The field of artificial intelligence and blockchain is rapidly evolving, with a focus on improving the performance and efficiency of Large Language Models (LLMs) and blockchain simulations. Researchers are exploring innovative architectures, such as Mixture of Experts (MoE), and developing unified approaches to tool integration, which can significantly reduce development overhead and improve execution performance. In the area of blockchain, there is a growing need for standardized and optimized simulation parameters, leading to the development of generic frameworks for optimization in blockchain simulators. Additionally, cross-chain asset exchange is becoming increasingly important, with new protocols being proposed to achieve grief-free and bribery-safe atomic swaps. Noteworthy papers include: The paper on Unified Tool Integration for LLMs, which proposes a protocol-agnostic approach to function calling, reducing code overhead and improving performance. The paper on 4-Swap, which presents a novel cross-chain atomic swap protocol that is both grief-free and bribery-safe, completing asset exchange in just four transactions.