From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents

Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful. Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles. Such designs fail to capture the complexity of evolving user preferences and neglect preference-conditioned task execution-a discrepancy we term as

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ethics.ai (3 August 2026), “From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents,” evidence record 15883, https://ethics.ai/record/15883 (originally published by arXiv).

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