mlsanityData to 14 Oct 2026

LLM-enabled Social Agents

arXiv 2605.02335 ↗First posted 4 May 2026cs.MA cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 16 of its 63 references that are in the map. First posted on arXiv 4 May 2026.

Builds on

16 of 63 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities8 May
  2. 2024Harnessing the power of LLMs for normative reasoning in MASs25 Mar
  3. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 13

  1. 2023Large Language Modelscited by 780
  2. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 309
  3. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 82
  4. 2025Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistantcited by 54
  5. 2024Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challengescited by 32
  6. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Userscited by 32
  7. 2025Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advancescited by 21
  8. 2025DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systemscited by 20
  9. 2025Mind the (Belief) Gap: Group Identity in the World of LLMscited by 12
  10. 2026The AI Hippocampus: How Far are We From Human Memory?cited by 11
  11. 2023"Mango Mango, How to Let The Lettuce Dry Without A Spinner?": Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partnercited by 6
  12. 2025Towards an LLM-powered Social Digital Twinning Platformcited by 4
1 more
  1. 2025A Plan Reuse Mechanism for LLM-Driven Agentcited by 3

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  1. 2025Large Language Models Miss the Multi-Agent Mark
  2. 2024Evolution of Social Norms in LLM Agents using Natural Language
  3. 2024LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models
  4. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View
  5. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
  6. 2025Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
  7. 2026Agent Sociodrama: Social-Dynamics-Guided Persona Recasting for LLM Agent Teams
  8. 2025The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation