WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2606.18847 [cs.AI]

(or arXiv:2606.18847v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.18847 Focus to learn more arXiv-issued DOI via DataCite

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