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新的Agentic World Modeling使LLM代理无需环境副本即可进行扩展 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了Agentic World Modeling (AWM)，这是一种改进大型语言模型代理的新方法，它使测试时能够进行扩展，而无需复制整个环境。AWM将下一个观测预测重新构建为证据获取任务，代理模拟器通过安全接口查询真实环境，以构建结果观测，而不会改变共享状态。这种方法允许在共享的、不可重置的环境中进行并行探索，在$\tau$-bench、AppWorld和OfficeBench等基准测试中，其性能平均比单次模拟基线高40.07%，比训练过的世界模型基线高12.36%。 AI

影响 能够更高效、更可扩展地在复杂共享环境中测试LLM代理。

排序理由 该集群包含一篇详细介绍LLM代理新方法的论文。

在 arXiv cs.CL 阅读 →

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新的Agentic World Modeling使LLM代理无需环境副本即可进行扩展 · 跟踪2个来源

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该集群包含一篇详细介绍LLM代理新方法的论文。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Wendi Li, Huseyin A. Inan, Wei-Ning Chen, Yanzhi Chen, Chetan Bansal, Saravan Rajmohan, Sharon Li ·

    Agentic World Modeling for Agent Test-time Scaling without Environment Replica

    arXiv:2610.40927v1 Announce Type: cross Abstract: Test-time scaling (TTS) improves large language model agents by sampling and selecting among multiple candidate trajectories, but common realizations assume that these trajectories can be executed in parallel. This assumption brea…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Sharon Li ·

    Agentic World Modeling for Agent Test-time Scaling without Environment Replica

    Test-time scaling (TTS) improves large language model agents by sampling and selecting among multiple candidate trajectories, but common realizations assume that these trajectories can be executed in parallel. This assumption breaks in realistic deployments, where enterprise work…

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