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

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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.

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

arXiv cs.AI Jiajia Song, Bobo Li, Haiwen Yi, Zibo Ji, Meishan Zhang, Hao Fei, Min Zhang, Mong-Li Lee, Wynne Hsu 2026-08-03 arXiv:2608.02171
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-18 14:27:23.174633 UTC

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.
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