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REVIEW 4 major objections 5 minor 25 references

Applying the Chinese Wall Reverse Engineering Technique to Large Language Model Code Editing

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A strong model's comment annotations lift weaker code models' edit success by up to 66 percent.

desk verdict A prompt-format confound likely inflates the headline Comma result, but the clear writing and honest framing make this a useful cautionary read. read the letter →

arxiv 2507.15599 v1 pith:2QLM6JON submitted 2025-07-21 cs.SE cs.LG

Manatsawin Hanmongkolchai This is my paper
classification cs.SEcs.LG
keywords codeeditingChineseWalltechniqueLLMannotationinference-timetransferlicense-compliantmodelsCanItEditbenchmarkmodelenhancementcomment-guideddecoding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that a weak, license-curated code model can be made more capable at edit time by having a strong proprietary model write detailed step-by-step comments into the target code, which the weak model then follows. The motivation is legal and practical: top large language models are trained on undisclosed data that may include copyrighted code, while models trained only on permissive or public-domain sources are too weak to be useful. On the CanItEdit benchmark, the technique lifts Comma v0.1 1T's pass@20 from 20.00 to 33.33 (a 66% relative gain) and Starcoder2 Instruct's pass@1 from 35.10 to 42.05 (about 20%). The paper positions this as an inference-time transfer channel that could become more valuable once truly public-domain code models exist. The author explicitly notes the technique is an application, not proof of full legal distinctness, and that today's limited supply of well-performing, license-safe models constrains practical use.

What carries the argument

The load-bearing mechanism is the annotated source code itself, used as a one-shot specification channel. A strong model is prompted once to embed EDIT-prefixed comments at the exact locations where edits are needed, with each comment describing one step in novice-readable terms; the editor model is then prompted to follow those comments and remove them. This separates the reasoning-heavy task of planning an edit from the mechanical task of writing the edit, and it requires no weight updates, no data disclosure, and no exposure of the strong model's training corpus. The Chinese Wall name comes from a reverse-engineering procedure in which one team writes specifications from proprietary artifacts and a second team, with no access to the artifacts, builds from the specifications alone. Here the wall is informational: the editor sees the problem statement and the comments, but not the strong model's other reasoning or its proprietary data.

What would settle it

Compare the pass@20 gains on a set of editing problems created after the annotator model's training cutoff, or on a private never-published benchmark, using the same pipeline; if the gains shrink to the noise level, the reported improvements are likely benchmark contamination in the annotator rather than evidence for a general Chinese Wall transfer.

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Extended reading notes

Core claim

The central claim, stated in the paper's own terms, is that a Chinese Wall pipeline—strong model writes specifications, weak model implements—transfers part of the strong model's editing ability to the weak model without retraining or exposing hidden training data. Concretely, Gemini 2.5 Pro receives the original problem statement and source code and returns annotated code whose comments, prefixed with EDIT, break the required edit into novice-level steps. The editor model, Comma v0.1 1T, Starcoder2 Instruct, or Phi-4, receives only the annotated code plus the same problem statement and must produce the edit. Table 2 shows the largest gains on the weakest models: Comma's pass@20 rises from 20.00 to 33.33 and pass@1 from 9.14 to 21.24, while Starcoder2 Instruct's pass@1 rises from 35.10 to 42.05. The paper treats the result as evidence that weaker but ethically aligned models can perform tasks otherwise reserved for stronger models, while cautioning that the method does not always help—Phi-4's pass@20 falls slightly even as its pass@1 improves by a quarter.

Load-bearing premise

The load-bearing premise is that Gemini 2.5 Pro, the annotator, has not memorized the public CanItEdit benchmark during its undisclosed training; the paper does not test this, so if it fails, the measured gains could be answer leakage rather than a transferable technique.

Editorial extensions

If this is right

  • Comma v0.1 1T's pass@20 on CanItEdit rises from 20.00 to 33.33 and its pass@1 from 9.14 to 21.24, turning a mostly nonfunctional model into one that completes a third of the editing problems.
  • One annotation pass from the strong model can be reused across all editor models, since the same Gemini 2.5 Pro annotations are fed to Comma, Starcoder2 Instruct, and Phi-4.
  • The effect is not monotonic in model strength: Phi-4's pass@1 improves to exceed Gemini's own pass@1, but its pass@20 decreases slightly, so annotation-following helps most when the editor model is weakest.
  • Practical deployment remains limited until a code model trained solely on public-domain content exists; Comma and Pleias are trained on permissive data, not unrestricted public domain.
  • The pipeline gives no guarantee of legal distinctness; the paper explicitly does not claim that every legal requirement of the original reverse-engineering technique is satisfied.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the effect survives a contamination check, the central implication is that a proprietary model's capability can be exported to open models as structured comments at inference time, which is a cost-effective alternative to distillation or instruction tuning that requires no access to weights or training data.
  • A direct stress test would be to generate annotations from a strong model on a benchmark created after that model's knowledge cutoff, or on a private held-out set, and compare gains; sustained gains would separate genuine teaching from memorized answers.
  • The mechanism suggests a testable relationship between annotation granularity and transfer: varying the number of EDIT steps and their placement relative to edit sites should modulate the gain, revealing how much of the transfer is instructional versus merely a prompt rewrite.
  • The same annotation channel could transfer stylistic or formatting constraints, not just code edits, by changing what the comments ask for—a route the paper does not explore.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a "Chinese Wall" technique for code editing: a strong proprietary model (Gemini 2.5 Pro) is prompted to add detailed `EDIT:` comments to the input code, and weaker open-weight models (Comma, Starcoder2 Instruct, Phi-4) are then asked to follow these comments. The authors evaluate on the CanItEdit benchmark, reporting that the technique improves Comma's pass@20 from 20.00 to 33.33 (relative +66%) and Starcoder2 Instruct's pass@1 from 35.10 to 42.05 (relative +20%). They position the work as a way to make permissively-licensed models more usable while mitigating copyright concerns, while acknowledging that the technique does not guarantee legal cleanliness.

