NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning

Researchers introduced NormAct, a benchmark evaluating whether multimodal large language models comply with hidden social norms during embodied planning. Tests on GPT-5.4, Claude Opus 4.7, and Gemini 3 Pro showed models achieved explicit goals 67.3% of the time but complied with hidden norms only 26.4%. The proposed NormPerceptor cue generator improved task success from 24.2% to 46.7% by inferring scene-relevant norms before planning.

arXiv:2606.27826v1 Announce Type: new Abstract: Multimodal large language models MLLMs are increasingly deployed as embodied planners in egocentric environments, where task success requires not only achieving instructed goals but also acting in socially appropriate ways. While explicit goals may render certain actions optimal, implicit social norms often impose hidden constraints. Existing evaluations typically focus on explicit goal achievement or direct norm knowledge, seldom assessing whether planners can infer and apply these hidden constraints within action sequences. We introduce NormAct, a benchmark for embodied social-norm interactions that evaluates plans on Goal Achievement, Norm Compliance, and overall Task Success. NormAct uniquely embeds hidden norms within ordinary tasks, testing whether models can realize them without explicit instruction. Experiments with state-of-the-art MLLMs GPT-5.4, Claude Opus 4.7, Gemini 3 Pro reveal a significant gap: models achieve explicit goals in 67.3\% of cases, but comply with hidden norms in only 26.4\%. Cue-condition experiments indicate that this gap stems not from a lack of general social knowledge, but from challenges in activating and grounding relevant norms in context. To address this, we propose NormPerceptor, a context-conditioned cue generator that infers scene-relevant norms prior to planning, increasing Task Success from 24.2\% to 46.7\%. Our results underscore the importance of enabling embodied agents to proactively detect hidden norms, ground them in visual evidence, and integrate them as action-planning constraints. Our benchmark is publicly available at https://huggingface.co/datasets/Caleb196x/NormAct.