Large language models are increasingly expected to adapt to individual users, reflecting differences in preferences, values, and communication styles. To evaluate whether models can serve diverse populations, we introduce MTPA, a benchmark that leverages large-scale survey data (WVS, EVS, GSS) to construct real, hyper-granular personas spanning demographics, beliefs, and values. Unlike prior benchmarks that rely on synthetic profiles or narrow trait prediction, MTPA conditions models on real personas and systematically tests their behavior across core alignment tasks. We show that persona conditioning exposes pluralistic misalignment: while aggregate metrics suggest models are truthful and safe, subgroup-specific evaluations reveal hidden pockets of degraded factuality, fairness disparities, and inconsistent value alignment. Alongside the benchmark, we release a dataset, toolkit, and baseline evaluations. MTPA is designed with extensibility and sustainability in mind: as the underlying survey datasets are regularly updated, MTPA supports regular integration of new populations and user traits.

Sentence embeddings are central to modern natural language processing, powering tasks such as clustering, semantic search, and retrieval-augmented generation. Yet, they remain largely opaque: their internal features are not directly interpretable, and users lack fine-grained control for downstream tasks. To address this issue, we introduce a formal framework to characterize the organization of features in sentence embeddings through information-theoretic means. Building on this foundation, we develop a method to identify interpretable feature directions and show how they can be composed to capture richer semantic structures. Experiments on both synthetic and real-world datasets confirm the presence of this semantic geometry and highlight the utility of our approach for enhancing interpretability and fine-grained control in sentence embeddings.