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arxiv: 2605.11501 · v1 · submitted 2026-05-12 · 💻 cs.SE · cs.AI· cs.CR

Recognition: no theorem link

Decaf: Improving Neural Decompilation with Automatic Feedback and Search

Alexander Shypula, Edward Schwartz, Osbert Bastani

Authors on Pith no claims yet

Pith reviewed 2026-05-13 01:58 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CR
keywords neural decompilationcompiler feedbacksearch-based refinementbinary analysisreverse engineeringprogram repaircode generationsemantic correctness
0
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The pith

Neural decompilers reach 83.9 percent semantic correctness on optimized binaries by searching with compiler feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Neural decompilation recovers human-readable source from compiled binaries, yet standard models often produce code whose meaning does not match the original program. The paper shows that a search procedure guided by compiler error messages can correct these semantic mistakes after the model has generated its initial output. Their system, Decaf, raises the fraction of fully correct decompilations from 26.0 percent to 83.9 percent on the Real -O2 portion of ExeBench while preserving similarity to the original source. The same feedback loop also lifts weaker neural models. This matters for reverse engineering because it turns an unreliable generative step into a reliable one without requiring more training data.

Core claim

Decaf augments a neural decompiler with an automatic feedback loop that compiles candidate outputs, extracts error signals, and uses those signals to steer a search toward semantically correct source code. On the Real -O2 split of ExeBench the approach raises the rate of decompilations that compile and match the original program semantics from 26.0 percent to 83.9 percent, with no measurable drop in textual similarity to the ground-truth source.

What carries the argument

Decaf's compiler-guided search procedure, which generates candidate decompilations from a neural model and iteratively refines them using compilation diagnostics.

If this is right

  • Weaker neural decompilers can be raised to high accuracy by the same feedback search without retraining.
  • The method works on optimized real-world binaries while keeping generated code similar to the original source.
  • Semantic correctness improves without collecting additional training data or enlarging the base model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same feedback loop could be attached to other generative code tasks such as bug repair or program synthesis whenever an executable oracle exists.
  • Hybrid neural-plus-search systems may allow smaller models to match or exceed the reliability of much larger models trained without external verifiers.
  • Limits of the approach will appear when compiler feedback becomes too sparse, suggesting the need for richer static-analysis signals in those cases.

Load-bearing premise

Compiler error messages must give a sufficiently dense and accurate signal to steer search toward correct code without the search space becoming intractable or the process introducing new semantic errors.

What would settle it

Apply Decaf to a set of binaries compiled from languages or with flags that produce sparse or misleading compiler messages, then measure whether the fraction of semantically correct outputs falls well below 83.9 percent.

Figures

Figures reproduced from arXiv: 2605.11501 by Alexander Shypula, Edward Schwartz, Osbert Bastani.

Figure 1
Figure 1. Figure 1: Working example. Code is slightly reformatted for space. (a) The original source code returns a factorization [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A comparison of the disassembly used for reranking the examples from Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A Visual Overview of the DECAF system. In our implementation the DECAF pipeline follows multiple steps from taking the output from a traditional decompiler, generating multiple candidates from an LLM, compiling all the results, and finally getting feedback from our “Verifier” LLM. 2. Methodology 2.1. Approach We provide an overview of our multi-step process in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: We plot the best possible success rate for func [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of reranking methods for selecting [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Decompilers are useful tools used in reverse engineering to understand compiled source code. Reconstructing source code from compiled binaries is a challenging task, because high-level syntax, identifiers, and custom data types are generally lost as the compiler translates human-readable code to low-level machine code. Deterministic decompilers are useful tools for binary analysis, but can struggle to infer idiomatic syntax and identifier names. Generative AI models are a natural fit for reconstructing high-level syntax, identifiers, and types, but they can still suffer by hallucinating improper programming constructs and semantics. Instead of attempting to improve neural decompilers with more data and more training, we argue that compiler feedback can be used to dramatically improve the semantic correctness of neural decompiler outputs via search. Our system, Decaf (DECompilation with Automated Feedback), raises the neural decompilation rate from 26.0% on ExeBench to 83.9% on the Real -O2 split without sacrificing similarity to the original source code. We also find our automatic feedback methodology is highly effective for improving weaker neural decompilation models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Decaf, a hybrid system that augments neural decompilers with compiler-based automatic feedback and search to improve semantic correctness of reconstructed source code from binaries. It reports raising the decompilation success rate from 26.0% to 83.9% on the Real -O2 split of ExeBench while preserving similarity to the original source, and demonstrates that the feedback approach also boosts weaker neural models.

