arXiv:2606.27596v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination. Deviating from the prevailing attention intensity assumption, we reveal a deeper dynamic structural misalignment: hallucination is triggered at decision-critical steps where specific attention heads, acting as risky mediators, decouple from visual evidence to lock onto language priors. This establishes a pathological shortcut that bypasses visual grounding. To dismantle this, we propose Fox (Faithfulness and Observational-flow vi
The proliferation of advanced LVLMs highlights the urgency of addressing core reliability issues like hallucination, where current solutions are insufficient.
This research provides a causal framework for understanding and mitigating LVLM hallucination, moving beyond superficial fixes to improve model faithfulness and trustworthiness.
The focus shifts from general attention analysis to identifying and dismantling specific pathological shortcuts within LVLM decision-making, offering a new avenue for stable and reliable AI.
- · AI developers
- · LVLM users
- · AI safety researchers
- · Enterprises deploying AI
- · Developers relying solely on superficial fixes
- · Users experiencing frequent AI hallucinations
Improved reliability and reduced hallucination in large vision-language models, enhancing their practical applicability.
Increased trust in AI systems could accelerate the adoption of LVLMs in critical applications and industries.
More robust and faithful AI could lead to a re-evaluation of current AI safety paradigms, focusing on interpretability and causal intervention.
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Read at arXiv cs.AI