# The Incredible Shrinking Context... in a Decompiler Near You

arXiv: https://arxiv.org/abs/2409.11157 · HTML: https://mlsanity.com/paper/2409.11157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation](https://mlsanity.com/paper/2607.01590/) (2026)
- [SCDBench: A Benchmark for LLM-Based Smart Contract Decompilers](https://mlsanity.com/paper/2605.29059/) (2026)

## Abstract (arXiv)

Decompilation of binary code has arisen as a highly-important application in the space of Ethereum VM (EVM) smart contracts. Major new decompilers appear nearly every year and attain popularity, for a multitude of reverse-engineering or tool-building purposes. Technically, the problem is fundamental: it consists of recovering high-level control flow from a highly-optimized continuation-passing-style (CPS) representation. Architecturally, decompilers can be built using either static analysis or symbolic execution techniques. We present Shrknr, a static-analysis-based decompiler succeeding the state-of-the-art Elipmoc decompiler. Shrknr manages to achieve drastic improvements relative to the state of the art, in all significant dimensions: scalability, completeness, precision. Chief among the techniques employed is a new variant of static analysis context: shrinking context sensitivity. Shrinking context sensitivity performs deep cuts in the static analysis context, eagerly "forgetting" control-flow history, in order to leave room for further precise reasoning. We compare Shrnkr to state-of-the-art decompilers, both static-analysis- and symbolic-execution-based. In a standard benchmark set, Shrnkr scales to over 99.5% of contracts (compared to ~95%), covers (i.e., reaches and manages to decompile) 67% more code, and reduces key imprecision metrics by over 65%.