arXiv:2607.02374v1 Announce Type: new Abstract: 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 mea
The proliferation of personalized AI models and the increasing complexity of their internal reasoning necessitates new methods for evaluating their integrity and consistency.
Understanding how personalization 'drifts' the reasoning of large language models is crucial for ensuring their reliability, ethical deployment, and trust in AI-driven decision-making.
The ability to quantify reasoning drift using frameworks like DRIFTLENS offers a new lens for evaluating AI systems beyond simple output accuracy, highlighting potential biases introduced by personalization.
- · AI ethics researchers
- · AI developers
- · Users of personalized AI
- · Auditors of AI systems
- · Developers ignoring reasoning integrity
- · Unaccountable AI systems
Increased scrutiny and demand for transparency in personalized AI systems.
Development of new architectural approaches to mitigate reasoning drift in LLMs.
Enhanced trust and broader adoption of highly personalized AI applications due to improved reliability and explainability.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI