---
title: "CodeRipple: Wavelet-Based Detection of LLM-Generated Code"
description: "The paper tackles training-free detection of code written by large language models. 34 references."
authors: ["Xingyu Yao", "Zhendong Mao", "Quan Wang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1777/"
doi: "10.18653/v1/2026.acl-long.1777"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yao-2026-coderipple-wavelet-based-detection-llm/"
---
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# CodeRipple: Wavelet-Based Detection of LLM-Generated Code

**Authors:** Xingyu Yao, [Zhendong Mao](https://md.research.elamur.ai/free/en/authors/zhendong-mao/) and [Quan Wang](https://md.research.elamur.ai/free/en/authors/quan-wang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1777/) · [PDF](https://aclanthology.org/2026.acl-long.1777.pdf) · [DOI 10.18653/v1/2026.acl-long.1777](https://doi.org/10.18653/v1/2026.acl-long.1777) · [OpenAlex](https://openalex.org/W7166870782)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles training-free detection of code written by large language models. Existing zero-shot detectors were mostly borrowed from text detection. They summarize the Token Perplexity Sequence (TPS), the per-token negative log-probabilities from a proxy model, into global scores. For code, those scores become nearly indistinguishable, because programming tasks allow little variation in solutions. The authors find that the difference lies in local shape instead. Machine-written code shows short, isolated perplexity spikes, while human code fluctuates over longer stretches. Their detector, CodeRipple, analyzes the TPS as a signal using Daubechies wavelets in two ways:
- The Stationary Wavelet Transform, combined with sample entropy, describes fluctuation shape at several scales.
- The Discrete Wavelet Transform measures how energy is spread across those scales.

The two feature sets are combined into a single weighted score. The method was tested on AI Code Detection Education, HumanEval and CoDet-M4. It beat training-free baselines such as DetectCodeGPT, Binoculars and FastDetectGPT, at modest computational cost. This suggests that the timing pattern of token uncertainty is a more reliable signal for code provenance than aggregate perplexity.

## Key points
- The three benchmarks cover different challenges:
  - AI Code Detection Education contains ChatGPT code under 13 evasion strategies.
  - HumanEval contains outputs of 5 recent LLMs, including GPT-4-Turbo and Claude-3-Sonnet.
  - CoDet-M4 spans Python, Java and C++.
- On AI Code Detection Education (Strategy 1), CodeRipple reached an AUROC of 0.925. The strongest baseline, FastDetectGPT, scored 0.800, and DetectCodeGPT scored 0.600.
- Inference took about 165.8 ms per sample, more than 20× faster than DetectCodeGPT (about 3251 ms). Peak VRAM was roughly 13.3 GB, about half that of dual-model methods like Binoculars.
- Performance improved with more wavelet scales, and L = 4 was chosen. Results stayed stable across SWT/DWT weighting schemes and subsequence lengths. CodeLlama-7B served as a proxy model.
- Known weaknesses:
  - very short snippets;
  - templated human code, which produces false positives;
  - low-quality LLM code, which produces false negatives;
  - proxies that closely resemble the generator.
- Code is released at github.com/yaoxingyu77/CodeRipple.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Xingyu Yao, Zhendong Mao and Quan Wang. 2026. CodeRipple: Wavelet-Based Detection of LLM-Generated Code. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yao2026coderipple,
  title = {{CodeRipple: Wavelet-Based Detection of LLM-Generated Code}},
  author = {Xingyu Yao, Zhendong Mao and Quan Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1777},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1777}
}
```

- [← previous CodeGuard: Improving LLM Guardrails in CS Education](https://md.research.elamur.ai/free/en/papers/raihan-2026-codeguard-improving-llm-guardrails-cs/)
- [next → CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum](https://md.research.elamur.ai/free/en/papers/wen-2026-coderise-bootstrapping-llms-ultra-low/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/)

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*Updated 2 November 2026*