Binary decompilation, which aims to recover source code from compiled binaries, can be viewed as a low-resource and high-constraint form of neural machine translation. However, existing decompilation techniques often fail to produce source code that can be successfully recompiled and re-executed, particularly for optimized binaries. Recent advances in large language models (LLMs) have enabled neural approaches to decompilation, but the generated code is typically only semantically plausible rather than truly executable, limiting their practical reliability. These shortcomings arise from compiler optimizations and the loss of semantic cues in compiled code, which LLMs struggle to recover without contextual guidance. To address this challenge, we propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code. Notably, our approach is model-agnostic and requires no fine-tuning, enabling plug-and-play use with off-the-shelf LLMs. We evaluate ICL4Decomp across multiple datasets, optimization levels, and compilers, demonstrating around 40% improvement in re-executability over state-of-the-art decompilation methods while maintaining robustness.