DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
Personalization changes what a model says to a user; we show that it can also change the reasoning trajectory used to justify the response. Modern LLMs personalize interactions by storing user attributes, preferences, and prior context, then injecting this information into future prompts. We study whether such memory reshapes reasoning on open-ended questions where no single ground-truth answer exists. To quantify this effect, we introduce DRIFTLENS, a ground-truth-free framework that maps each expressed reasoning step to a value category and measures divergence between a question's no-memory trajectory and its trajectory under injected user-attribute memory. We first validate that DRIFTL...
| Authors | Xi Fang, Weijie Xu, Yingqiang Ge et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 14 July 2026 |
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