Recent developments across various research areas have converged on a common theme of enhancing model reliability, interpretability, and adaptability in complex, high-dimensional environments. In causal inference, advancements have been made in handling latent confounders and integrating deep learning techniques to model time-sensitive effects, while reinforcement learning has seen innovations in exploration strategies and off-policy evaluation. The integration of causal insights into service systems has led to more accurate predictive models for dynamic environments. Large language models have focused on mitigating hallucinations and improving factual accuracy through uncertainty quantification and external knowledge integration. Similarly, vision-language models have adopted uncertainty quantification and novel decoding methods to enhance reliability and reduce hallucinations. These trends collectively reflect a shift towards more sophisticated, trustworthy, and adaptive models that can effectively navigate and interpret complex data environments.