CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference

Dimensions overall score 8.0

• CoDe-R refines decompiler output using LLMs with rationale guidance and adaptive inference, achieving state-of-the-art re-executability for lightweight models. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re-execute.problem 35%
  Evidencepartial

  CoDe-R refines decompiler output using LLMs with rationale guidance and adaptive inference, achieving state-of-the-art re-executability for lightweight models. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re-execute.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re-execute.method 35%
  Evidencepartial

  Binary decompilation is a critical reverse engineering task aimed at reconstructing high-level source code from stripped executables. Although Large Language Models (LLMs) have recently shown promise, they often suffer from "logical hallucinations" and "semantic misalignment" due to the irreversible semantic loss during compilation, resulting in generated code that fails to re-execute.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• ScienceToStartup currently rates this 8.0/10 on the public viability pass. Evaluation on the HumanEval-Decompile benchmark demonstrates that CoDe-R (using a 1.3B backbone) establishes a new State-of-the-Art (SOTA) in the lightweight regime. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.result 35%
  Evidencepartial

  ScienceToStartup currently rates this 8.0/10 on the public viability pass. Evaluation on the HumanEval-Decompile benchmark demonstrates that CoDe-R (using a 1.3B backbone) establishes a new State-of-the-Art (SOTA) in the lightweight regime. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

• Decompilation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.why now 35%
  Evidencepartial

  Decompilation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.

  Implicationpartial

  Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.

  Verificationpartial

  partial

Competitive landscape

CoDe-R refines decompiler output using LLMs with rationale guidance and adaptive inference, achieving state-of-the-art re-executability for lightweight models.