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English(EN) PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning

新的PACE框架通过交错推理和执行来加速具身AI规划

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了PACE（Planning with Adaptive Cognitive Effort，具有自适应认知努力的规划）新框架，旨在通过解决过高的推理延迟，使增强推理的大型语言模型在具身系统中更加实用。PACE引入了交错思考-行动（Interleaved Think-Act）架构，允许推理和行动执行并发进行，同时还有一个动态预算分配器（Dynamic Budget Allocator），根据可用执行时间调整推理令牌预算。在Qwen3-8B-AWQ模型上于Robotouille基准测试中进行测试，PACE相比ReAct+Think基线在成功率上提高了67%，思考时间加速了6.9倍，有效地将66.8%的思考时间隐藏在执行窗口内。 AI

影响 使推理模型能够在对延迟敏感的具身领域中运行，可能加速机器人技术和现实世界AI应用。

排序理由 该集群包含一篇详细介绍AI规划新框架和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的PACE框架通过交错推理和执行来加速具身AI规划

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该集群包含一篇详细介绍AI规划新框架和方法的学术论文。[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) · Yuchen Huang, Xijiang Ying, Zhenhua Ma, Xiaxiang Yuan, Zhijie Gao, Jiayi Huang, Ruichi Mao, Jiazheng Zhang, Hongsheng Ti, Maotao Tian, Rong Shi, Lu Zhao, Shizhuang Zhang, Zhuo Cui, He Wang, Ling Liu, Wei Zhang ·

    PACE：面向时间高效具身规划的自适应预算分配

    arXiv:2608.03034v1 Announce Type: cross Abstract: Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains impractical due to prohibitive inference delays-often exceeding minutes per plannin…

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