# Harnessing the power of LLMs for normative reasoning in MASs

arXiv: https://arxiv.org/abs/2403.16524 · HTML: https://mlsanity.com/paper/2403.16524/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- 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, 4 of 4)

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Can Machines Learn Morality? The Delphi Experiment](https://mlsanity.com/paper/2110.07574/) (2021)
- [Emergence of Social Norms in Generative Agent Societies: Principles and Architecture](https://mlsanity.com/paper/2403.08251/) (2024)

## Built on by (most cited first, 7 of 7)

- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values](https://mlsanity.com/paper/2407.03000/) (2024)
- [Evolution of Cooperation in LLM-Agent Societies: A Preliminary Study Using Different Punishment Strategies](https://mlsanity.com/paper/2504.19487/) (2025)
- [Can LLMs Reason About Trust?: A Pilot Study](https://mlsanity.com/paper/2507.21075/) (2025)
- [Social Norm Reasoning in Multimodal Language Models: An Evaluation](https://mlsanity.com/paper/2603.03590/) (2026)
- [Governance-Aware Vector Subscriptions for Multi-Agent Knowledge Ecosystems](https://mlsanity.com/paper/2603.20833/) (2026)
- [LLM-enabled Social Agents](https://mlsanity.com/paper/2605.02335/) (2026)

## Abstract (arXiv)

Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, traditional techniques have limitations, such as operating in limited environments often using brittle symbolic reasoning. The advent of Large Language Models (LLMs) offers a promising solution, providing a rich and expressive vocabulary for norms and enabling norm-capable agents that can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper examines the potential of LLM-based agents to acquire normative capabilities, drawing on recent Natural Language Processing (NLP) and LLM research. We present our vision for creating normative LLM agents. In particular, we discuss how the recently proposed "LLM agent" approaches can be extended to implement such normative LLM agents. We also highlight challenges in this emerging field. This paper thus aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents.