# LLM-enabled Social Agents

arXiv: https://arxiv.org/abs/2605.02335 · HTML: https://mlsanity.com/paper/2605.02335/

## 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.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 16 of 16)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://mlsanity.com/paper/2309.13007/) (2023)
- [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024)
- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges](https://mlsanity.com/paper/2412.03220/) (2024)
- [From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users](https://mlsanity.com/paper/2508.17281/) (2025)
- [LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities](https://mlsanity.com/paper/2405.06700/) (2024)
- [Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances](https://mlsanity.com/paper/2502.16804/) (2025)
- [DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2503.07675/) (2025)
- [Mind the (Belief) Gap: Group Identity in the World of LLMs](https://mlsanity.com/paper/2503.02016/) (2025)
- [The AI Hippocampus: How Far are We From Human Memory?](https://mlsanity.com/paper/2601.09113/) (2026)
- [Harnessing the power of LLMs for normative reasoning in MASs](https://mlsanity.com/paper/2403.16524/) (2024)
- ["Mango Mango, How to Let The Lettuce Dry Without A Spinner?": Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner](https://mlsanity.com/paper/2310.05853/) (2023)
- [Towards an LLM-powered Social Digital Twinning Platform](https://mlsanity.com/paper/2505.10681/) (2025)
- [A Plan Reuse Mechanism for LLM-Driven Agent](https://mlsanity.com/paper/2512.21309/) (2025)

## 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.