A new hierarchical framework aggregates user behavioral logs into intent memories to generate evidence-grounded personas. Researchers used a groupwise extension of Direct Preference Optimization to prioritize cluster cohesion and truthfulness. This approach reduces noise in user modeling. Practitioners can now derive more accurate, verifiable user profiles from interleaved activity logs.