Visual hallucination detection in large vision-language models via evidential conflict | Paper Citation Record

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This paper cites https://lmsys.org/blog/2023-03-30-vicuna.

Visual hallucination detection in large vision-language models via evidential conflict https://lmsys.org/blog/2023-03-30-vicuna

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This paper cites International Journal of Man-Machine Studies, 30(5):525–536, 1989.

Visual hallucination detection in large vision-language models via evidential conflict International Journal of Man-Machine Studies, 30(5):525–536, 1989

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Visual hallucination detection in large vision-language models via evidential conflict Unresolved cited work

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This paper cites GPT-4 Technical Report.

Visual hallucination detection in large vision-language models via evidential conflict GPT-4 Technical Report

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This paper cites Continual evidential deep learning for out-of-distribution de- tection.

Visual hallucination detection in large vision-language models via evidential conflict Continual evidential deep learning for out-of-distribution de- tection

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This paper cites Flamingo: avisuallanguagemodelforfew-shot learning.Advances in neural information processing systems, 35:23716– 23736, 2022.

Visual hallucination detection in large vision-language models via evidential conflict Flamingo: avisuallanguagemodelforfew-shot learning.Advances in neural information processing systems, 35:23716– 23736, 2022

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This paper cites Vqa: Visual ques- tion answering.

Visual hallucination detection in large vision-language models via evidential conflict Vqa: Visual ques- tion answering

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This paper cites Evidential classification for defending against adver- sarial attacks on network traffic.Information Fusion, 92:115–126, 2023.

Visual hallucination detection in large vision-language models via evidential conflict Evidential classification for defending against adver- sarial attacks on network traffic.Information Fusion, 92:115–126, 2023

Reference 8

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This paper cites Let there be a clock on the beach: Reducing object hallucination in image captioning.

Visual hallucination detection in large vision-language models via evidential conflict Let there be a clock on the beach: Reducing object hallucination in image captioning

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Observation 7d73047a-2407-451c-90aa-24f8ac6084f4 · outbound

This paper cites Learning horn envelopes via queries from language models.International Journal of Approximate Reasoning, 171:109026, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Learning horn envelopes via queries from language models.International Journal of Approximate Reasoning, 171:109026, 2024

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This paper cites Language models are few-shot learners.

Visual hallucination detection in large vision-language models via evidential conflict Language models are few-shot learners

Reference 11

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This paper cites A survey on evaluation of large language models.ACM Transactions on Intelligent Systems and Technology, 15(3):1–45, 2024.

Visual hallucination detection in large vision-language models via evidential conflict A survey on evaluation of large language models.ACM Transactions on Intelligent Systems and Technology, 15(3):1–45, 2024

Reference 12

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This paper cites Inside: Llms’ internal states retain the power of hallucination detection.

Visual hallucination detection in large vision-language models via evidential conflict Inside: Llms’ internal states retain the power of hallucination detection

Reference 13

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Observation d8b77850-242f-4d13-bd99-895dc71a32c5 · outbound

This paper cites In- structBLIP: Towards General-purpose Vision-Language Models with In- struction Tuning, June 2023.

Visual hallucination detection in large vision-language models via evidential conflict In- structBLIP: Towards General-purpose Vision-Language Models with In- struction Tuning, June 2023

Reference 14

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Observation 97bada2a-f823-4c89-a30f-124ec9130719 · outbound

This paper cites Upper and lower probability inferences based on a sample from a finite univariate population.Biometrika, 54(3-4):515–528, 1967.

Visual hallucination detection in large vision-language models via evidential conflict Upper and lower probability inferences based on a sample from a finite univariate population.Biometrika, 54(3-4):515–528, 1967

Reference 15

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Observation 5771c716-9ed9-4e1e-a968-fd3cd09a0502 · outbound

This paper cites Generalized evidence theory.Applied Intelligence, 43(3):530–543, 2015.

Visual hallucination detection in large vision-language models via evidential conflict Generalized evidence theory.Applied Intelligence, 43(3):530–543, 2015

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This paper cites A neural network classifier based on dempster-shafer theory.IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(2):131–150, 2000.

Visual hallucination detection in large vision-language models via evidential conflict A neural network classifier based on dempster-shafer theory.IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(2):131–150, 2000

Reference 17

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This paper cites Decision-making with belief functions: A review.

