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English(EN) Executable World Models for ARC-AGI-3 in the Era of Coding Agents

AI代理使用可执行世界模型解决ARC-AGI-3基准测试

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文介绍了一种可执行世界模型方法，用于AI代理解决ARC-AGI-3基准测试。该系统使用Python来维护和验证世界模型，重构以简化并规划执行前的动作。在与GPT-5.5测试时，该代理解决了25个游戏中的15个，达到了58.12%的RHAE，而GPT-5.4解决了8个游戏，RHAE为41.29%。 AI

影响 展示了一种有前景的方法，使AI代理能够解决复杂的推理和规划任务，有可能提高在类似基准测试上的性能。

排序理由 该集群包含一篇详细介绍AI代理新方法和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI代理使用可执行世界模型解决ARC-AGI-3基准测试

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该集群包含一篇详细介绍AI代理新方法和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sergey Rodionov ·

    面向编码代理时代的ARC-AGI-3可执行世界模型

    arXiv:2605.05138v2 Announce Type: replace Abstract: We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous observations, refactors it toward simpler abstractions as a practical proxy for…

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