Researchers proposed a multi-judge committee approach to detect hallucinated spans in vision-language model outputs. They fine-tuned multiple models and combined their predictions through majority voting and activation probes, achieving top results in a shared task. This work may inform the development of more accurate and robust AI agents in vision-language tasks.