Significance. If the reported gains hold under controlled comparison, the technique would be a simple and inexpensive way to boost the code-editing abilities of models trained on permissively licensed data, with direct relevance to the ongoing copyright debate. The paper is commendably honest about its legal caveats and includes sample outputs. However, the central empirical claim currently rests on an uncontrolled prompt-format difference for the main beneficiary (Comma), a potential benchmark-contamination channel via the undisclosed training data of the annotator model, and the absence of any uncertainty quantification. These issues must be addressed before the improvement can be attributed to the Chinese Wall annotations rather than to artifacts of the evaluation protocol.

major comments (4)
  1. [Section 4.1 and Appendix A] The treated condition for Comma appears to include a one-shot demonstration prompt (a full worked example of the "add a sub function" task) that the baseline condition lacks. The baseline is described as using "the original prompts" adapted to Ollama, with no mention of a demonstration; the Chinese Wall condition is described as "using one-shot prompts for both models" and the only concrete prompt shown for Comma in Appendix A contains a full in-context example. Since Comma is a base model, the addition of an in-context example alone can substantially improve output format adherence and task execution, independent of the Gemini annotations. The paper never states whether the Table 2 runs for Comma used this demonstration prompt. If they did, the +66% improvement is not cleanly attributable to the annotations; if they did not, the evaluation is underspecified. Please report the exact prompts used for all editor models in the main evaluation and add a control condition that uses the same one-shot demonstration but without the Gemini-generated annotations (or with generic comments).
  2. [Section 3.2 and Section 4.1] The annotator model, Gemini 2.5 Pro, has undisclosed training data, and CanItEdit is a public benchmark. The paper does not address the possibility that Gemini has memorized the benchmark tasks, in which case the annotations could encode near-solution content rather than general editing instructions. The sample annotation in Appendix B (test case 10) already contains comments that closely track the correct solution (e.g., "The header is the first item in the list returned by `self.contents()`" and "Return the first element from that list"). This makes the measured improvement for Comma and Starcoder2 compatible with an answer-leakage story. To support the paper's general claim, please either present evidence that the annotations do not contain problem-specific solution details beyond generic instructions, or evaluate on a held-out variant of the benchmark that Gemini cannot have memorized. At minimum, this limitation should be explicitly discussed and its impact on the conclusions assessed.
  3. [Table 2 and Section 4.1.1] The headline differences are reported without any uncertainty quantification. For Comma, pass@20 rises from 20.00 to 33.33; on 105 problems this corresponds to about 21 vs. 35 successful problems, and the two-proportion standard error is roughly 6 percentage points, so the difference is only marginally significant. Similarly, the Starcoder2 pass@1 improvement, while larger in terms of samples, is reported without confidence intervals. Please provide confidence intervals or significance tests for the key comparisons, and be explicit about the number of problems/samples underlying each reported percentage.
  4. [Section 4.1 (Implementation)] The description of the evaluation protocol is not sufficiently detailed for replication. The paper says "using one-shot prompts for both models" and "we pass the Geminia-annotated code into the editor model, along with the problem description," but the full prompt template is not given for the main CanItEdit runs. Appendix A shows only the Quake fast-inverse-square-root test, which is not part of the benchmark. Please provide the complete prompts and any runtime-specific formatting modifications for the baseline and treated conditions for each editor model, ideally in an appendix.
minor comments (5)
  1. [Table 2] The row labels "Gemini 2.5 Pro + phi4" and "Gemini 2.5 Pro" are ambiguous because the latter could be misread as the annotator-aided condition and the former as something involving the annotator. Please clarify that "Gemini 2.5 Pro" by itself means the model solving the original benchmark directly.
  2. [Section 4.1 (Evaluation metrics)] The definition of pass@1 as "the percent of passing attempts in all 2,100 samples" should state explicitly that this is the fraction of the 2,100 sampled edits that passed all tests, and clarify whether the 2,100 samples are 105 problems x 20 completions.
  3. [Section 1 and Footnote 1] There is a typo in the footnote: "do not claims" should be "does not claim." Also, the term "Chinese Wall" is overloaded in security and legal contexts (typically a conflict-of-interest barrier); while the reverse-engineering analogy is explained, consider adding a note distinguishing the two uses.
  4. [Section 2 (Related Work)] The distinction from Aider's architect mode is stated, but the contribution could be sharpened: in Aider's mode the reasoning model may emit code, whereas here the strong model is strictly limited to comments. A table contrasting the two would help the reader understand the novelty.
  5. [Section 4.1.1 (Results)] Relative improvements of 66% and 120% for Comma are reported without absolute context; given the low baseline (pass@1 9.14), a small absolute gain is amplified. Reporting both absolute and relative changes would be more informative.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical benchmark evaluation is self-contained and externally grounded.

