Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI
Long-term memory is becoming a core substrate for personalized AI, yet most systems still represent personalization as discrete records in a largely static latent space, accessed under one global similarity notion. For data mining, this creates a mismatch: the evidence is a temporal event stream, while the dominant abstraction is a searchable record set. We argue that long-horizon personalization should instead model memory as a user-specific dynamical state space with locally heterogeneous geometry. Geometry here is a computational language, not a literal claim about cognition: it captures stable versus volatile regions, variable-rate drift, heterogeneous neighborhoods, and uncertainty a...
| Authors | Jiahong Liu, Wenhao Yu, Zexuan Qiu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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