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English(EN) MarkovSim: an LLM Agent Harness with Bounded Context for Long-Horizon Computational Simulation

MarkovSim LLM Harness 管理有界上下文以进行长时仿真

研究人员开发了 MarkovSim,一个新颖的 LLM Agent Harness,专为长时计算仿真而设计。该系统通过实施新颖的上下文管理方案,解决了 LLM 超过其上下文窗口的挑战。MarkovSim 将工作流记录存储在结构化内存中,并在状态转换时创建进度的紧凑表示,确保上下文保持有界,无论仿真长度如何。MarkovSim 在有限元分析和六个基准问题上进行了测试,取得了很高的成功率,纠正了求解器操作和模型物理学中的故障,并且仅在需要工程判断时才需要人工干预。 AI

影响 该系统通过克服上下文窗口限制,有可能实现更复杂和更长期的 AI 驱动仿真。

排序理由 该集群包含一篇详细介绍新型 LLM Agent Harness 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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MarkovSim LLM Harness 管理有界上下文以进行长时仿真

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该集群包含一篇详细介绍新型 LLM Agent Harness 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) · Yeui-Lung Lei, Wen-Bo Chen, Pan Zhang ·

    MarkovSim:一个具有有限上下文的LLM智能体工具集,用于长时计算模拟

    arXiv:2610.35349v1 Announce Type: cross Abstract: Recent studies have applied large language model (LLM) agent systems to computational simulation workflows. Such workflows are long-horizon, so the information generated can exceed what an LLM can reason over at once, its context …