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arXiv · 2602.02806

De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution

Dongqing Li·Zheqiao Cheng·Geoff K. Nicholls·Quyu Kong
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Abstract

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.

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Dongqing Li, Zheqiao Cheng, Geoff K. Nicholls, Quyu Kong. 2026-02-02. De-Linearizing Agent Traces: Bayesian Inference of Latent Partial Orders for Efficient Execution. https://arxiv.org/abs/2602.02806

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