In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

Recent advancements in AI focus on motion synthesis and protein modeling. The In-Context Model Predictive Generation (ICMPG) framework integrates language-model planning with inference to bridge the gap between semantic fidelity and physical realism in human motion synthesis. Simultaneously, new AI models are being utilized to generate complete models of proteins in motion, specifically targeting intricately folded cell membrane proteins to enhance drug and antibody discovery pathways by simulating how drug candidates bind to these proteins.