arXiv:2605.00696v2 Announce Type: replace-cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets. Classical Bayesian design and computerized adaptive testing typically rely on restrictive parametric assumptions or expensive posterior approximations, limiting their use in heterogeneous, high-dimensional, and cold-start settings. We introduce a persona-induced latent variable model that represents a user's state through membership in a finite dictionary of AI person
The proliferation of AI applications across diverse user bases necessitates more adaptive and personalized querying methods to efficiently extract relevant information.
This research offers a novel approach to understanding user-dependent quantities of interest, crucial for improving the efficiency and effectiveness of AI systems in personalized interactions.
The reliance on restrictive parametric assumptions in traditional adaptive querying is reduced, enabling more robust and scalable solutions for heterogeneous and cold-start environments.
- · AI agents developers
- · Personalized AI services
- · Psychometric assessment platforms
- · Adaptive learning systems
- · One-size-fits-all AI models
- · Traditional query optimization methods
More efficient and accurate personalized AI interactions will become possible.
AI systems will be able to learn individual user preferences and states with fewer data points, accelerating personalization.
This could lead to a new paradigm in human-AI interaction, where systems anticipate needs based on implicit 'persona priors'.
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Read at arXiv cs.CL