64×64智能体网格分阶段解决长时序任务，提升稳定性与效率。
Curriculum Guided Massive Multi Agent System Solving For Robust Long Horizon Tasks
- 构建64×64轻量级智能体网格，按空间课程逐步扩展任务范围。
- 通过负对数似然评估置信度，降低对专家反馈依赖37%以上。
- 适合需要长期规划的机器人操控与复杂任务分解场景。
大型语言模型与多智能体系统在分解复杂任务方面展现出潜力，但在长时序推理任务中表现不佳且计算成本持续上升。本文提出一种分层多智能体架构，将推理分布于64×64的轻量级智能体网格上，并由选择性专家支持。通过空间课程逐步扩大网格的运行区域，确保智能体先掌握中心区域的简单任务，再逐步应对外围更复杂的任务。为提升可靠性，系统引入负对数似然（NLL）作为置信度度量，使课程能优先选择既准确又校准良好的区域。采用汤普森采样课程管理器，根据智能体能力与基于NLL的奖励信号自适应选择训练区域。在具有空间结构的汉诺塔基准上进行评估，该任务模拟了诸多机器人操作与规划任务的长时序特性。结果表明，该方法显著提升了系统稳定性，减少超过37%的专家调用次数，并通过分布式协作实现更强的长程推理能力。
原文摘要 · Abstract (English)
Large Language Models and multi-agent systems have shown promise in decomposing complex tasks, yet they struggle with long-horizon reasoning tasks and escalating computation cost. This work introduces a hierarchical multi-agent architecture that distributes reasoning across a 64*64 grid of lightweight agents, supported by a selective oracle. A spatial curriculum progressively expands the operational region of the grid, ensuring that agents master easier central tasks before tackling harder peripheral ones. To improve reliability, the system integrates Negative Log-Likelihood as a measure of confidence, allowing the curriculum to prioritize regions where agents are both accurate and well calibrated. A Thompson Sampling curriculum manager adaptively chooses training zones based on competence and NLL-driven reward signals. We evaluate the approach on a spatially grounded Tower of Hanoi benchmark, which mirrors the long-horizon structure of many robotic manipulation and planning tasks. Results demonstrate improved stability, reduced oracle usage, and stronger long-range reasoning from distributed agent cooperation.
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