  People have to remember an ever-expanding volume of information. Wearables
that use information capture and retrieval for memory augmentation can help but
can be disruptive and cumbersome in real-world tasks, such as in social
settings. To address this, we developed Memoro, a wearable audio-based memory
assistant with a concise user interface. Memoro uses a large language model
(LLM) to infer the user's memory needs in a conversational context,
semantically search memories, and present minimal suggestions. The assistant
has two interaction modes: Query Mode for voicing queries and Queryless Mode
for on-demand predictive assistance, without explicit query. Our study of
(N=20) participants engaged in a real-time conversation demonstrated that using
Memoro reduced device interaction time and increased recall confidence while
preserving conversational quality. We report quantitative results and discuss
the preferences and experiences of users. This work contributes towards
utilizing LLMs to design wearable memory augmentation systems that are
minimally disruptive.
