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A reviewer recounts flagging two papers with fabricated authors that were accepted as orals, and reports that 68% of 22 reviewed submissions contained fabricated citations, LLM-generated content, or fake author lists. Multiple studies confirm tens of thousands of such papers in 2025 alone, with peer reviews increasingly AI-generated.
Pangram Labs announces Pangram 4, its most powerful AI detector yet, with significantly reduced false positive and false negative rates, robust detection across frontier models, and new image scan features.
Introduces DWT-Fusion, a training-free framework using discrete wavelet analysis of token log-probabilities for detecting LLM-generated text, achieving strong AUROC results on multiple datasets.
This paper investigates how well LLMs can detect their own generated content in educational contexts, finding that detection accuracy varies by task type and is unreliable for short-answer questions.
A developer explores using classical machine learning to detect LLM-generated web fiction, creating an open-source demo and model with ~85% single-sentence accuracy.
The paper introduces Telescope Perplexity, a metric that measures token repetition probability to detect LLM-generated text in a zero-shot manner, achieving state-of-the-art or competitive performance across diverse datasets.
This blog post explores using reinforcement learning to train a small model to detect writing styles of different LLMs, providing code and traces.
Pangram, a tool for detecting LLM use, reveals that many academics do not understand the difference between false positives and false negatives.
This paper evaluates the extent of undisclosed LLM-generated content in parliamentary texts from the UK and Sweden by training an interpretable text classifier, finding a steady increase in undisclosed LLM use from 2022 onwards.
Rodrigo Arias Mallo proposes using asciinema recordings as proof of human authorship for FOSS contributions to Dillo, arguing that LLMs struggle to convincingly generate such recordings.
DetectRL-X is a comprehensive multilingual benchmark for evaluating LLM-generated text detectors across 8 languages and 6 domains, including stress testing with AI-assisted writing operations and perturbations. It reveals strengths and limitations of current detectors in multilingual scenarios.
This paper addresses the degradation of likelihood-based machine-generated text detectors by identifying a Simpson's paradox in token-score aggregation. It proposes a learned local calibration step that significantly improves detection performance across various models and datasets.
Proposes LiSCP, a lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content, focusing on feature stability under adversarial manipulation. Achieves superior performance on in-domain and cross-domain detection with notable robustness.
Andrew Kelley, creator of Zig, argues that LLM-assisted contributions are detectable through distinct mistakes and a 'digital smell,' comparing it to smoking in a non-smoking house.
LLMSniffer is a detection framework that fine-tunes GraphCodeBERT with supervised contrastive learning to distinguish AI-generated code from human-written code, achieving 78% accuracy on GPTSniffer and 94.65% on Whodunit benchmarks. The approach addresses critical challenges in academic integrity and code quality assurance by combining code-structure-aware embeddings with contrastive learning and comment removal preprocessing.