When answering subjective questions, an ideal LLM should surface diverse plausible perspectives rather than favoring a single viewpoint, a characteristic known as pluralism. Recent studies show that modern LLMs optimized through preference alignment systematically favor certain positions on subjective queries, making pluralism evaluation increasingly important. However, existing evaluation methods focus dominantly on multiple-choice and question-answering tasks, leaving open-ended generation largely unaddressed.We propose PLURALEVAL, an evaluation framework that assesses LLM pluralism in open-ended generation by comparing outputs against free-form crowd responses. Our approach decomposes ground-truth responses into atomic, non-overlapping claims, then evaluates whether LLMs adequately cover this diverse claim space. We then introduce WildSCOPE, a multi-domain dataset of natural crowd responses, and demonstrate that PLURALEVAL captures novel insights, such as the collapse of pluralism through sycophancy, where LLM systematically degrades in overton pluralism when a user’s belief is revealed. Finally, we discuss the value and actionable insights for preserving and encouraging pluralism from LLM deployers’ side.

Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in the multimodal symbolic music domain remain largely unexplored.We introduce WildScore, the first in-the-wild multimodal symbolic music reasoning and analysis benchmark, designed to evaluate MLLMs’ capacity to interpret real-world music scores and answer complex musicological queries. Each instance in WildScore is sourced from genuine musical compositions and accompanied by authentic user-generated questions and discussions, capturing the intricacies of practical music analysis. To facilitate a comprehensive evaluation, we propose a systematic taxonomy,comprising both high-level and fine-grained musicological ontologies. Furthermore, we frame complex music reasoning as multiple-choice question answering,enabling controlled and scalable assessment of MLLMs’ symbolic music understanding. Empirical benchmarking of state-of-the-art MLLMs on WildScore reveals intriguing patterns in their visual-symbolic reasoning, uncovering both promising directions and persistent challenges for MLLMs in symbolic music reasoning and analysis.We release the dataset and code.