As large language models are increasingly deployed in high-stakes domains, it is essential that their outputs reflect not average human preference, but diverse perspectives. Achieving such pluralism remains challenging: existing pluralistic alignment methods rely on prompt-level interventions, represent only a limited set of values, or are compute-heavy, lacking explicit value control and representation. Furthermore, existing steering methods require manual value selection and cannot dynamically adapt to scenario-dependent value requirements, limiting their scalability and generality. To address these limitations, we introduce VISPA, a training-free pluralistic alignment framework that performs automatic, context-driven value selection and modular value-steered generation. VISPA identifies scenario-relevant values dynamically and applies activation steering to produce value-conditioned responses representing distinct perspectives. These responses enable support for three pluralistic alignment modes: Overton, Steerable, and Distributional. VISPA supports the addition of new values without finetuning and is compatible with diverse steering instantiations and steering models. Across extensive experiments spanning multiple models and evaluation settings, VISPA demonstrates strong performance across all pluralistic alignment modes in healthcare and beyond.

As large language models are increasingly deployed in sensitive domains such as healthcare, ensuring their outputs reflect the diverse values and perspectives held across populations is critical. However, existing alignment approaches, including pluralistic paradigms like Modular Pluralism, often fall short in the health domain, where personal, cultural, and situational factors shape pluralism. Motivated by the aforementioned healthcare challenges, we propose a first lightweight, generalizable, pluralistic alignment approach, ETHOSAGENTS, designed to simulate diverse perspectives and values. We empirically show that it advances the pluralistic alignment for all three modes across seven varying-sized open and closed models. Our findings reveal that health-related pluralism demands adaptable and normatively aware approaches, offering insights into how these models can better respect diversity in other high-stakes domains.