The field of large language models (LLMs) is moving towards a deeper understanding of the underlying mechanisms that cause hallucinations, which are plausible but factually incorrect outputs. Researchers are investigating the neural mechanisms that contribute to hallucinations, such as the identification and impact of hallucination-associated neurons. Additionally, novel frameworks are being developed to interpret the internal thinking process of LLMs, including the use of latent debate to capture hidden supporting and attacking signals within a single model. These advances are leading to the development of more effective methods for mitigating hallucinations, including introspection and cross-modal multi-agent collaboration. Noteworthy papers include: H-Neurons, which demonstrates that a sparse subset of neurons can predict hallucination occurrences and are causally linked to over-compliance behaviors. Latent Debate, which introduces a framework for interpreting model predictions through implicit internal arguments and provides a strong baseline for hallucination detection. InEx, which proposes a training-free, multi-agent framework that autonomously mitigates hallucination through internal introspective reasoning and external cross-modal multi-agent collaboration.