---
title: "LAMCL — paper by Zhou et al. (2026) — summary and ideas"
description: "The authors propose LAMCL, which has two components. 37 references. Among them: 0."
authors: ["Bing Zhou", "Zhe Huang", "Shilei Tan", "Kai Zhao", "Zhou Yongcheng"]
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.1118/"
doi: "10.18653/v1/2026.acl-long.1118"
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/zhou-2026-lamcl-length-aware-momentum-contrastive/"
---
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# LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection

**Authors:** Bing Zhou, Zhe Huang, Shilei Tan, [Kai Zhao](https://md.research.elamur.ai/free/en/authors/kai-zhao/) and Zhou Yongcheng · **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.1118/) · [PDF](https://aclanthology.org/2026.acl-long.1118.pdf) · [DOI 10.18653/v1/2026.acl-long.1118](https://doi.org/10.18653/v1/2026.acl-long.1118) · [OpenAlex](https://openalex.org/W7166836942)

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

## Summary
The paper addresses a difficult detection case: text that a human wrote and an LLM later rewrote or polished. Such revisions differ from the source only through small lexical edits, so watermarking-, logit- and training-based detectors often miss them, particularly when texts are short. The authors propose LAMCL, which has two components. The Enhance Before Detection (EBD) module asks an LLM to reprocess the input and joins the original and reprocessed versions. A RoBERTa-base encoder encodes both. A Consistency Measurement block then uses multi-head attention to compute cross- and self-consistency and fuses these features with adaptive weights. The second component is a Momentum Contrastive Learning setup with a Length-aware Weighting (LW) module. It creates positive pairs through dropout noise and selects hard negatives using text length and labels, which reduces the attribution ambiguity of short texts. The authors also build a benchmark spanning several LLMs, revision prompts and scenarios. LAMCL beats existing detectors in AUROC, including on short and out-of-domain texts, which makes it useful for spotting subtle AI-assisted edits.

## Key points
- **Benchmark coverage:** Revisions were produced by Qwen3, Llama3, DeepSeek and GPT using both rewrite and polish prompts. Evaluation includes cross-prompt and cross-LLM settings.
- **Heterogeneous test sets:** Training used the dataset from Chen et al. (2025). Testing used XSum, WritingPrompts, PubMedQA and SQuAD, with DeepSeek revisions. LAMCL reached an average AUROC of 0.9555, compared with 0.9318 for SIMLLM and 0.8353 for ImBD.
- **Ablation on XSum (average AUROC):**
  - MCL baseline: 0.9597
  - adding LW: 0.9604
  - adding EBD: 0.9648
  - both modules: 0.9672
- **Short texts (under 30 words, Qwen3 revisions):**
  - rewrite task: AUROC 0.9246 vs. 0.9084 for MCL
  - polish task: AUROC 0.9262 vs. 0.8950 for MCL
- **Limitations and code:** Detection speed depends on LLM inference time. The work does not cover human edits of machine-generated text. Code is released at github.com/hangtze/LAMCL.

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

## Institutions

- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 145 works · 114,409 citations
- [Chongqing University](https://md.research.elamur.ai/free/en/universities/chongqing-university/) — University, Chongqing, China · 14 works · 318 citations

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## Cite

Cite as: Bing Zhou, Zhe Huang, Shilei Tan, Kai Zhao and Zhou Yongcheng. 2026. LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection. 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{zhou2026lamcl,
  title = {{LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection}},
  author = {Bing Zhou, Zhe Huang, Shilei Tan, Kai Zhao and Zhou Yongcheng},
  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.1118},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1118}
}
```

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*Updated 2 November 2026*