The recent developments in the field of AI and machine learning, particularly in the application of Large Language Models (LLMs) to video procedure planning, long-term action anticipation, and embodied instruction following, indicate a significant shift towards more robust, generalizable, and context-aware systems. Researchers are increasingly focusing on enhancing the capabilities of LLMs to handle complex tasks that require a deep understanding of temporal dynamics, semantic actions, and the ability to reason about actions in a systematic manner. Innovations include the integration of LLMs with action languages for complex reasoning, the development of frameworks that leverage LLMs for open vocabulary procedure planning, and the creation of models that ensure temporal context coherence in long-term action anticipation. These advancements are not only improving the performance of AI systems on benchmark datasets but are also paving the way for more sophisticated applications in embodied AI.

Noteworthy papers include:

• PlanLLM: Introduces a cross-modal joint learning framework with LLMs for video procedure planning, achieving superior performance on benchmarks by enhancing action step decoding and employing mutual information maximization.
• Temporal Context Consistency Above All: Proposes a method for long-term action anticipation that ensures temporal context coherence and models action transitions, validated on multiple benchmark datasets.
• Hindsight Planner: Develops a closed-loop few-shot planner for embodied instruction following, demonstrating competitive performance under a few-shot assumption.
• Multimodal Large Models Are Effective Action Anticipators: Introduces the ActionLLM framework, leveraging LLMs for long-term action anticipation and demonstrating the superiority of the approach on benchmark datasets.
• LLM+AL: Bridges LLMs with action languages for complex reasoning about actions, showing consistent improvement in accuracy with minimal human corrections.