You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914606596751360 |
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| author | Hsing, Nicole Zheng, Asuka Yuxi Zhao, Yi Tu, Haoqin Huang, Jen-Tse |
| author_facet | Hsing, Nicole Zheng, Asuka Yuxi Zhao, Yi Tu, Haoqin Huang, Jen-Tse |
| contents | Ensuring agent behaviors in distributed open multi-agent systems remains challenging, especially as populations grow and unaligned agents may exist. We show that a single aligned agent can propagate cooperative behaviors to untrained agents purely through natural language interaction, a phenomenon we term Alignment Propagation. We study this in the Red-Black Game, a team-based iterated Prisoner's Dilemma in which teammates deliberate and vote to determine their team's collective action. By distilling the cooperative reasoning and persuasive dialogues of a teacher model into a Qwen-3-14B, we obtain a seed agent that, when placed among four untrained teammates, doubles the cooperation rate from 24.8% to 62.2%, outperforming the teacher model and a vanilla Gemini-3.1-Pro. Remarkably, a seed trained exclusively on the RedBlack Game transfers zero-shot to Sugarscape, a spatially grounded survival simulation with pairwise trading, achieving a 91.5% trade success rate versus a 21.6% baseline. Our results reframe multi-agent alignment from an exhaustive per-agent training problem to a scalable social capability that can be engineered through strategic seed placement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27586 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents Hsing, Nicole Zheng, Asuka Yuxi Zhao, Yi Tu, Haoqin Huang, Jen-Tse Multiagent Systems Computation and Language Ensuring agent behaviors in distributed open multi-agent systems remains challenging, especially as populations grow and unaligned agents may exist. We show that a single aligned agent can propagate cooperative behaviors to untrained agents purely through natural language interaction, a phenomenon we term Alignment Propagation. We study this in the Red-Black Game, a team-based iterated Prisoner's Dilemma in which teammates deliberate and vote to determine their team's collective action. By distilling the cooperative reasoning and persuasive dialogues of a teacher model into a Qwen-3-14B, we obtain a seed agent that, when placed among four untrained teammates, doubles the cooperation rate from 24.8% to 62.2%, outperforming the teacher model and a vanilla Gemini-3.1-Pro. Remarkably, a seed trained exclusively on the RedBlack Game transfers zero-shot to Sugarscape, a spatially grounded survival simulation with pairwise trading, achieving a 91.5% trade success rate versus a 21.6% baseline. Our results reframe multi-agent alignment from an exhaustive per-agent training problem to a scalable social capability that can be engineered through strategic seed placement. |
| title | You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents |
| topic | Multiagent Systems Computation and Language |
| url | https://arxiv.org/abs/2605.27586 |