What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness
arXiv:2605.30911v1 Announce Type: cross 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,
The paper directly addresses fundamental architectural factors contributing to LVLM hallucination, a critical and current bottleneck in AI reliability.
Understanding the architectural roots of hallucination is crucial for designing more reliable and trustworthy large vision-language models, accelerating their practical adoption.
The focus for improving LVLMs may shift from incremental component fixes to more fundamental architectural redesigns based on identified factors.
- · AI developers
- · LVLM users
- · Makers of reliable AI applications
- · Models prone to hallucination
- · Users distrusting of current AI systems
- · Approaches focusing only on post-hoc hallucination mitigation
Architectural theories on hallucination reduction will guide future LVLM development.
Improved LVLM reliability will broaden their applications across sensitive domains.
Increased trust in LVLMs could accelerate the integration of AI agents into complex decision-making systems.
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Read at arXiv cs.AI