NormAct shows MLLMs reach explicit goals in 67.3% of cases but comply with hidden norms in only 26.4%, with NormPerceptor raising task success from 24.2% to 46.7%.
Measuring physical-world privacy awareness of large language models: An evaluation benchmark
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Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.
citing papers explorer
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NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning
NormAct shows MLLMs reach explicit goals in 67.3% of cases but comply with hidden norms in only 26.4%, with NormPerceptor raising task success from 24.2% to 46.7%.
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How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study
Vision-language models exhibit perceptual fragility and fail to consistently respect privacy constraints when operating in simulated physical environments, with performance declining in cluttered scenes and under conflicting commands.