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Rohan Paul· @rohanpaul_ai · X· · 原发布时间 AI 评分54

牛津研究发现,不同模型的 AI 智能体易因自定规则而协作失误

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研究用 9 个小型合作游戏测试基于 GPT-5.2、Gemini 3 Pro 等模型的文本和代码智能体。在一项游戏中,文本智能体与其他模型搭档时,得分低于稳拿 5 分的安全选项;加入简短的共同协调原则后,智能体能稳定选择该选项。

原帖引用 · 原文

@rohanpaul_ai

New Oxford paper finds that frontier LLM agents fail to coordinate with unfamiliar partners because each one treats its own made-up rules (arbitrary convention) as the obvious choice.

Agents from different companies increasingly must work together with no agreed rules. Past studies used shared scaffolding or complex games that hide why coordination fails.

They tested text and code agents built on models like GPT-5.2 and Gemini 3 Pro in 9 small cooperative games. In Cat & Dog, a safe move guarantees 5 points, while a signal convention pays 10 only if both agents pick the same one.

Paired with other models, text agents scored well below that safe 5, each calling its own arbitrary convention natural. A short shared statement of coordination principles, called a Coordination-friendly definition, led agents to reliably take the safe 5.

If your agent must work with agents you did not build, tell it to avoid unstated conventions and prefer actions any partner reads correctly.

来源:Rohan Paul · x.com

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