Hallucinations

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Period ending 2026-09-21

13 new papers

A weekly snapshot of new work published in Hallucinations.

Period ending 2026-09-14

10 new papers

A weekly snapshot of new work published in Hallucinations.

Period ending 2026-09-07

11 new papers

A weekly snapshot of new work published in Hallucinations.

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Hallucinations

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275 papersExplore

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Large Language Model Hallucination

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125 papersExplore

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Hallucination Detection

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70 papersExplore

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Hallucination Mitigation

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62 papersExplore

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Hallucination Rate

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46 papersExplore

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Illusions

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44 papersExplore

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Robust Hallucination Detection

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23 papersExplore

438 papers

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MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
Mar 4, 2026Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo +2Magnetic Resonance Imaging Reconstruction Multimodal Pretraining
Probing for Knowledge Attribution in Large Language Models
Feb 26, 2026Ivo Brink, Alexander Boer, Dennis UlmerVisual Attribution Hallucinations
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
Feb 19, 2026Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj +1Large Language Model Uncertainty Large Language Model Output
WavePhaseNet: A DFT-Based Method for Constructing Semantic Conceptual Hierarchy Structures (SCHS)
Feb 16, 2026Kiyotaka Kasubuchi, Kazuo FukiyaSemantic Representations Hallucinations
HALT: Hallucination Assessment via Log-probs as Time series
Feb 2, 2026Ahmad Shapiro, Karan Taneja, Ashok GoelHallucination Detection Hallucination Rate
Relational Linearity is a Predictor of Hallucinations
Jan 16, 2026Yuetian Lu, Yihong Liu, Sebastian Gerstner +3Hallucinations Relational
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Jan 5, 2026Amirali Ebrahimzadeh, Seyyed M. SaliliHallucinations
Graded strength of comparative illusions is explained by Bayesian inference
Nov 18, 2025Yuhan Zhang, Erxiao Wang, Cory ShainIllusions Bayesian
Silence is Golden: Mitigating Hallucinations in Large Audio-Language Models via Layer-Weighted Vector Steering
Oct 14, 2025Tsung-En Lin, Kuan-Yi Lee, Hung-Yi LeeLarge Audio Language Models Hallucinations
Psychological Imagination Networks Show Cross-Population Centrality and Clustering Alignment in Humans That Large Language Models Fail to Replicate
Oct 5, 2025Saurabh Ranjan, Brian OdegaardImagination Illusions
SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision
Aug 5, 2025Zhaoxu Li, Chenqi Kong, Yi Yu +6Large Language Model Hallucination Hallucination Mitigation
Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Jul 21, 2025Siyuan Liu, Wenjing Liu, Zhiwei Xu +3Llm-Based Agent Hallucination Rate
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
May 18, 2025Kai Tang, Jinhao You, Yichen Guo +8Large Language Model Hallucination Hallucination Mitigation
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2Large Language Model Hallucination Large Language Model Responses
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Feb 20, 2025Yihao Xue, Kristjan Greenewald, Youssef Mroueh +1Robust Hallucination Detection Hallucination Detection
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
May 3, 2024Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5Citations Large Language Model Reliability
When Context Misleads: Surprisal, Energy and Attention Entropy as Metrics of Coherence Illusions in LLMs
Date pendingEce Takmaz, Nitin Kumar, Li Kloostra +1Narrative Coherence Surprisal
The C-index illusion: discrimination without calibration in published survival models
Date pendingRafael da Silva, Danilo AlvaresSurvival Predictions Survival Analysis

Mar 4, 2026 · cs.CVJ/K move · Enter open · S save

MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction

Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo, Matteo Figini+1

University College London, UK · Centre for AI, DS&AI, AstraZeneca, UK

Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness. In many clinical workflows, complementary MRI acquisitions (e.g. high-quality structural scans) are routinely available, yet existing reconstruction methods lack mechanisms to leverage this additional information. We propose MPFlow, a zero-shot multi-modal reconstruction framework built on rectified flow that incorporates auxiliary MRI modalities at inference time without retraining the generative prior to improve anatomical fidelity. Cross-modal guidance is enabled by our proposed self-supervised pretraining strategy, Patch-level Multi-modal MR Image Pretraining (PAMRI), which learns shared representations across modalities. Sampling is jointly guided by data consistency and cross-modal feature alignment using pre-trained PAMRI, systematically suppressing intrinsic and extrinsic hallucinations. Extensive experiments on HCP and BraTS show that MPFlow matches diffusion baselines on image quality using only 20% of sampling steps while reducing tumor hallucinations by more than 15% (segmentation dice score). This demonstrates that cross-modal guidance enables more reliable and efficient zero-shot MRI reconstruction.

Magnetic Resonance Imaging Reconstruction Multimodal Pretraining Medical Image Generation Cross-Modal Learning Hallucinations

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