User Preference Modeling for Conversational LLM Agents: Weak Rewards from Retrieval-Augmented Interaction
Large language models are increasingly used as personal assistants, yet most lack a persistent user model, forcing users to repeatedly restate preferences across sessions. We propose Vector-Adapted Retrieval Scoring (VARS), a pipeline-agnostic, frozen-backbone framework that represents each user with long-term and short-term vectors in a shared preference space and uses these vectors to bias retrieval scoring over structured preference memory. The vectors are updated online from weak scalar rewards from users' feedback, enabling personalization without per-user fine-tuning. We evaluate on \textsc{MultiSessionCollab}, an online multi-session collaboration benchmark with rich user preferenc...
| Authors | Yuren Hao, Shuhaib Mehri, ChengXiang Zhai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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