PULSE: Provenance-Aware User-Profile Learning and Selective Evaluation for Long-Term LLM Personalization

Long-term LLM personalization aims to adapt an assistant’s responses to user preferences as they evolve across interactions. Existing approaches typically represent users through latent embeddings, natural-language profiles, or learned summaries to guide future response generation based on interaction history. However, profile updating and utilization often remain implicit, leading to two challenges: situational or uninformative feedback may be incorporated into persistent preferences, while valid but context-irrelevant profile information may distort the evaluation of responses to the current request. Long-term personalization therefore requires determining when recent interactions provide reusable preference evidence that justifies profile revision and when stored information should influence response selection. To address these challenges, we propose PULSE, a provenance-aware framework that explicitly models both profile updating from interaction evidence and selective profile utilization in personalized response evaluation. PULSE selects interaction events containing reusable preference evidence, determines the appropriate update state (no update, local preference refinement, or long-term preference revision), and generates evidence-grounded profile statements. It then converts updated profiles into request-specific criteria and evaluates candidate responses through a criterion-guided personalized residual reward. This residual reward selectively corrects a profile-free anchor reward when the profile provides applicable guidance, preserving general response quality while preventing irrelevant preferences from distorting response selection. Experiments on HorizonBench with both static- and evolving-preference instances show that PULSE achieves effective personalized ranking and preference-change discrimination while maintaining evidence-grounded profiles and limiting the influence of unsupported or context-irrelevant profile information.