Visual hallucination detection in large vision-language models via evidential conflict Decision-making with belief functions: A review

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Observation d2bdbcb0-5b0a-469c-ae02-458c9d1622ab · outbound

This paper cites Logistic regression, neural networks and dempster– shafer theory: A new perspective.Knowledge-Based Systems, 176:54–67, 2019.

Visual hallucination detection in large vision-language models via evidential conflict Logistic regression, neural networks and dempster– shafer theory: A new perspective.Knowledge-Based Systems, 176:54–67, 2019

Reference 19

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Observation 2515dd79-9ed1-4776-8d4d-1a7dc890446d · outbound

This paper cites Bert: Pre-trainingofdeepbidirectionaltransformersforlanguageunder- standing.

Visual hallucination detection in large vision-language models via evidential conflict Bert: Pre-trainingofdeepbidirectionaltransformersforlanguageunder- standing

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Observation d4872b9c-5a3d-4087-81df-e961f8159cf8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, June 2021.

Visual hallucination detection in large vision-language models via evidential conflict An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, June 2021

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Observation 889b5c5d-a70e-43d7-9488-017d798cde1a · outbound

This paper cites A Survey of Vision-Language Pre-Trained Models, July 2022.

Visual hallucination detection in large vision-language models via evidential conflict A Survey of Vision-Language Pre-Trained Models, July 2022

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This paper cites Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models.

Visual hallucination detection in large vision-language models via evidential conflict Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models

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This paper cites De- tecting hallucinations in large language models using semantic entropy.

Visual hallucination detection in large vision-language models via evidential conflict De- tecting hallucinations in large language models using semantic entropy

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Observation 86d8440e-ff17-4e36-878e-8255416207fa · outbound

This paper cites Make me a bnn: A simple strategy for estimating bayesian uncertainty from pre-trained models.

Visual hallucination detection in large vision-language models via evidential conflict Make me a bnn: A simple strategy for estimating bayesian uncertainty from pre-trained models

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Observation 11c8d4f9-ca36-4bf9-a54a-a668c7925fe7 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Dropout as a bayesian approxima- tion: Representing model uncertainty in deep learning

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Observation 6ce163a6-eed5-49d2-92ab-0075804fe566 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Visual hallucination detection in large vision-language models via evidential conflict LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

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Visual hallucination detection in large vision-language models via evidential conflict Hallusionbench: an advanced diagnostic suite for entangled lan- guage hallucination and visual illusion in large vision-language models

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Observation 6c41a3e6-547d-4e3f-bc36-59011d8e80e7 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Looking for a needle in a haystack: A comprehensive study of hallucinations in neural machine translation

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Visual hallucination detection in large vision-language models via evidential conflict On calibra- tion of modern neural networks

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Visual hallucination detection in large vision-language models via evidential conflict DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Observation 2e9d03e0-a804-42dd-8da5-897dc9251aa8 · outbound

This paper cites Divert more attention to vision-language object tracking.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Divert more attention to vision-language object tracking.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2024

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Observation 64c1870e-a0d0-4d26-8426-f2dc7f9eabf1 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Ciem: Con- trastive instruction evaluation method for better instruction tuning

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Visual hallucination detection in large vision-language models via evidential conflict Automated trading systems statistical and machine learning methods and hardware implementation: a survey.Enterprise Information Sys- tems, 13(1):132–144, 2019

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Observation 266f32e7-4178-492f-94f6-9fb1c74510c0 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development.Advances in neural information processing systems, 2021

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This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Visual hallucination detection in large vision-language models via evidential conflict A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

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Visual hallucination detection in large vision-language models via evidential conflict Lymphoma segmentation from 3D PET-CT images using a deep evidential network

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Observation 1dd1a4d4-1fcd-45ee-ab45-d97db385fc7f · outbound

This paper cites Surveyof hallucination in natural language generation.ACM Computing Surveys, 55(12):1–38, 2023.

Visual hallucination detection in large vision-language models via evidential conflict Surveyof hallucination in natural language generation.ACM Computing Surveys, 55(12):1–38, 2023

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Observation d012d6db-602e-4126-9d3e-a2dba92bf33b · outbound

This paper cites Language models (mostly) know what they know.CoRR, 2022.

