As artificial intelligence systems evolve, today's developments showcase both breakthroughs and limitations in making AI more human-like. From self-correcting AI agents that can learn from their errors to specialized language models finding the right balance of expertise, researchers are pushing boundaries while grappling with fundamental challenges in machine learning. Meanwhile, a new benchmark for video understanding reveals just how far AI still needs to go to match human expert-level reasoning across diverse fields like healthcare and engineering. Links to all the papers we discussed: Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training, Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models, MMVU: Measuring Expert-Level Multi-Discipline Video Understanding, TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space, UI-TARS: Pioneering Automated GUI Interaction with Native Agents, InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model