English(EN) Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budget

TRACE复制揭示阈值依赖性和基准偏差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

本文提供了一份关于复制TRACE方法以从预训练自回归序列模型中提取因果图的实践指南。作者发现，TRACE的最佳阈值取决于真实边距，而不是一个固定常数，并且默认阈值主要识别直接的、相邻的影响。他们还观察到，在原始TRACE论文中使用的合成基准可能会通过将因果真实性集中在滞后1来扭曲结果，并且在选定的阈值下，F1分数仅用两个粒子就会饱和。 AI

影响为从序列模型中提取因果图的方法的局限性和实际应用提供了见解。

排序理由该集群包含一篇详细介绍特定方法复制和分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Alex Chadyuk, Alicia Zhang, Roy Kucukates · 2026-09-02 04:00

复制 TRACE：其阈值和粒子预算的实践指南

arXiv:2609.01108v1 Announce Type: new Abstract: TRACE (Math & Lienhart, arXiv:2602.01135) reads causal graphs over event types out of a pretrained autoregressive sequence model by thresholding a per-position conditional-mutual-information estimate at a fixed tau. We independently…

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复制 TRACE：其阈值和粒子预算的实践指南

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