Visual hallucination detection in large vision-language models via evidential conflict Language models (mostly) know what they know.CoRR, 2022

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Observation b3cc82ce-5fe8-46ee-9033-dd50881bc8dc · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Calibrated language mod- elsmusthallucinate

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Observation 1238f025-4e9d-4a11-8f7e-e21743d8d1a5 · outbound

This paper cites Scaling Laws for Neural Language Models.

Visual hallucination detection in large vision-language models via evidential conflict Scaling Laws for Neural Language Models

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Observation 0b8daf31-def1-479f-b0aa-f7965252f2b3 · outbound

This paper cites Large Language Models Must Be Taught to Know What They Don't Know.

Visual hallucination detection in large vision-language models via evidential conflict Large Language Models Must Be Taught to Know What They Don't Know

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Observation 968cb6e9-22cf-4f28-bbd7-a94d254df1d8 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Deep visual-semantic alignments for generating image descriptions

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Observation 44a9c677-cb19-43e7-9a2b-5b29bb31a4fe · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Semantic uncer- tainty: Linguistic invariances for uncertainty estimation in natural lan- guagegeneration

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Observation 101cddb4-acfb-42df-b4da-cc8ca69da86d · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep en- sembles.Advances in neural information processing systems, 30, 2017.

Visual hallucination detection in large vision-language models via evidential conflict Simple and scalable predictive uncertainty estimation using deep en- sembles.Advances in neural information processing systems, 30, 2017

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Observation a5589882-6300-4b59-ada5-e08c305f8c93 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict Evaluating object hallucination in large vision-language models

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This paper cites Teaching models to express their uncertainty in words.Transactions on Machine Learning Research, 2022.

Visual hallucination detection in large vision-language models via evidential conflict Teaching models to express their uncertainty in words.Transactions on Machine Learning Research, 2022

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Visual hallucination detection in large vision-language models via evidential conflict Microsoft coco: Common objects in context

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Observation dfceb3ab-a268-4973-acfb-16ec50cee845 · outbound

This paper cites Lawrence Zitnick.

Visual hallucination detection in large vision-language models via evidential conflict Lawrence Zitnick

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This paper cites Generating with confi- dence: Uncertainty quantification for black-box large language models.

Visual hallucination detection in large vision-language models via evidential conflict Generating with confi- dence: Uncertainty quantification for black-box large language models

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Observation c33e1944-73c7-43f1-8937-257c4a3e00f5 · outbound

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Visual hallucination detection in large vision-language models via evidential conflict A Survey on Hallucination in Large Vision-Language Models

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This paper cites Im- proved baselines with visual instruction tuning.

Visual hallucination detection in large vision-language models via evidential conflict Im- proved baselines with visual instruction tuning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.406363Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5442738-b71e-4c15-98e0-8386634c2e5e · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Visual instruction tuning.Advances in neural information processing systems, 36, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.760440Z

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Observation f99d276b-62b9-4201-beed-615d2b1bb35f · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? InEuro- pean conference on computer vision, pages 216–233.

Visual hallucination detection in large vision-language models via evidential conflict Mmbench: Is your multi-modal model an all-around player? InEuro- pean conference on computer vision, pages 216–233

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.387998Z

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Observation f82cda00-cf75-4b16-b870-7cb640564cda · outbound

This paper cites Object halluci- nation detection in large vision language models via evidential conflict.

Visual hallucination detection in large vision-language models via evidential conflict Object halluci- nation detection in large vision language models via evidential conflict

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.375238Z

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Observation cbbce128-1ef6-478c-9b40-6a405b0f490f · outbound

This paper cites Negative object presence evaluation (NOPE) to measure object hallucination in vision-language models.

Visual hallucination detection in large vision-language models via evidential conflict Negative object presence evaluation (NOPE) to measure object hallucination in vision-language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.362485Z

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Observation 2cc904c9-125c-42ae-9bd9-32e75f0fe203 · outbound

This paper cites AI Halluci- nations: A Misnomer Worth Clarifying.

Visual hallucination detection in large vision-language models via evidential conflict AI Halluci- nations: A Misnomer Worth Clarifying

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.350570Z

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Observation 840b16d0-3fa3-43db-8f89-b0efc3edda32 · outbound

This paper cites Uncertainty estimation in autoregressive structured prediction.

