The field of artificial intelligence is witnessing significant advancements in multimodal reasoning and logical inference, driven by the development of innovative frameworks and architectures. Researchers are focusing on creating more robust and reliable models that can handle complex scenarios, ambiguous contexts, and conflicting stances. The use of large language models, multimodal agents, and logical reasoning techniques is becoming increasingly prevalent in various applications, including clinical decision support, procedural activity understanding, and high-assurance reasoning. Noteworthy papers in this area include MedLA, which proposes a logic-driven multi-agent framework for complex medical reasoning, and LOGicalThought, which introduces a neurosymbolically-grounded architecture for high-assurance reasoning. MedMMV is also notable for its controllable multimodal multi-agent framework, which demonstrates superior reliability and accuracy in medical benchmarks. Overall, these developments are pushing the boundaries of AI capabilities and paving the way for more trustworthy and effective systems.