🤖 AI Summary
Although large language models exhibit strong linguistic capabilities, they lack a foundational understanding of social dynamics, making it difficult to generate socially intelligible behaviors that align with roles, norms, and contextual constraints. This work proposes, for the first time, a systematic framework in which character-driven role modeling serves as the core mechanism for building socially intelligent agents. By formalizing roles as structured persona descriptions, the approach enables a principled transformation from linguistic competence to contextually appropriate social behavior. The study introduces a hybrid control architecture that integrates large language models with theories of social roles and persona-based modeling, and outlines a research roadmap encompassing representation, control, and evaluation. This framework establishes a foundational theoretical and methodological baseline for advancing research on socially capable artificial agents.
📝 Abstract
Large Language Models (LLMs) have transformed agent-agent and human-agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environments. This paper develops a conceptual baseline for LLM-enabled social agents by arguing that they should be grounded in role definitions operationalized through persona descriptions. On this basis, we outline research directions for representation, hybrid control, and evaluation. The paper concludes that persona-based role definitions are a necessary foundation for turning language competence into social behaviour.