The field of AI-driven scientific discovery is witnessing a significant shift towards explainability and transparency. Researchers are increasingly focusing on developing techniques that can provide insights into the decision-making processes of AI models, making them more trustworthy and reliable. One of the key directions is the integration of Large Language Models (LLMs) with domain-specific knowledge to enable more accurate and interpretable predictions. Another area of research is the development of frameworks that can facilitate collaboration between AI and human scientists, ensuring that AI-generated outputs are rigorous, logical, and consistent with established theoretical models. Noteworthy papers in this regard include MoRE-LLM, which proposes a novel approach to combining data-driven models with knowledge extracted from LLMs, and AI-Newton, which presents a concept-driven physical law discovery system capable of autonomously deriving physical laws from raw data. Additionally, papers like Advancing AI-Scientist Understanding and Do Two AI Scientists Agree? highlight the importance of ensuring the reliability and interpretability of AI outputs in scientific discovery.