NormDetect is a new detection method that anchors a learned per-section head to a normalized zero-shot score to improve low‑false‑positive detection of machine‑generated web text. By blending the two scores with a weight chosen on training folds under a human false‑positive rate cap, it raises recall by 5.0 points over normalized Binoculars at a calibrated 0.1% FPR on the RAID benchmark. The method maintains a human FPR bound of at most 1% in every section, outperforming other detectors within this bound.
arXiv:2607. 04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge.
The paper introduces a new metric, Relative Peak Prominence Rate (RelPPR), to detect black-box LLM-generated text by measuring localized entropy fluctuations. It shows that existing dual-model and resampling-based detectors fail on instruction-conditioned text, and that RelPPR outperforms them across diverse models and datasets. The authors provide code for their detector on GitHub.
The study evaluates machine‑generated text detectors on scientific literature, finding that many detectors perform near perfectly on certain benchmarks due to superficial formatting cues rather than genuine linguistic differences. A logistic regression using 27 superficial features achieved macro‑F1 scores between 0.833 and 0.997, outperforming other detectors on arXiv subsets. The most powerful cue was the PDF extraction artifact—human texts preserve line breaks while generated texts appear as a single block—allowing classification at 0.996 AUC. By introducing format counterfactuals and normalizing formatting, the authors demonstrate that re‑wrapping machine‑written abstracts can flip 20–27% of detector decisions, reducing the generalization gap and improving out‑of‑domain performance.
arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.
arXiv:2505. 14608v3 Announce Type: replace-cross Abstract: Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable.
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).
arXiv:2604. 25860v2 Announce Type: replace-cross Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints.
The paper investigates how to detect text generated by large language models (LLMs) when the data has been edited or contaminated. By modeling human and machine text as finite-order Markov processes with Huber contamination, the authors derive an exact boundary that determines when reliable detection is possible. They show that a clipped likelihood-ratio test can achieve vanishing worst‑case errors below this boundary and that clipping improves robustness across several detectors and datasets, yielding significant gains in true‑positive rates at a fixed false‑positive rate.
arXiv:2606. 31074v1 Announce Type: cross Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics.
TextSeal2 is a distortion‑free watermarking method for large language models that maintains signal integrity during speculative decoding. It merges multiple keys into a single virtual key using a Gaussian copula, ensuring that the acceptance rate and output diversity remain unchanged while adding no measurable inference latency. Experiments on three LLMs show that TextSeal2 detects 8–22 percentage points more 100‑token answers than its predecessor TextSeal and outperforms textGrain under speculative decoding, all without post‑logits optimization.
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...
arXiv:2603. 18482v2 Announce Type: replace-cross Abstract: Standard decoding strategies for text generation, including top-$k$, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting outputs to high-probability regions.