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ARXIV:2605.30911 · LVLM HALLUCINATION · SUBMITTED 01 JUN · 20:23 UTC · FRESHNESS STALE
ARXIV:2605.30911LVLM HALLUCINATIONSUBMITTED 01 JUN · 20:23 UTCFRESHNESS STALEYusheng He · Jizhe Zhou · Xia Du · Zheng Lin · Jun Luo · Jiancheng Lv · arXiv
CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models.
Opportunity summary
Pain CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models.
Evidence 0 refs | 3 sources | 50% coverage
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CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models. But what makes an LVLM hallucinate less?
Hallucination remains one of the key challenges undermining the reliability of Large Vision-Language Models (LVLMs). But what makes an LVLM hallucinate less?
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments across 7 design aspects show that: 1) the widely emphasized scaling of model parameters has only limited impact on reducing all three types…
LVLM Hallucination moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models.
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10.48550/arXiv.2605.30911CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models.
Abstract
Hallucination remains one of the key challenges undermining the reliability of Large Vision-Language Models (LVLMs). But what makes an LVLM hallucinate less? Many existing efforts focus on improving internal components of the model. We argue that hallucination fundamentally stems from how the model architecture is designed. To investigate this, we factor the architecture design into three dimensions: Linguistic Foundation (LF), Visual Representation (VR), and Semantic Alignment (SA), and categorize hallucinations into Co-occurrence, Similarity, and previously overlooked Uncertainty types. Building on this formulation, we propose CoSimUE, a benchmark that creates fine-grained hallucination scenarios through controlled textual perturbations and random perturbations, enabling mapping between design choices and hallucination behaviors. Experiments across 7 design aspects show that: 1) the widely emphasized scaling of model parameters has only limited impact on reducing all three types of hallucinations; 2) larger and better-trained language foundations can reduce co-occurrence hallucinations; 3) stronger visual encoders and higher resolutions mitigate similarity errors; 4) effective alignment strategies alleviate uncertainty hallucinations. 5) Furthermore, cross-dimensional analysis reveals that jointly enhancing visual fidelity and alignment quality yields the most comprehensive improvements. This study provides the first systematic exploration linking architecture-level design to hallucination robustness, offering practical guidance for developing reliable and efficient LVLMs.
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PROBLEM
CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models. But what makes an LVLM hallucinate less?
METHOD
Hallucination remains one of the key challenges undermining the reliability of Large Vision-Language Models (LVLMs). But what makes an LVLM hallucinate less?
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments across 7 design aspects show that: 1) the widely emphasized scaling of model parameters has only limited impact on reducing all three types of hallucinations; 2) larger and better-trained lang...
WHY NOW
LVLM Hallucination moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 11, "author": "Yusheng He; Jizhe Zhou; Xia Du; Zheng Lin; Jun Luo; Jiancheng Lv"
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CoSimUE is a benchmark and framework that links LVLM architectural design choices to specific hallucination types, providing guidance for building more reliable models.
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LVLM Hallucination
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