提出可微分方法，从线性记录中推断隐藏的局部顺序关系。
A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
- 用平滑替代品替换不连续的顺序约束，实现可微分推断
- 在合成数据和云代理轨迹上验证，精度接近硬模型且速度更快
- 适合处理带隐含依赖关系的轨迹数据，如工作流与社会等级
许多排名和智能体轨迹数据虽以线性序记录，但其潜在结构仅为部分有序。这在智能体与工作流轨迹中尤为常见，观察到的顺序可能仅是任意线性化，并非真实前置关系。本文提出一种针对此类轨迹的可微分潜层部分序推断方法。基于带有硬边界约束的噪声线性扩展模型，将不连续的乘积序优先关系与二元边界可行性替换为光滑近似，得到连续后验分布，保留闭包级别的部分序语义，并支持基于梯度的MCMC与变分推断。理论证明了软传递性、极限边界恢复及收敛至硬似然。在合成数据、社会支配关系记录及云智能体轨迹上的实验表明，小规模实例后验拟合接近硬式MCMC，大规模问题则实现更优的运行时-精度权衡。
原文摘要 · Abstract (English)
Many ranking and agent trace datasets are recorded as linear orders even though their latent structure is only partially ordered. This is especially common in agent and workflow traces, where observed order may reflect arbitrary linearization rather than true prerequisites. We introduce a differentiable relaxation for latent partial-order inference from such traces. Starting from a hard frontier-constrained model of noisy linear extensions, we replace discontinuous product-order precedence and binary frontier feasibility with smooth surrogates, yielding a continuous posterior that preserves closure-level partial-order semantics and supports gradient-based MCMC and variational inference. We prove soft transitivity, sharp-limit frontier recovery, and convergence to the hard likelihood. Experiments on synthetic data, records of social dominance relations, and cloud-agent traces show close posterior fidelity to hard MCMC on small instances and improved runtime--accuracy trade-offs on larger problems.
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