Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement

• URL: http://arxiv.org/abs/2606.16162v1
• Date: Mon, 15 Jun 2026 03:25:05 GMT
• Title: Binary Decompilation LLM with Feedback-Driven Multi-Turn Refinement
• Abstract summary: This paper presents AutoDecompiler, a decompilation-specialized LLM trained with reinforcement learning for feedback-driven multi-turn binary decompilation.<n>We design decompilation-specific rewards that capture code validity, recompilability, execution consistency, and semantic fidelity.<n>We train the AutoDecompiler family and evaluate it across different input settings, model scales, and benchmarks.
• Score: 39.339662640253785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Binary decompilation is fundamental to security tasks such as vulnerability discovery, malware inspection, and executable-only program understanding. Recent LLM-based decompilation methods have shown promising results, but most still follow a single-turn generation paradigm: given assembly code or decompiler-produced pseudo-code, the model generates one output and stops. Consequently, the generated code may appear readable or even compile successfully, yet still deviate from the behavior of the original binary and mislead downstream analysis. This paper presents AutoDecompiler, a decompilation-specialized LLM trained with reinforcement learning for feedback-driven multi-turn binary decompilation. Instead of treating decompilation as one-shot code generation, AutoDecompiler formulates it as an iterative refinement process, where the model revises generated code based on compilation, execution, and input/output testing feedback. To enable this process, we design decompilation-specific rewards that capture code validity, recompilability, execution consistency, and semantic fidelity. We further construct stage-aware diagnostic feedback from compiler errors, execution failures, and failed test cases, and introduce progress-aware trajectory rewarding and turn-aware advantage reweighting to encourage beneficial revisions while suppressing regressions. We train the AutoDecompiler family and evaluate it across different input settings, model scales, and benchmarks. Experimental results show that AutoDecompiler consistently outperforms its single-turn counterparts under the same model size and input setting, achieving clear improvements in behavioral re-executability. These results demonstrate that learning to exploit program feedback with reinforcement learning is an effective direction for improving the functional correctness of LLM-based binary decompilation.

Related papers

• Multi-View Decompilation for LLM-Based Malware Classification [2.6107088019301252]
  We provide a benchmark of benign utilities and malicious programs spanning a range of threat behaviors.<n>We find that providing both decompiler views improves malicious-class F1, mainly by increasing recall on malicious samples.
  arXiv  Detail & Related papers  (2026-06-18T16:15:30Z)
• Unlocking LLM Code Correction with Iterative Feedback Loops [5.504955093712013]
  This study introduces metrics to evaluate code failures, analyze rectification patterns, and compare the effectiveness of reasoning and non-reasoning models.<n>Results show that reasoning models consistently improve over iterations, substantially outperforming non-reasoning models in leveraging feedback.
  arXiv  Detail & Related papers  (2026-06-16T04:47:42Z)
• PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalent [8.576619291429969]
  PCodeTrans bridges the gap between decompilation, recompilation, and rigorous function-level dynamic validation.<n>It achieves 100% function-level compilability on unstripped binaries alongside 99.55% and 99.89% test-validated behavioral consistency.
  arXiv  Detail & Related papers  (2026-03-16T05:54:24Z)
• Can LLMs Recover Program Semantics? A Systematic Evaluation with Symbolic Execution [1.5377279217726239]
  Obfuscation poses a persistent challenge for software engineering tasks such as program comprehension, maintenance, testing, and vulnerability detection.<n>We investigate whether fine-tuned language models can effectively deobfuscate programs and restore analyzability.
  arXiv  Detail & Related papers  (2025-11-24T13:55:20Z)
• Context-Guided Decompilation: A Step Towards Re-executability [50.71992919223209]
  Binary decompilation plays an important role in software security analysis, reverse engineering and malware understanding.<n>Recent advances in large language models (LLMs) have enabled neural decompilation, but the generated code is typically only semantically plausible.<n>We propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code.
  arXiv  Detail & Related papers  (2025-11-03T17:21:39Z)
• D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning [49.16469288280772]
  Decompilers reconstruct human-readable source code from binaries.<n>Despite recent advances, their outputs often suffer from syntactic and semantic errors and remain difficult to read.<n>With the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output.<n>We present D-LIFT, an enhanced decompiler-LLM pipeline with fine-tuned reinforcement learning.
  arXiv  Detail & Related papers  (2025-06-11T19:09:08Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
  The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
  arXiv  Detail & Related papers  (2025-02-17T12:38:57Z)
• Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement [43.2637367483626]
  Decompilation transforms compiled code back into a high-level programming language when source code is unavailable. Previous work has primarily focused on enhancing decompilation performance by increasing the scale of model parameters or training data for pre-training. By integrating these two methods, we achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%.
  arXiv  Detail & Related papers  (2024-06-25T02:37:53Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.