# MAEBE: Multi-Agent Emergent Behavior Framework

arXiv: https://arxiv.org/abs/2506.03053 · HTML: https://mlsanity.com/paper/2506.03053/

## Key facts

- Cited by 8 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 4 of its 13 references that are in the map.
- First posted on arXiv 3 Jun 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, 4 of 4)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023)
- [Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems](https://mlsanity.com/paper/2410.07283/) (2024)
- [The Greatest Good Benchmark: Measuring LLMs' Alignment with Utilitarian Moral Dilemmas](https://mlsanity.com/paper/2503.19598/) (2025)

## Built on by (most cited first, 8 of 8)

- [MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation](https://mlsanity.com/paper/2510.15186/) (2025)
- [Agent-Testing Agent: A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents](https://mlsanity.com/paper/2508.17393/) (2025)
- [Convergence dynamics of Agent-to-Agent Interactions with Misaligned objectives](https://mlsanity.com/paper/2511.08710/) (2025)
- [Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective](https://mlsanity.com/paper/2512.04691/) (2025)
- [Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability](https://mlsanity.com/paper/2509.11068/) (2025)
- [AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation](https://mlsanity.com/paper/2512.03466/) (2025)
- [Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software](https://mlsanity.com/paper/2606.28235/) (2026)
- [WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies](https://mlsanity.com/paper/2609.05591/) (2026)

## Abstract (arXiv)

Traditional AI safety evaluations on isolated LLMs are insufficient as multi-agent AI ensembles become prevalent, introducing novel emergent risks. This paper introduces the Multi-Agent Emergent Behavior Evaluation (MAEBE) framework to systematically assess such risks. Using MAEBE with the Greatest Good Benchmark (and a novel double-inversion question technique), we demonstrate that: (1) LLM moral preferences, particularly for Instrumental Harm, are surprisingly brittle and shift significantly with question framing, both in single agents and ensembles. (2) The moral reasoning of LLM ensembles is not directly predictable from isolated agent behavior due to emergent group dynamics. (3) Specifically, ensembles exhibit phenomena like peer pressure influencing convergence, even when guided by a supervisor, highlighting distinct safety and alignment challenges. Our findings underscore the necessity of evaluating AI systems in their interactive, multi-agent contexts.