AI 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：人工智能智能体日益以顺序动作轨迹的形式执行流程化工作流，这掩盖了潜在的并发性并导致重复的逐步推理。本文提出BPOP，一种从含噪声的线性化轨迹中推断潜在依赖偏序关系的贝叶斯框架。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MCMC推断，避免了线性扩展的#P-难边际化计算。我们在开源的Cloud-IaC-6（包含异构LLM生成轨迹的云资源配置任务集）和WFCommons科学工作流上进行评估。BPOP在依赖结构恢复方面比纯轨迹基准和流程挖掘基准更准确，且推断出的图结构支持编译执行器剔除无关上下文，从而显著降低令牌使用量和执行时间。