# Tady: A Neural Disassembler without Structural Constraint Violations

arXiv: https://arxiv.org/abs/2506.13323 · HTML: https://mlsanity.com/paper/2506.13323/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [TensorFlow-Serving: Flexible, High-Performance ML Serving](https://mlsanity.com/paper/1712.06139/) (2017)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [jTrans: Jump-Aware Transformer for Binary Code Similarity](https://mlsanity.com/paper/2205.12713/) (2022)
- [XDA: Accurate, Robust Disassembly with Transfer Learning](https://mlsanity.com/paper/2010.00770/) (2020)
- [CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision](https://mlsanity.com/paper/2402.16928/) (2024)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [Datalog Disassembly](https://mlsanity.com/paper/1906.03969/) (2019)
- [SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask](https://mlsanity.com/paper/2007.14266/) (2020)
- [On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers](https://mlsanity.com/paper/2012.09155/) (2020)
- [CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection](https://mlsanity.com/paper/2402.18818/) (2024)
- [A Semantics-Based Hybrid Approach on Binary Code Similarity Comparison](https://mlsanity.com/paper/1907.01374/) (2019)
- [Disassembly as Weighted Interval Scheduling with Learned Weights](https://mlsanity.com/paper/2505.01536/) (2025)

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

- [Cybersecurity is the True Frontier for Generative AI Success or Failure](https://mlsanity.com/paper/2606.28929/) (2026)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)

## Abstract (arXiv)

Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread errors in existing neural disassemblers' outputs. These errors often originate from models' limited context modeling and instruction-level decoding that neglect global structural integrity. We introduce Tady, a novel neural disassembler featuring an improved model architecture and a dedicated post-processing algorithm, specifically engineered to address these deficiencies. Comprehensive evaluations on diverse binaries demonstrate that Tady effectively eliminates structural constraint violations and functions with high efficiency, while maintaining instruction-level accuracy.