arXiv AI June 12, 2026 By Igor Itkin

Quickest Detection of Hallucination Onset: Delay Bounds and Learned CUSUM Statistics

arXiv:2606. 12476v1 Announce Type: cross Abstract: Token-level hallucination detectors are evaluated as classifiers, by AUC over all tokens, yet a streaming monitor is judged by its reaction time: the number of tokens that pass between the onset of a hallucination and the alarm.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv AI

1d ago

DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models

arXiv:2608. 14632v1 Announce Type: cross Abstract: Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation.

By Xin Zhang, Yili Wang, Yue Tan, Xin He, Yanyu Qian, Yixin Liu, Yi Chang, Shirui Pan, Xin Wang

llms diffusion benchmarks safety

arXiv AI

Jul 28

TRE: Training-Free Hallucination Detection for Diffusion Language Models

arXiv:2607. 22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem.

By Pengcheng Weng, Yanyu Qian, Yue Tan, Yixin Liu

llms diffusion safety

arXiv AI

Jul 28

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

arXiv:2607. 24586v1 Announce Type: cross Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model.

By Bianca Raimondi, Davide Evangelista, Maurizio Gabbrielli, Elena Loli Piccolomini

llms safety

arXiv Machine Learning

1d ago

MoRFI: Monotonic Sparse Autoencoder Feature Identification

arXiv:2604. 26866v2 Announce Type: replace-cross Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction.

By Dimitris Dimakopoulos, Shay B. Cohen, Ioannis Konstas

llms fine-tuning safety

arXiv Machine Learning

Jun 18

Decomposing Prediction Mechanisms for In-Context Recall

arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.

By Sultan Daniels, Dylan Davis, Dhruv Gautam, Wentinn Liao, Gireeja Ranade, Anant Sahai

llms efficiency

arXiv AI

Jul 23

CUSUM-Shaped Inference-Time Monitoring and Targeted Re-Decoding for Quantized Small Language Model Reasoning

arXiv:2607. 20129v1 Announce Type: new Abstract: Quantized small autoregressive reasoning models can enter long, repetitive, or unproductive trajectories, yet inference-time compute is usually allocated without observing how a trajectory develops.

By El Hassane Ettifouri, Ayoub Belfatmi, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane

llms efficiency

Related stories

DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models

TRE: Training-Free Hallucination Detection for Diffusion Language Models

D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models

MoRFI: Monotonic Sparse Autoencoder Feature Identification

Decomposing Prediction Mechanisms for In-Context Recall

CUSUM-Shaped Inference-Time Monitoring and Targeted Re-Decoding for Quantized Small Language Model Reasoning