# Group Selection Promotes Prosocial Prompts in Populations of LLM Agents

arXiv: https://arxiv.org/abs/2606.23343 · HTML: https://mlsanity.com/paper/2606.23343/

## 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 17 of its 36 references that are in the map.
- First posted on arXiv 22 Jun 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, 17 of 17)

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- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Evolution through Large Models](https://mlsanity.com/paper/2206.08896/) (2022)
- [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024)
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- [Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?](https://mlsanity.com/paper/2406.13605/) (2024)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas](https://mlsanity.com/paper/2604.15267/) (2026)
- [Agent Alignment in Evolving Social Norms](https://mlsanity.com/paper/2401.04620/) (2024)
- [Evolution of Social Norms in LLM Agents using Natural Language](https://mlsanity.com/paper/2409.00993/) (2024)
- [Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails](https://mlsanity.com/paper/2510.04860/) (2025)
- [Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems](https://mlsanity.com/paper/2602.14471/) (2026)

## Abstract (arXiv)

Current approaches to instill prosociality in large language model (LLM) agents often rely on humans specifying desired behaviors at the individual level, which does not guarantee cooperation within LLM populations. As frontier training shifts toward individual rewards for verifiable tasks, such as mathematics and coding, this outcome-based focus may further undermine cooperation in multi-agent settings. Large-scale cooperation in human populations emerged via unguided evolutionary mechanisms, not a central architect. Group selection, in which cooperative groups within a population outcompete less cooperative ones, has been argued to be essential. In this study, we explore whether group selection can promote cooperation in populations of LLM agents. We introduce a multi-agent simulation framework in which LLM agents play a repeated social dilemma game and transmit their natural-language prompts across generations under either individual- or group-level selection. Under group selection, prompts from high-performing groups are transmitted, thereby promoting prosociality and stabilizing cooperation. Under individual selection, self-interested prompts dominate, causing populations to collapse into collective defection. This gap is robust across prompt ablations, alternative game framings, and model swaps. We theoretically reproduce key results using a replicator-mutator model, whose empirical transmission kernel predicts a phase transition at a critical threshold. Preliminary findings show that, when informed about the selection mechanism, GPT-5.4 preemptively and gradually adjusts first-generation donations. This demonstrates strong anticipatory behavior that was not observed in the other tested models. These results demonstrate that prosocial prompts and cooperative behaviors evolve in LLM agent populations under group selection.