arXiv Computation and Language August 27, 2026 By Haitong Luo, Xuying Meng, Weiyao Zhang, Wenji Zou, Shengfeng Lou, Xuefeng Jiang, Chungang Lin, Yujun Zhang

Unveiling Spectral Mechanisms in Training-Free LLM Text Detection

Read the original on arXiv Computation and Language →

The paper investigates training‑free detection of machine‑generated text using spectral analysis. It shows that spectral energy correlates with variance in token probability trajectories and that human writing produces characteristic fluctuations, termed "generative vitality." The authors find that spectral signals are strongest for long, continuous, constrained generations, while shorter or mixed texts require additional confidence‑based metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

llms

Hugging Face Trending Papers

Aug 25

Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text

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...

benchmarks

arXiv AI

Jul 7

Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability

arXiv:2607. 04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge.

By Christopher Nassif, Josh F. Cooper

llms benchmarks safety

arXiv Computation and Language

Oct 5

Ontological Instability and Statistical Amplification: The Paradox of "Humanizing" LLM-Generated Text

The paper investigates why supervised AI‑text detectors, specifically a RoBERTa‑based model, can be fooled by subtle changes in language. By applying semantic, structural, and tokenizer‑level perturbations to a large dataset and controlled Mistral‑7B‑Instruct outputs, the authors show that increasing verb diversity makes machine text easier to detect and that detection scores correlate with statistical complexity, leading to a high false‑positive rate on formal human writing. They also evaluate an event‑based latent space detector, finding that paraphrasing and homoglyphs significantly alter extracted event sequences and verbs, yet the detector’s performance remains modest (AUC 0.577).

By Claudiu Creanga, Liviu Dinu

llms diffusion nlp benchmarks

arXiv AI

Jul 24

Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.

By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu

llms

arXiv Machine Learning

Oct 2

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...

By Luxu Liang, Xiang Li

llms safety

arXiv Computation and Language

Oct 8

EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors

arXiv:2610.09976v1 Announce Type: new Abstract: AI-generated text (AIGT) detection can be sensitive to the decoding choices of the source large language model (LLM). We observe that perturbing next-t...

By Jicheng Zhou, Kahim Wong, Jialong Wang, Jiantao Zhou

llms fine-tuning