The field of robotic motion planning and autonomous systems is witnessing significant advancements with the integration of Large Language Models (LLMs) and formal methods. Researchers are exploring innovative approaches to address the challenges of spatio-temporal couplings, hallucination, and spurious behavior detection in multi-robot systems. The use of Linear Temporal Logic (LTL) and simulation-based planning is becoming increasingly popular for defining semantic diversity criteria and generating semantically diverse plans. Furthermore, the development of frameworks inspired by philosophical systems and the compilation of programming languages to real-time operating system primitives are also contributing to the growth of this field. Notable papers include: T3 Planner, which introduces a self-correcting LLM framework for robotic motion planning with temporal logic. PathFormer, which presents a Transformer with 3D grid constraints for digital twin robot-arm trajectory generation, achieving high accuracy and success rates in controlled tests.