The field of autonomous task planning and code translation is witnessing significant developments, driven by the integration of Large Language Models (LLMs) and innovative architectural designs. Researchers are exploring flexible self-reflection mechanisms, hierarchical reflection architectures, and synthetic data generation to enhance the performance and adaptability of LLMs in complex tasks. Notable advancements include the improvement of overall performance and self-reflection flexibility in long-horizon robotic tasks, as well as the enhancement of code translation capabilities through automated pipeline optimization and in-house fine-tuning of open-source LLMs. Some noteworthy papers in this area include: FCRF, which proposes a novel Mentor-Actor architecture for flexible self-reflection in LLMs. ACT, which presents an innovative framework for improving code translation capabilities through synthetic data generation and adaptive training. ExpTeach, which grounds VLMs to physical robots through self-generated memory and reflection. MobileUse, which introduces a hierarchical reflection architecture for robust and adaptive mobile task execution.