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

TL;DR - IBA-Bench is a new benchmark testing whether personalized LLM agents can act on implicit user preferences inferred from messy longitudinal interaction histories, not just recall them. It targets the "knowledge-to-action gap" that existing personalization benchmarks miss.

• Prior benchmarks rely on static preference snapshots, fixed interaction logs, or QA over predefined user profiles — none evaluate preference-conditioned task execution.
• IBA-Bench is built from longitudinal histories containing noise, implicit cues, and temporal inconsistencies, spanning nine application domains.
• The authors propose IBA-Agent, which reconciles conflicting priorities via broad retrieval plus trajectory-level alignment.
• Reported results: state-of-the-art LLM agents still struggle with effective personalization, while IBA-Agent substantially improves behavioral alignment in complex scenarios.