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English(EN) The Shape of a Program: Path Signatures for Trace-to-Program Induction

路径签名增强了程序归纳能力,优于传统模型

研究人员开发了一种使用路径签名进行程序归纳的新方法,这是一种源自随机分析的技术。该方法将执行跟踪视为高维粗糙路径,并使用路径签名变换对其进行编码。在Karel、List和RASP域上的实验表明,这种对数签名跟踪编码器在训练数据有限的情况下,性能优于LSTM、Transformer和Fourier基线。该方法还展示了一个收缩管道,可以在没有外部预言机的情况下识别监督数据中的冗余,从而产生更具可解释性的潜在几何结构。 AI

影响 这项研究提供了一种新颖的程序归纳方法,有望提高分析代码或执行跟踪的AI系统的可解释性和效率。

排序理由 该集群包含一篇详细介绍程序归纳新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

路径签名增强了程序归纳能力,优于传统模型

本文如何被排名

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37 / 100
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Tool
该集群包含一篇详细介绍程序归纳新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mohamed Ghanem, Bernd Finkbeiner ·

    程序的形状:用于从跟踪到程序归纳的路径签名

    arXiv:2611.13005v1 Announce Type: cross Abstract: Synthesizing programs from execution traces is a fundamental challenge in algorithmic interpretability and the reverse engineering of complex systems. We bring a rough-path-theoretic perspective to program induction, treating exec…