Decompilation -- recovering source code from compiled binaries -- is essential for security analysis, malware reverse engineering, and legacy software maintenance. However, existing decompilers produce code that often fails to compile or execute correctly, limiting their practical utility. We present a multi-agent framework that transforms decompiled code into re-executable source through Multi-level Constraint-Guided Decompilation (MCGD). Our approach employs a hierarchical validation pipeline with three constraint levels: (1) syntactic correctness via parsing, (2) compilability via GCC, and (3) behavioral equivalence via LLM-generated test cases. When validation fails, specialized LLM agents iteratively refine the code using structured error feedback. We evaluate our framework on 1,641 real-world binaries from ExeBench across three decompilers (RetDec, Ghidra, and Angr). Our framework achieves 84-97% re-executability, improving baseline decompiler output by 28-89 percentage points. In comparison with state-of-the-art LLM-based decompilation methods using the same GPT-4o backbone, our approach (84.1%) outperforms LLM4Decompile (80.3%), SK2Decompile (73.9%), and SALT4Decompile (61.8%). Our ablation study reveals that execution-based validation is critical: compile-only approaches achieve 0% behavioral correctness despite 91-99% compilation rates. The system converges efficiently, with 90%+ binaries reaching correctness within 2 iterations at an average cost of $0.03-0.05 per binary. Our results demonstrate that constraint-guided agentic refinement can bridge the gap between raw decompiler output and practically useful source code.
ALT4Decompile: Inferring C-aligned Abstract Loop Tree for LLM-Based Binary Decompilation
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
CHISEL-ing Back Source Code with AI-enabled Iterative Recovery
A*STAR Institute of Advanced Intelligence and Computing（A*STAR先进智能与计算研究所）；Coimbatore Institution of Technology（科因巴托尔理工学院）；National University of Singapore（新加坡国立大学）
From Heuristics to Transformers: A Comprehensive Survey of Type Inference from Stripped Binaries
Guangzhou University of Software（广州软件学院）；Guangzhou Huali College（广州华立学院）；Nukus State Technical University（努库斯国立技术大学）；Xidian University（西安电子科技大学）；Shenzhen University of Information Technology（深圳信息技术大学）；Shenzhen University（深圳大学）；National University of Computer and Emerging Sciences Karachi（巴基斯坦国家计算机与新兴科学大学卡拉奇分校）；National University of Computer and Emerging Sciences（巴基斯坦国家计算机与新兴科学大学）
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology（计算机科学与技术学院/人工智能学院，中国矿业大学）；Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology（教育部矿山数字化工程研究中心，中国矿业大学）
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Singapore Management University（新加坡管理大学）；Monash University Malaysia（马来西亚莫纳什大学）；Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院）；The Chinese University of Hong Kong（香港中文大学）；North Carolina State University（北卡罗来纳州立大学）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