full rationale

The paper's central claim is an empirical measurement, not a derivation: feeding Gemini 2.5 Pro's EDIT comments to Comma and Starcoder2 improves their pass@1/pass@20 on the external CanItEdit benchmark (Section 4.1.1, Table 2). The annotations are generated once from each problem's task description and starting code, then reused across all editor models; the pass@k metric and benchmark come from Cassano et al. [22], an external source. There is no fitted parameter renamed as a prediction, no equation that reduces to its own input, and no author-invoked uniqueness theorem. The only self-reference is the author's Hugging Face 8-bit quantization of Comma [7], which is an experimental artifact rather than load-bearing evidence for the technique. Two limitations in the paper are real but are not circularity: (i) Gemini's undisclosed training data may include CanItEdit, making the annotations potentially contaminated by benchmark answers (Sections 3.2 and 4.1); and (ii) for Comma, the treated condition in Appendix A adds a worked few-shot example that the baseline's 'original prompts' (Section 4.1) may lack, so the measured gain could partly reflect prompt formatting. Both are empirical confounds or contamination risks, not definitional reductions of the claimed result to its inputs, so they do not raise the circularity score.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the benchmark being a valid proxy, the teacher model not having memorized the benchmark, and the prompt adaptation not biasing baselines. No free parameters or invented entities are introduced; the Chinese Wall is an analogy, not a new component.

assumptions (3)
  • domain assumption CanItEdit is a valid proxy for general code-editing ability
    The paper evaluates only on this 105-problem Python benchmark and generalizes to 'complicated tasks'.
  • domain assumption Gemini 2.5 Pro has not memorized CanItEdit solutions
    The annotator is a proprietary model with undisclosed training data; benchmark contamination would invalidate the attribution of improvement to the technique.
  • domain assumption The Ollama prompt adaptations preserve the original benchmark difficulty
    The authors modified the benchmark to support Ollama; if the adaptation disadvantaged baseline runs, the measured improvement is inflated.

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Cite this review

Pith. "Pith review of Applying the Chinese Wall Reverse Engineering Technique to Large Language Model Code Editing." pith.science (2026). https://pith.science/paper/2QLM6JON

@misc{pith2026250715599,
  author       = {Pith},
  title        = {Pith review of: Applying the Chinese Wall Reverse Engineering Technique to Large Language Model Code Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2QLM6JON}},
  note         = {Machine review of arXiv:2507.15599}
}
read the original abstract

Large language models for code (Code LLM) are increasingly utilized in programming environments. Despite their utility, the training datasets for top LLM remain undisclosed, raising concerns about potential copyright violations. Some models, such as Pleias and Comma put emphasis on data curation and licenses, however, with limited training data these models are not competitive and only serve as proof of concepts. To improve the utility of these models, we propose an application of the "Chinese Wall" technique, inspired by the reverse engineering technique of the same name -- a high quality model is used to generate detailed instructions for a weaker model. By doing so, a weaker but ethically aligned model may be used to perform complicated tasks that, otherwise, can only be completed by more powerful models. In our evaluation, we've found that this technique improves Comma v0.1 1T's performance in CanItEdit benchmark by over 66%, and Starcoder2 Instruct by roughly 20% compared to when running the same model on the benchmark alone. The practical application of this technique today, however, may be limited due to the lack of models trained on public domain content without copyright restrictions.

Figures

Figures reproduced from arXiv: 2507.15599 by the authors.

Figure 1
Figure 1. Traditional “Chinese Wall” reverse engineering flow [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Implementation data flow 4.1.1 Results Descriptive Lazy Model pass@1 pass@20 ExcessCode pass@1 pass@20 ExcessCode Gemini 2.5 Pro + phi4 68.67 72.38 0.14 ± 0.06 55.33 60.00 0.28 ± 0.19 Gemini 2.5 Pro 57.66 94.29 0.31 ± 0.09 53.60 86.67 0.41 ± 0.15 phi4 54.71 73.33 0.47 ± 0.17 49.62 66.67 0.42 ± 0.13 Gemini 2.5 Pro + starcoder2:instruct 42.05 71.43 0.19 ± 0.08 34.67 55.24 0.30 ± 0.19 starcoder2:instruct 35.10 60.00 0.… view at source ↗

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Reviewed August 6, 2026 · model on record in the stance chip above.