversation.

Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents