arXiv: cs.AI preprintsInternational6 November 2026
Axis-LoRA: Probing and Adapting Embedding Models for Personal-Memory Retrieval
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arXiv:2611.07820v1 Announce Type: cross Abstract: As LLMs are increasingly asked questions about a user's personal experiences, retrieval must distinguish memories by time, place, person, and event. Current personal-memory pipelines rely on frozen dense embedding models to embed memories for retrieval, but we find that these models do not capture the four axes well. Swapping a memory's date or place barely shifts its embedding, and a trained projection on top of the frozen embedding does not recover the missing signal. The problem lives inside the model, where each axis is encoded by a distinc
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