English(EN) Echo: Learning-based Matching Decompilation using Trusted Back Translation

Echo系统利用可信反向翻译提高二进制反编译准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了Echo，一种新颖的匹配反编译系统，它利用可信反向翻译来提高从二进制文件中恢复的源代码的可靠性。Echo利用编译作为反馈机制来指导迭代搜索过程，生成候选程序和编译配置。然后，系统重新编译这些候选程序，测量汇编级相似性，并通过基于规则的重写和神经方法来改进不匹配之处。在评估中，Echo显著优于现有基线，平均实现了2.43倍的精确匹配，并在分析恶意软件二进制文件时展示了优于GPT-5.6和Codex等模型的性能。 AI

影响提高了从二进制文件中恢复源代码的可靠性和准确性，可能改进软件安全分析和逆向工程。

排序理由该集群包含一篇详细介绍新代码反编译系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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本文如何被排名

Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新代码反编译系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jun Bi, Xiangxin Fang, Aarsh Chaube, Jos\'e Wesley De Souza Magalh\~aes, Rodrigo C. O. Rocha, Michael O'Boyle · 2026-09-17 04:00

Echo：基于学习的匹配反编译与可信反向翻译

arXiv:2609.18706v1 Announce Type: cross Abstract: Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompilation addresses this problem by searching for source code whose recompiled assem…

本文如何被排名

报道来源 [1]

Echo：基于学习的匹配反编译与可信反向翻译

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