从序列到结构：面向大语言模型智能体的关系不确定性传播

摘要

可靠的不确定性量化（UQ）对于在复杂交互环境中部署大语言模型（LLM）智能体至关重要。现有的不确定性量化方法主要依赖局部信号，如词元概率、预测熵或单步置信度，因而忽视了执行轨迹中错误累积所依赖的长程依赖关系。因此，这些方法可能无法识别那些根因出现在最终答案之前数个推理或交互步骤的智能体失败。我们提出RUPA（面向智能体的关系不确定性传播），一个面向LLM智能体的轨迹级不确定性量化框架。RUPA将执行历史表示为一个有向轨迹图，其中推理状态、工具交互和环境反馈作为节点，通过时间依赖边和语义依赖边相连。随后，RUPA在该图上传播不确定性，以刻画执行风险如何在交互步骤之间累积和转移。传播后的信号与轨迹级行为特征及目标对齐信息相结合，为完整的智能体轨迹生成置信度估计。我们在代表性智能体基准测试上评估RUPA，包括τ-2、Terminal-Bench-2和GAIA，使用了涵盖多个模型家族的6个开源大语言模型。实验结果表明，RUPA在提供更准确的不确定性估计、实现更早的失败检测以及改善不确定性引导的智能体执行方面，持续优于现有的不确定性量化方法。这些结果证明，显式建模关系依赖对于长时程LLM智能体的可靠不确定性量化至关重要，为可信赖的智能体执行提供了实用基础。

English

Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range dependencies through which errors accumulate across an execution trajectory. As a result, they may fail to identify agent failures whose causes originate several reasoning or interaction steps before the final answer. We propose RUPA (Relational Uncertainty Propagation for Agents), a trajectory-level UQ framework for LLM agents. RUPA represents an execution history as a directed trajectory graph in which reasoning states, tool interactions, and environment feedback are nodes connected by temporal and semantic dependency edges. It then propagates uncertainty over this graph to capture how execution risk accumulates and transfers across interaction steps. The propagated signal is combined with trajectory-level behavioral features and goal-alignment information to produce a confidence estimate for the full agent trajectory. We evaluate RUPA on representative agent benchmarks, including τ-2, Terminal-Bench-2, and GAIA, using 6 open-source LLMs spanning multiple model families. Experimental results show that RUPA consistently outperforms existing UQ methods by providing more accurate uncertainty estimates, enabling earlier failure detection, and improving uncertainty-guided agent execution across diverse agent tasks. These results demonstrate that explicitly modeling relational dependency is crucial to reliable UQ for long-horizon LLM agents, providing a practical foundation for trustworthy agent execution.