LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation
arXiv:2603.12152v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized assistants remain misaligned with real-world user-assistant interactions, failing to capture the complexity of external contexts and users' cognitive states. To bridge this gap, we propose LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model within physical environments for coherent life trajectories generation, and simulates intention-driven user interactive behaviors. Based on LifeSim, we introduce LifeSim-Eval, a comprehensive benchmark for multi-scenario, long-horizon personalized assistance. LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, and adopts a multi-turn interactive method to assess models' abilities to complete explicit and implicit intentions, recover user profiles, and produce high-quality responses. Unde
arXiv:2603.12152v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized assistants remain misaligned with real-world user-assistant interactions, failing to capture the complexity of external contexts and users' cognitive states. To bridge this gap, we propose LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model within physical environments for coherent life trajectories generation, and simulates intention-driven user interactive behaviors. Based on LifeSim, we introduce LifeSim-Eval, a comprehensive benchmark for multi-scenario, long-horizon personalized assistance. LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, and adopts a multi-turn interactive method to assess models' abilities to complete explicit and implicit intentions, recover user profiles, and produce high-quality responses. Under both single-scenario and long-horizon settings, our experiments reveal that current LLMs face significant limitations in handling implicit intention and long-term user preference modeling.
Executive Summary
This article presents LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model, simulating intention-driven user interactive behaviors in physical environments. The LifeSim-Eval benchmark assesses the capabilities of large language models (LLMs) in handling implicit intention and long-term user preference modeling. The authors conduct experiments under both single-scenario and long-horizon settings, revealing significant limitations in current LLMs. The proposed framework bridges the gap between existing benchmarks and real-world user-assistant interactions, providing a comprehensive evaluation of personalized assistants. The introduction of LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, adopting a multi-turn interactive method to evaluate models' abilities. The study contributes to the advancement of universal AI assistants and has significant implications for the development of intelligent systems.
Key Points
- ▸ LifeSim models user cognition through the BDI model, simulating intention-driven user interactive behaviors in physical environments.
- ▸ LifeSim-Eval benchmark assesses the capabilities of LLMs in handling implicit intention and long-term user preference modeling.
- ▸ The experiments reveal significant limitations in current LLMs under both single-scenario and long-horizon settings.
Merits
Strength
The proposed framework bridges the gap between existing benchmarks and real-world user-assistant interactions, providing a comprehensive evaluation of personalized assistants.
Accuracy
The LifeSim-Eval benchmark covers 8 life domains and 1,200 diverse scenarios, adopting a multi-turn interactive method to evaluate models' abilities.
Demerits
Limitation
The study relies on the Belief-Desire-Intention (BDI) model, which may not fully capture the complexity of human cognition and behavior.
Scope
The study focuses on personalized assistants, but the applicability of the proposed framework to other domains, such as healthcare or finance, is unclear.
Expert Commentary
The article presents a significant contribution to the field of human-computer interaction and AI development. The proposed framework, LifeSim, offers a comprehensive evaluation of personalized assistants, highlighting the limitations of current LLMs in handling implicit intention and long-term user preference modeling. The study's findings have significant implications for the development of universal AI assistants and the design of intelligent systems. However, the reliance on the BDI model and the limited scope of the study are notable limitations. Nevertheless, the proposed framework has the potential to bridge the gap between existing benchmarks and real-world user-assistant interactions, providing a more accurate evaluation of personalized assistants.
Recommendations
- ✓ Future studies should investigate the applicability of the proposed framework to other domains, such as healthcare or finance.
- ✓ The development of more sophisticated models of human cognition and behavior is necessary to fully capture the complexity of human interaction with AI-powered systems.