Memory Retrieval for Changing Preferences
Long-context dialogue systems must decide both when to access memory and which parts of the interaction history are relevant. Existing approaches typically rely on heuristic retrieval signals or always-on memory usage, failing to account for the changing and potentially inconsistent nature of user preferences. In this work, we propose a unified framework for memory access and selection based on changing preferences. We formulate personalized memory retrieval as identifying which historical turns provide evidence about a user's latent preference state, rather than relying on surface-level semantic similarity. To this end, we quantify the utility of each memory turn using a Bayes factor, de...
| Authors | Yuehan Qin, Li Li, Linxin Song et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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