Multi-language Neural Decompilation

Deterministic decompilers and state of the art neural decompilers often support binary to C translation yet there is a lack of decompilers for modern high level languages (HLLs) such as Swift, Go and Rust. Current benchmarks for decompilation fixate on binary to C decompilation at the function level, limiting the realistic decompilation tasks to evaluate decompilers on. We investigate using LLMs to translate binaries at the repository level for multiple HLLs, and introduce BlankSpaceBench, a multi-language decompilation benchmark. We also develop BlankSpace, a multi-language agentic decompilation framework, which uses existing binary analysis tools to translate binaries from multiple instruction set architectures (ISAs) to HLLs, with support for coding agents Claude Code and Codex. We compare BlankSpace, frontier models, and deterministic decompilers on top open source projects, evaluating decompilation for arm64 to Swift, x86-64 to C, and x86-64 to Go.