构建长期记忆的家居代理评估基准，测试其跨时记忆与行动能力。
WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents
- 设计时序扩展的家居任务追踪数据集，包含对话、动作与状态变化。
- 在动态环境中，记忆失效与状态覆盖仍是主要挑战，计划生成困难。
- 提出可见性感知记忆框架，适合长期交互式智能体研发者参考。
为在真实家庭中长时间辅助人类，具身智能体需记住用户习惯、世界状态及过往交互。现有长期记忆评测多聚焦语言检索与问答，而具身评测通常仅关注短时任务执行，未检验动态环境中长期记忆的实际应用。我们提出WorldLines，一个面向长期具身家庭协助的项目驱动型基准，构建包含对话、动作、执行反馈、物体与设备状态变化的时序扩展家庭轨迹，并转化为带证据链的问答与具身任务规划样本。进一步提出ObsMem，一种基于观察者的记忆框架，维护可见性感知的记忆与行动原生状态轨迹，支持状态感知决策。实验揭示在部分可观测性、状态被覆盖及长期记忆转化为具身计划方面仍存在持续挑战，而ObsMem提供了更优的参考架构。
原文摘要 · Abstract (English)
To assist humans over extended periods in real homes, embodied agents must remember user routines, world states, and past interactions. Existing long-term memory benchmarks mainly evaluate language-centric retrieval and question answering, while embodied benchmarks often focus on short-horizon task execution without testing long-term memory use in dynamic environments. We introduce WorldLines, a project-driven benchmark for long-horizon embodied household assistance. It constructs temporally extended household traces with dialogues, actions, execution feedback, object and device state changes, and converts them into evidence-linked samples for Memory QA and Embodied Task Planning. We further propose ObsMem, an observer-grounded memory framework that maintains visibility-aware memories and action-native state trails for state-aware decisions. Experiments reveal persistent challenges in partial observability, overwritten world states, and translating long-term memory into embodied plans, while ObsMem offers a stronger reference architecture for this setting.
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