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Anthropic向更多安全团队开放最强Claude模型

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Anthropic向更多安全团队开放最强Claude模型

2026年9月,麻省理工学院等机构公布博弈人工智能系统Ataraxos,在信息不完全的战略棋类游戏中展现出较强能力。与双方棋子身份完全公开的国际象棋不同,Stratego要求玩家推断对手隐藏棋子的等级,并在试探、保密和风险之间作出选择,因此被用于研究复杂决策。

系统先通过自我对弈学习基础策略,再在每一步行动前利用生成模型估计当前棋盘的可能状态,动态修正选择。研究团队报告,其训练样本需求不到对比系统的百分之一,自我对弈局数不到三十分之一,仍取得更强的游戏表现。系统也被扩展到其他规则不同的信息不完全游戏中。研究的意义在于提高有限计算资源下的推理效率,并为谈判或网络安全等场景提供算法思路。游戏环境拥有清晰规则和评价标准,现实任务则更复杂,团队下一步希望加强决策解释与审查能力,让人类能够理解模型为何给出某项建议。

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