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.
@article{arxiv.2602.02806,
title = {De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution},
author = {Dongqing Li and Zheqiao Cheng and Geoff K. Nicholls and Quyu Kong},
journal= {arXiv preprint arXiv:2602.02806},
year = {2026}
}