arXiv:2606.18847v1 Announce Type: new 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, exec
The accelerating development of advanced AI models and embodied agents necessitates more sophisticated benchmarking to measure progress beyond short-term tasks, pushing for evaluation in complex, real-world scenarios.
This benchmark addresses a critical gap in assessing long-term memory and statefulness in embodied AI, which is essential for developing truly autonomous and helpful agents for practical applications.
The introduction of WorldLines provides a standardized, project-driven benchmark that will allow for more rigorous development and comparison of long-horizon embodied AI, moving beyond language-centric or short-task evaluations.
- · AI research labs developing embodied agents
- · Robotics companies
- · Smart home technology developers
- · AI developers focused on long-term interaction
- · AI projects lacking robust long-term memory solutions
- · Benchmark development focusing solely on short-horizon tasks
Embodied agents will be designed with more advanced memory architectures to perform complex, multi-step tasks over extended periods.
The improved capabilities of these agents could lead to their wider adoption in domestic assistance roles, increasing efficiency and personalized support.
As agents become more integrated into daily life, ethical and privacy concerns regarding long-term data retention and autonomous decision-making in personal environments will intensify.
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Read at arXiv cs.AI