MUSE-Home: Evaluating and Aligning LLM Agents for Multi-User Smart Homes

Smart-home assistants work in shared environments, where residents have different needs, permissions, routines, and observations, while their actions affect one another through shared spaces and devices. We introduce Multi-User Smart-home Evaluation (MUSE-Home), an executable benchmark that studies four challenges created by this setting: multi-user conflict, socially conditioned proactivity, cross-resident environment forensics, and robustness against unnecessary coordination. MUSE-Home contains 6,790 training instances and a manually audited out-of-distribution test set of 721 instances. The best evaluated model achieves only 45.35% task success. We further propose Aggregating Local Interests with Global Need-weighting (MUSE-Align), a judge-based alignment framework that combines observation-bounded resident judgments with full-context weights for current needs. We use this signal to select SFT and RL tasks, and to provide a soft reward for GRPO. MUSE-Align improves Qwen3-4B from 26.63% to 31.35% under SFT and from 20.39% to 38.56% under GRPO. These results provide a benchmark and training framework for developing LLM agents that act appropriately in shared homes.