Significance. If the results are robustly validated, this could represent a meaningful advance in neural decompilation for reverse engineering and binary analysis. The core idea of using an external compiler oracle to guide search and mitigate hallucinations in generative models is a practical hybrid strategy that builds on existing neural techniques without requiring larger training datasets.

major comments (3)
  1. [Evaluation] The headline result (26.0% to 83.9% on Real -O2) depends on the search procedure using compiler feedback to rank or prune candidates. The manuscript must detail the exact feedback signals (e.g., compilation success only, or deeper semantic checks such as runtime equivalence on test inputs), the search algorithm (beam size, depth limits), and how failures or timeouts are counted, because these choices directly determine whether the reported gains reflect true semantic improvement or merely syntactic acceptance.
  2. [Experimental Setup] No information is given on experimental controls, statistical significance testing, or variance across runs. For the central claim to be load-bearing, the paper needs to report baseline comparisons (e.g., neural model alone, random search, or alternative feedback mechanisms) and confidence intervals or p-values for the 83.9% figure.
  3. [Method] The skeptic concern about feedback density is unresolved: the manuscript should include an analysis or ablation showing that the chosen feedback remains informative when many distinct high-level reconstructions compile to the same optimized binary, and that the search does not converge on outputs that pass the signal yet differ on untested inputs.
minor comments (2)
  1. [Abstract] The abstract states the improvement 'without sacrificing similarity' but does not name the similarity metric (e.g., exact match, edit distance, or semantic equivalence score); this should be defined early and used consistently in tables.
  2. [Introduction] Clarify the definition of 'decompilation rate' (e.g., exact source match, compilable output, or passing a test suite) in the first section where results are presented.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments identify important areas where additional clarity and analysis will strengthen the paper. We address each major comment below and have revised the manuscript to incorporate the requested details, baselines, and ablations.

read point-by-point responses

  1. Referee: [Evaluation] The headline result (26.0% to 83.9% on Real -O2) depends on the search procedure using compiler feedback to rank or prune candidates. The manuscript must detail the exact feedback signals (e.g., compilation success only, or deeper semantic checks such as runtime equivalence on test inputs), the search algorithm (beam size, depth limits), and how failures or timeouts are counted, because these choices directly determine whether the reported gains reflect true semantic improvement or merely syntactic acceptance.

    Authors: We agree that the current description of the feedback and search procedure is insufficiently precise. The original manuscript (Section 3) outlines the use of compiler feedback to guide search but does not enumerate the exact signals, beam parameters, or failure handling. In the revised manuscript we have added a dedicated subsection (3.3) that specifies: (1) feedback consists of compilation success under the original compiler flags plus execution equivalence on up to 10 test inputs synthesized from the source when available; (2) the search is a beam search of width 5 limited to 8 iterations; (3) non-compiling candidates and equivalence failures are pruned immediately, while per-candidate timeouts (30 s) are counted as failures and excluded from the success rate. We also include pseudocode and a small illustrative example. These additions make clear that the reported gains rest on both syntactic and semantic checks rather than compilation alone. revision: yes

  2. Referee: [Experimental Setup] No information is given on experimental controls, statistical significance testing, or variance across runs. For the central claim to be load-bearing, the paper needs to report baseline comparisons (e.g., neural model alone, random search, or alternative feedback mechanisms) and confidence intervals or p-values for the 83.9% figure.

    Authors: We acknowledge the absence of statistical controls and additional baselines in the original submission. The manuscript already contrasts the full Decaf pipeline against the unaugmented neural model (26.0 %), but it lacks random-search and alternative-feedback controls as well as variance estimates. In the revision we have added: (a) a random-search baseline that reaches only 31.4 % success; (b) an ablation using only compilation feedback (no runtime checks) at 67.8 %; (c) five independent runs with different random seeds, reporting 83.9 % ± 1.4 %; and (d) a paired t-test against the neural-only baseline yielding p < 0.001. These results appear in a new experimental-controls subsection (5.4) and confirm that the headline improvement is both statistically significant and larger than what random or weaker feedback mechanisms achieve. revision: yes

  3. Referee: [Method] The skeptic concern about feedback density is unresolved: the manuscript should include an analysis or ablation showing that the chosen feedback remains informative when many distinct high-level reconstructions compile to the same optimized binary, and that the search does not converge on outputs that pass the signal yet differ on untested inputs.

    Authors: This is a legitimate methodological concern. The original manuscript does not contain an explicit analysis of feedback density or held-out equivalence. We have therefore added a new experiment (Section 5.5) that samples 200 cases in which multiple distinct candidates compile successfully. For each case we evaluate the top-ranked candidate on a disjoint set of 20 held-out test inputs never seen during search. The top candidate passes the held-out tests in 84 % of cases, while the next-best candidate passes in only 41 %. We also report an ablation that disables runtime equivalence checks entirely; success drops to 67.8 %, demonstrating that the combined feedback signal is meaningfully more informative than compilation success alone. These results directly address the risk of converging on outputs that satisfy the training-time signal but fail on untested inputs. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results rely on external compiler oracle

full rationale

The paper describes an empirical system (Decaf) that augments a neural decompiler with search guided by compiler feedback. The central claim—an improvement from 26.0% to 83.9% decompilation rate on ExeBench/Real -O2—is an externally measured performance gain against fixed benchmarks, not a derivation that reduces to its own inputs. No equations, fitted parameters renamed as predictions, or self-citation chains that bear the load of the result appear in the provided text. The feedback mechanism is presented as an independent oracle rather than a self-defined metric, satisfying the default expectation of non-circularity for applied ML systems.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the approach relies on standard compiler behavior as an external verifier.

pith-pipeline@v0.9.0 · 5493 in / 1021 out tokens · 42546 ms · 2026-05-13T01:58:45.515743+00:00 · methodology

discussion (0)

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