Visual hallucination detection in large vision-language models via evidential conflict Uncertainty estimation in autoregressive structured prediction

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.336156Z

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Observation f2620a4c-fdcf-4c44-84e9-3e1ce6718583 · outbound

This paper cites Selfcheckgpt: Zero- resource black-box hallucination detection for generative large language models.

Visual hallucination detection in large vision-language models via evidential conflict Selfcheckgpt: Zero- resource black-box hallucination detection for generative large language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.324732Z

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Observation 04f7bf2f-a343-42d4-afc6-89fc3b657dce · outbound

This paper cites On faithfulness and factuality in abstractive summarization.

Visual hallucination detection in large vision-language models via evidential conflict On faithfulness and factuality in abstractive summarization

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.312999Z

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Observation 509e43fa-6053-436d-ba8a-f31571beb61c · outbound

This paper cites Concise thoughts: Impact of output length on llm reasoning and cost.CoRR, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Concise thoughts: Impact of output length on llm reasoning and cost.CoRR, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.301950Z

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Observation cffb2c3d-e77e-4104-b2a6-ce68f419e993 · outbound

This paper cites Gpt-4 technical report, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Gpt-4 technical report, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.794016Z

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Observation 59b50ab5-0e9b-4792-ae2e-bf8882d44e69 · outbound

This paper cites Dinov2: Learning robust visual fea- tures without supervision.Transactions on Machine Learning Research, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Dinov2: Learning robust visual fea- tures without supervision.Transactions on Machine Learning Research, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.283113Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a557bf0a-2133-44dd-933a-7d2da9a51185 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Visual hallucination detection in large vision-language models via evidential conflict Learning transferable visual models from natural language supervision

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.802033Z

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Observation 22464717-705d-4b45-b98b-f79ec3d36b0d · outbound

This paper cites Semantic Consistency for Assuring Reliability of Large Language Models.

Visual hallucination detection in large vision-language models via evidential conflict Semantic Consistency for Assuring Reliability of Large Language Models

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Resolution: verified exact; local_arxiv, observed 2026-08-06T23:11:54.973051Z

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Observation f0838fab-a27a-4516-88e6-389107080e33 · outbound

This paper cites The Troubling Emergence of Hallucination in Large Language 35 Models - An Extensive Definition, Quantification, and Prescriptive Re- mediations.

Visual hallucination detection in large vision-language models via evidential conflict The Troubling Emergence of Hallucination in Large Language 35 Models - An Extensive Definition, Quantification, and Prescriptive Re- mediations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.265538Z

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Observation 94b4c4fb-3aa2-47eb-ba62-238555650572 · outbound

This paper cites Object hallucination in image captioning.

Visual hallucination detection in large vision-language models via evidential conflict Object hallucination in image captioning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.253926Z

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Observation 9743512e-1077-44fa-9560-22dc9a28d62d · outbound

This paper cites Evidential deep learning to quantify classification uncertainty.Advances in neural infor- mation processing systems, 31, 2018.

Visual hallucination detection in large vision-language models via evidential conflict Evidential deep learning to quantify classification uncertainty.Advances in neural infor- mation processing systems, 31, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.240363Z

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Observation bc4b48ad-3c54-4afd-a693-df4673c491e9 · outbound

This paper cites Princeton university press, 1976.

Visual hallucination detection in large vision-language models via evidential conflict Princeton university press, 1976

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.227169Z

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Observation 265e380f-9c33-4511-a15e-746d46c79941 · outbound

This paper cites Talking about large language models.Communica- tions of the ACM, 67(2):68–79, 2024.

Visual hallucination detection in large vision-language models via evidential conflict Talking about large language models.Communica- tions of the ACM, 67(2):68–79, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.216276Z

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Observation 44ad2a08-3bde-483a-9ccc-e7c72ed57f8a · outbound

This paper cites Belief functions: the disjunctive rule of combination and the generalized bayesian theorem.International Journal of approximate reasoning, 9(1):1–35, 1993.

Visual hallucination detection in large vision-language models via evidential conflict Belief functions: the disjunctive rule of combination and the generalized bayesian theorem.International Journal of approximate reasoning, 9(1):1–35, 1993

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.205582Z

Source-reported events for the cited work

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Observation e9b21436-0ba9-4bd5-9354-2bbc44ed2e95 · outbound

This paper cites Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback.

