MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories

Key points

Instead of storing all video verbatim, it converted footage into first-person text episodes anchored on people and places, leaving memories in a form that is easy to find later.
It solved the problem of similar items competing in retrieval as memories pile up with a time-indexed agentic reader, improving performance without any training.
It beat the strongest training-free baseline by 4.6-12.0%, and optimizing the writer itself with reinforcement learning added another 2.7-5.0%.
The gains held across different writer and reader models, making it a general design not tied to a specific model.

External research summaries. These are not HDATF publications or measured product results.