# Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory Augmentation

arXiv: https://arxiv.org/abs/2403.02135 · HTML: https://mlsanity.com/paper/2403.02135/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 20 of 20)

- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025)
- [Multi-RAG: A Multimodal Retrieval-Augmented Generation System for Adaptive Video Understanding](https://mlsanity.com/paper/2505.23990/) (2025)
- [ProMemAssist: Exploring Timely Proactive Assistance Through Working Memory Modeling in Multi-Modal Wearable Devices](https://mlsanity.com/paper/2507.21378/) (2025)
- [OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering](https://mlsanity.com/paper/2409.08250/) (2024)
- [An Egocentric Vision-Language Model based Portable Real-time Smart Assistant](https://mlsanity.com/paper/2503.04250/) (2025)
- [LLAMAPIE: Proactive In-Ear Conversation Assistants](https://mlsanity.com/paper/2505.04066/) (2025)
- [Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection](https://mlsanity.com/paper/2409.08895/) (2024)
- [VisionClaw: Always-On AI Agents through Smart Glasses](https://mlsanity.com/paper/2604.03486/) (2026)
- [H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions](https://mlsanity.com/paper/2606.09461/) (2026)
- [Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews](https://mlsanity.com/paper/2408.04681/) (2024)
- [Hierarchical Memory Orchestration for Personalized Persistent Agents](https://mlsanity.com/paper/2604.01670/) (2026)
- [SpeechLess: Micro-utterance with Personalized Spatial Memory-aware Assistant in Everyday Augmented Reality](https://mlsanity.com/paper/2602.00793/) (2026)
- [Resonance: Drawing from Memories to Imagine Positive Futures through AI-Augmented Journaling](https://mlsanity.com/paper/2503.24145/) (2025)
- [Memento: Towards Proactive Visualization of Everyday Memories with Personal Wearable AR Assistant](https://mlsanity.com/paper/2601.17622/) (2026)
- [From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality](https://mlsanity.com/paper/2602.03059/) (2026)
- [MemArena: An Ego-Centric Benchmark for On-Device Agentic Personal Memory Assistants at Scale](https://mlsanity.com/paper/2608.02613/) (2026)
- [EgoCITE: Context-Augmented Indexing and Time-Aware Retrieval for Long-Horizon Egocentric Memory](https://mlsanity.com/paper/2608.12627/) (2026)
- ["MeBo Leaves a Piece of You Behind": Designing a Relational Voice-Based Memory Companion for Older Adults](https://mlsanity.com/paper/2609.24706/) (2026)
- [AS2D: Accelerating On-Demand Audio Understanding on Mobile Devices](https://mlsanity.com/paper/2609.37617/) (2026)

## Abstract (arXiv)

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.