DynHD is a method for detecting hallucinations in diffusion large language models (D‑LLMs) by focusing on token‑level uncertainty and its evolution during the denoising process. It introduces a semantic‑aware evidence construction module that filters out non‑informative structural tokens and highlights uncertainty in informative tokens, and a reference evidence generator that models the expected trajectory of uncertainty, enabling a deviation‑based detector to identify hallucinations. Experiments show DynHD outperforms existing baselines while being more efficient across various benchmarks and backbone models.
By Yanyu Qian, Yue Tan, Yixin Liu, Wang Yu, Shirui Pan
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
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applications.
The paper proposes a hidden‑state probing method for detecting hallucinations at the span level in large language model outputs, moving beyond token‑wise binary classification. By examining layer‑wise activation patterns, the approach identifies the exact onset and continuation tokens of hallucinations, achieving higher precision‑recall AUC than random baselines despite class imbalance. Additionally, the authors introduce a cross‑model detection framework where one model observes another’s internal representations, showing that an external observer can match or surpass the generator’s own self‑detection of hallucination onsets, even when the observer is smaller.
By Kingshuk Gupta, Davide Buscaldi
arXiv:2606. 24790v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations.
By Anand Kamat, Daniel Blake, Brent M. Werness
arXiv:2606. 00819v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved strong performance across diverse natural language tasks, yet their outputs often suffer from hallucinations -- content that is misaligned with factual information.
By Hanze Li, Jinhao You, Yichen Guo, Kai Tang, Shuangyang Xie, Xiande Huang
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
The paper introduces a low-cost method for detecting hallucinations in large language models by treating the model as a black-box dynamical system. It projects responses into a high-dimensional manifold, models the latent state-space dynamics with Koopman operator theory, and uses differential residual scores from transition operators to distinguish factual from hallucinated outputs. The approach requires only a single-sample pass and shows state-of-the-art performance across three benchmarks with reduced resource overhead.
By Dan Wilson, Mohamed Akrout
The paper introduces a token‑level hallucination detector that treats hallucinations as temporally extended spans and uses sequence labeling. It fuses 33‑dimensional features from text statistics, NLI entailment, and language‑model surprisal, and applies a BiGRU to achieve an AUC of 0.840 on RAGTruth, outperforming a logistic‑regression baseline by 11 points. The study shows that temporal ordering of features, rather than model capacity, drives most of the performance gain, and the detector remains effective on unseen language models with less than 4% AUC loss.
By Igor Itkin
arXiv:2607. 10476v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions.
By Basel Abdeen, S M Tahmid Siddiqui, Meah Tahmeed Ahmed, Anoop Singhal, Latifur Khan, Punya Parag Modi, Ehab Al-Shaer
The paper introduces Dynamic Alignment Compensation (DAC), a training‑free inference‑time technique designed to reduce hallucinations in Large Vision‑Language Models (LVLMs). DAC monitors cross‑modal representation drift across decoder layers and generation steps, applying lightweight residual compensation through Layer‑wise Semantic Compensation and Sequential Semantic Correction. Experiments on nine multimodal benchmarks across various LVLM backbones demonstrate that DAC consistently lowers hallucination rates while preserving overall performance.
By Kairong Yu, Zixin Zhu, Le Yu, Hongwei Wang
arXiv:2604. 10697v2 Announce Type: replace-cross Abstract: Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context.
By Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz