SIMLIFE: Pattern Understanding for Long-Horizon Human-Agent Partnership

2026-09-16

AI Summary

研究通过构建SimLife平台模拟长期家庭生活，评估模型在理解长期行为模式上的能力，揭示了现有模型在深入规则理解和适应变化方面的不足。

Abstract

Understanding humans over long horizons requires agents to infer not only what people need in the moment, but also how routines form, why they repeat, and when they change. We introduce SimLife, a scalable platform for simulating long-term household life with rich visual observations, ground-truth action logs, and synthetic dialogues with audio. Built on SimLife, SimLife-BP evaluates long-context pattern understanding: the ability to infer latent behavioral rules from weeks or months of everyday observations. The benchmark contains 106 episodes averaging 15.49 hours and 38.57 in-game days, and 1,439 question-answer pairs. Each task probes direct, counterfactual, noisy, and inverse reasoning under different levels of rule hints. Evaluating frontier models and architectures, we find that current models often achieve surface-level prediction without comprehensive rule understanding, rely on frequency-based heuristics rather than if-then reasoning over evidence, and struggle to adapt when behavioral patterns change. These findings suggest that long-context pattern understanding remains a major bottleneck for future embodied agents, while SimLife opens a broader space for studying memory, personalization, adaptation, and long-horizon planning in everyday human-AI interaction.

Problem

• long-horizon human-agent partnership
• pattern understanding
• behavioral rules

Innovation

• long-horizon pattern understanding
• SimLife platform
• behavioral rules inference