LLM-enabled Social Agents
For researchers building LLM-powered agents, this provides a conceptual baseline to address the gap between language fluency and social competence.
The paper argues that LLM-based agents lack social grounding and proposes persona-based role definitions as a foundation for enabling socially intelligible behavior, outlining research directions for representation, control, and evaluation.
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.