Multi-Agent Systems

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Multi-Agent Systems

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Multi-Agent Reinforcement Learning

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Coordination

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Multi-Agent Reasoning

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Cooperation

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1,086 papers

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Sustaining Cooperation in Populations Guided by AI: A Folk Theorem for LLMs
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Robust Fusion of Object-Level V2X for Learned 3D Object Detection
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Topology-Driven Anti-Entanglement Control for Soft Robots
May 1, 2026Haoyang Le, Shengxuan Wang, Mohan Chen +1Soft Robots Multi-Robot Systems
Social Bias in LLM-Generated Code: Benchmark and Mitigation
May 1, 2026Fazle Rabbi, Lin Ling, Song Wang +1Large Language Model Bias Code Generation
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May 1, 2026James Pierce, Vaiva Kalnikaitė, Siddharth Gupta +1Human-Ai Collaboration Coordination

May 7, 2026 · cs.GTJ/K move · Enter open · S save

Sustaining Cooperation in Populations Guided by AI: A Folk Theorem for LLMs

Jonathan Shaki, Eden Hartman, Sarit Kraus, Yonatan Aumann

Bar-Ilan University, Israel

Large language models (LLMs) are increasingly used to provide instructions to many agents who interact with one another. Such shared reliance couples agents who appear to act independently: they may in fact be guided by a common model. This coupling can change the prospects for cooperation among agents with misaligned incentives. We study settings in which multiple LLMs each advise a population of clients who participate in instances of an underlying game, creating strategic interaction at the level of the LLMs themselves. This induces a meta-game among the LLMs, mediated through clients. We first analyze the one-shot setting, where shared instructions can change equilibrium behavior only when an LLM may influence more than one role in the same interaction; in such cases, cooperation may emerge, and the effect of client share can be beneficial, harmful, or non-monotone, depending on the base game. Our main result concerns the repeated setting. We prove a folk theorem for LLMs: despite indirect observation and the clients' inability to identify which LLM advised their opponents, all feasible and individually rational outcomes can be sustained as

\varepsilon

-equilibria. The result does not follow from the standard folk theorem and requires new proof techniques. Together, these results show that shared LLM guidance can sustain cooperation among populations of agents even when the underlying incentives are misaligned.

Cooperation Multi-Agent Large Language Model Large Language Equilibrium Populations

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