Voting steers representation in democratic bodies and functions as the primary mechanism for imposing the social will of the electorate upon the decision making machinery of regional government. Citizens will often have diverse, and conflicting, viewpoints which makes faithful representation of their plural interests a serious challenge in multi-party democracies. In the context of multi-agent LLMs, pluralistic preference alignment addresses this problem and, often through deliberative and voting methodologies, aims to draw groups toward a common policy that consistently bridges together a range of individual preferences. However, in multi-agent settings where the goal is to craft policy satisfying very diverse preferences, such as in social or political scenarios, there is little understanding of how to structure general process mechanisms that lead to the best outcome. To address this we present VoteSim, a framework to simulate a wide range of electoral systems across sociopolitical topics and a diverse set of personalized voter LLM agents. We draw on a rich persona modeling dataset, a range of relevant sociopolitical topics, and prevalent legislative procedure to generate social policy through deliberation. Voting agents then rank their preferred policy to determine how electoral systems can satisfy group preference. Detailed empirical analysis reveals that pluralistic alignment in multi-agent LLMs on important sociopolitical issues is best achieved through proportional electoral representation, reflecting outcomes among multi-party systems in human society.

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers’ writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline without the skill library. We release PaperMentor as open source for public use.