I agents increasingly execute procedural workflows as sequential action traces, which obscures latent concurrency and induces repeated step-by-step reasoning. We introduce BPOP, a Bayesianframework that infers a latent dependency partial order from noisy linearized traces. BPOP models traces as stochastic linear extensions of an underlying graph and performs efficient MCMC inference via a tractable frontier-softmax likelihood that avoids #P-hard marginalization over linear extensions. We evaluate on our open-sourced Cloud-IaC-6, a suite of cloud provisioning tasks with heterogeneous LLM-generated traces, and WFCommons scientific workflows. BPOP recover dependency structure more accurately than trace-only and process-mining baselines, and the inferred graphs support a compiled executor that prunes irrelevant context, yielding substantial reductions in token usage and execution time.

翻译：智能体日益以顺序动作轨迹的形式执行流程化工作流，这种线性化表示掩盖了潜在的并发性，并导致重复的逐步推理。本文提出BPOP——一种贝叶斯推理框架，能够从含噪声的线性化轨迹中推断潜在的依赖偏序关系。BPOP将轨迹建模为底层图结构的随机线性扩展，并通过可处理的前沿-softmax似然函数执行高效的MCMC推理，避免了线性扩展的#P难边际化计算。我们在开源的Cloud-IaC-6（一套包含异构LLM生成轨迹的云资源配置任务集）和WFCommons科学工作流数据集上进行评估。实验表明，BPOP在依赖结构恢复精度上显著优于仅使用轨迹的方法及流程挖掘基线，且推断出的图结构可支持编译型执行器实现无关上下文的剪枝，从而显著降低令牌使用量和执行时间。