# Interaction Protocol Shapes Moral Judgment in Multi-Agent Debate

arXiv: https://arxiv.org/abs/2510.10002 · HTML: https://mlsanity.com/paper/2510.10002/

## Key facts

- Cited by 3 papers in the map (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 42 of its 61 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## 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, 30 of 42)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Humans or LLMs as the Judge? A Study on Judgement Biases](https://mlsanity.com/paper/2402.10669/) (2024)
- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://mlsanity.com/paper/2310.02124/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [Large Language Models Often Know When They Are Being Evaluated](https://mlsanity.com/paper/2505.23836/) (2025)
- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [Measuring Sycophancy of Language Models in Multi-turn Dialogues](https://mlsanity.com/paper/2505.23840/) (2025)
- [Moral Foundations of Large Language Models](https://mlsanity.com/paper/2310.15337/) (2023)
- [Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?](https://mlsanity.com/paper/2508.17536/) (2025)
- [DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life](https://mlsanity.com/paper/2410.02683/) (2024)
- [Large Language Models Reflect the Ideology of their Creators](https://mlsanity.com/paper/2410.18417/) (2024)
- [Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions](https://mlsanity.com/paper/2504.15236/) (2025)
- [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://mlsanity.com/paper/2311.08152/) (2023)
- [Calibrating the Confidence of Large Language Models by Eliciting Fidelity](https://mlsanity.com/paper/2404.02655/) (2024)
- [Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity](https://mlsanity.com/paper/2502.08788/) (2025)
- [Moral Alignment for LLM Agents](https://mlsanity.com/paper/2410.01639/) (2024)
- [Probing and Steering Evaluation Awareness of Language Models](https://mlsanity.com/paper/2507.01786/) (2025)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [The Hawthorne Effect in Reasoning Models: Evaluating and Steering Test Awareness](https://mlsanity.com/paper/2505.14617/) (2025)
- [Normative Evaluation of Large Language Models with Everyday Moral Dilemmas](https://mlsanity.com/paper/2501.18081/) (2025)
- [LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System](https://mlsanity.com/paper/2501.15749/) (2025)
- [ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models](https://mlsanity.com/paper/2406.04214/) (2024)

## Built on by (most cited first, 3 of 3)

- [AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power](https://mlsanity.com/paper/2604.07007/) (2026)
- [Preserving Disagreement: Architectural Heterogeneity and Coherence Validation in Multi-Agent Policy Simulation](https://mlsanity.com/paper/2604.26561/) (2026)
- [Measuring AI Accountability Through Argumentation Analysis: Can Model Reasoning Withstand Scrutiny?](https://mlsanity.com/paper/2609.05088/) (2026)

## Abstract (arXiv)

As agentic AI systems are deployed in advisory and evaluative roles, understanding how multi-agent interactions shape behavior becomes essential. Multi-agent debate has been studied as a mechanism to improve accuracy, but less is known about how debate structure -- the interaction protocol -- affects the values, dynamics, and consensus patterns that emerge when models navigate contested, real-world decisions. We address this gap by facilitating multi-agent debates among three models (GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash) to collectively assign blame in 1,000 everyday dilemmas from Reddit's ``Am I the Asshole'' community. We compare synchronous (parallel) and round-robin (sequential) interaction protocols, mirroring two fundamental ways multi-agent systems are orchestrated in practice. Across more than 30,000 total debates, our findings show striking behavioral differences, which we characterize through two dynamics: inertia and conformity. In the synchronous setting, GPT-4.1 showed stronger inertia (0.6-3.1% revision rates) than either Claude 3.7 Sonnet or Gemini 2.0 Flash (28-41% revision rates). Meanwhile, in round-robin debates, GPT-4.1 and Gemini 2.0 Flash stood out as highly conforming relative to Claude 3.7 Sonnet, with their verdict behavior strongly shaped by order effects. We further characterized the values invoked during debate, finding that GPT-4.1 emphasized personal autonomy and honest communication relative to its debate partners, while Claude 3.7 Sonnet and Gemini 2.0 Flash prioritized empathetic dialogue. Together, these results show how interaction protocol shapes moral reasoning in multi-turn debates, establishing it as a substantive sociotechnical design consideration in multi-agent systems.