Visual hallucination detection in large vision-language models via evidential conflict Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.194794Z

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Observation 5293d4e8-a6bf-4e8b-8c57-b7e9eb637629 · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

Visual hallucination detection in large vision-language models via evidential conflict Eyes wide shut? exploring the visual shortcomings of multimodal llms

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.840154Z

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Observation c8f34068-640a-476e-8f98-66688bd24500 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Visual hallucination detection in large vision-language models via evidential conflict LLaMA: Open and Efficient Foundation Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.843508Z

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Observation ea9c7499-72d5-49ea-bfd0-d104c8246f21 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Visual hallucination detection in large vision-language models via evidential conflict Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.848210Z

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Observation a52f9d9b-2110-4740-9717-e59b145bd796 · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

Visual hallucination detection in large vision-language models via evidential conflict Attention is all you need.Advances in Neural Information Processing Systems, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.852879Z

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Observation 1f329816-5018-4879-b85b-9847d25acc9d · outbound

This paper cites an unresolved cited work.

Visual hallucination detection in large vision-language models via evidential conflict Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:11:55.172147Z

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Observation 8124a305-04d0-4fcf-8592-8cdffc0a70b4 · outbound

This paper cites Self-consistency improveschainofthoughtreasoninginlanguagemodels.

Visual hallucination detection in large vision-language models via evidential conflict Self-consistency improveschainofthoughtreasoninginlanguagemodels

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.160551Z

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Observation 2e1bcce0-09b6-4f7b-bd01-092d399c4133 · outbound

This paper cites Evaluating and analyzing relationship halluci- nations in large vision-language models.

Visual hallucination detection in large vision-language models via evidential conflict Evaluating and analyzing relationship halluci- nations in large vision-language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.150208Z

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Observation dd4337fb-2875-4612-b0c5-cc3c3931fb2c · outbound

This paper cites On hallucination and predictive uncertainty in conditional language generation.

Visual hallucination detection in large vision-language models via evidential conflict On hallucination and predictive uncertainty in conditional language generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.139735Z

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Observation e26ae54b-0479-4cf5-bd30-3de16c81a5c3 · outbound

This paper cites Uncertainty quantifica- tion with pre-trained language models: A large-scale empirical analysis.

Visual hallucination detection in large vision-language models via evidential conflict Uncertainty quantifica- tion with pre-trained language models: A large-scale empirical analysis

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.126528Z

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Observation 75b82fcc-cd48-4435-9d73-352da57f0978 · outbound

This paper cites Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms.

Visual hallucination detection in large vision-language models via evidential conflict Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.112122Z

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Observation f3a67f7a-4bb9-43cd-ac46-36af3029e5a3 · outbound

This paper cites Deep evidential fusion network for medical image classification.International Journal of Approximate Reasoning, 150:188–198, 2022.

Visual hallucination detection in large vision-language models via evidential conflict Deep evidential fusion network for medical image classification.International Journal of Approximate Reasoning, 150:188–198, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.097927Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed19ce15-88c0-4f53-b8aa-b72b8f086db0 · outbound

This paper cites mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models.

Visual hallucination detection in large vision-language models via evidential conflict mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.884075Z

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Observation 872a62b5-d5ce-4dee-b376-a9796171ac8d · outbound

This paper cites mplug-owl2: Revolutionizing multi- modal large language model with modality collaboration.

Visual hallucination detection in large vision-language models via evidential conflict mplug-owl2: Revolutionizing multi- modal large language model with modality collaboration

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.085119Z

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Observation b8eaa694-c8a3-4d5f-965e-3700a78b5cdc · outbound

This paper cites R-tuning: Instructing large language models to say ‘i don’t know’.

Visual hallucination detection in large vision-language models via evidential conflict R-tuning: Instructing large language models to say ‘i don’t know’

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.074113Z

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Observation 32ef168f-9e66-443e-a033-13050537a271 · outbound

This paper cites A survey of controllable text generation using transformer-based pre- trained language models.ACM Computing Surveys, 56(3):1–37, 2023.

Visual hallucination detection in large vision-language models via evidential conflict A survey of controllable text generation using transformer-based pre- trained language models.ACM Computing Surveys, 56(3):1–37, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:11:55.061061Z

Source-reported events for the cited work

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Observation 9a6ff73f-0870-4909-a2ce-5654f00245e1 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Visual hallucination detection in large vision-language models via evidential conflict Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:54.899988Z

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