Beyond Static Evaluation: Rethinking the Assessment of Personalized Agent Adaptability in Information Retrieval

2025-10-04

AI Summary

Existing evaluations of personalized AI agents for information retrieval rely heavily on static benchmarks, failing to capture the temporal evolution of user needs and long-term interactive adaptability. To address this, we propose a novel dynamic, interaction-aware paradigm for longitudinal evaluation: (1) a time-varying preference modeling-based persona simulator; (2) a reference-interview-driven protocol for structured preference elicitation; and (3) cross-session behavioral adaptability metrics. Our method integrates large language model–driven user simulation, preference elicitation, and longitudinal interaction analysis. We conduct empirical evaluation in the e-commerce search setting using the PersonalWAB dataset. This work is the first to systematically define and empirically validate an evaluation framework for personalized agents’ *sustained adaptability*—a core capability for long-term user alignment. It establishes a reproducible, scalable theoretical and practical foundation for user-centered, longitudinal interaction optimization.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Personalized AI agents are becoming central to modern information retrieval, yet most evaluation methodologies remain static, relying on fixed benchmarks and one-off metrics that fail to reflect how users' needs evolve over time. These limitations hinder our ability to assess whether agents can meaningfully adapt to individuals across dynamic, longitudinal interactions. In this perspective paper, we propose a conceptual lens for rethinking evaluation in adaptive personalization, shifting the focus from static performance snapshots to interaction-aware, evolving assessments. We organize this lens around three core components: (1) persona-based user simulation with temporally evolving preference models; (2) structured elicitation protocols inspired by reference interviews to extract preferences in context; and (3) adaptation-aware evaluation mechanisms that measure how agent behavior improves across sessions and tasks. While recent works have embraced LLM-driven user simulation, we situate this practice within a broader paradigm for evaluating agents over time. To illustrate our ideas, we conduct a case study in e-commerce search using the PersonalWAB dataset. Beyond presenting a framework, our work lays a conceptual foundation for understanding and evaluating personalization as a continuous, user-centric endeavor.

Problem

• Assessing AI agent adaptability to evolving user needs in information retrieval
• Moving beyond static benchmarks to dynamic longitudinal interaction evaluation
• Developing continuous assessment methods for personalized agent performance improvement

Innovation

• Persona-based user simulation with evolving preference models
• Structured elicitation protocols for contextual preference extraction
• Adaptation-aware evaluation mechanisms measuring cross-session improvement