MetaX Weekly AI Paper Review Week 29 2025 -- Innovation in Next-Generation AI Architecture Maximizing Reasoning and Efficiency, New Horizons of Reliable Intelligence Interacting with Reality Beyond Memorization: Test-Time Scaling with Reflective Generative Model (MetaStone-S1): A generative model integrating policy model and reward model into one is proposed, improving inference efficiency and flexibly changing performance by adjusting computation at test time. A Survey of Context Engineering for Large Language Models: Systematically organizes the context engineering field for improving LLM performance and presents the core research challenge that models are weak in generating capability compared to their ability to understand complex context. Reasoning or Memorization Unreliable Results of Reinforcement Learning Due to Data Contamination: Through a self-made benchmark without data contamination, it verifies that many LLM reasoning improvements attributed to reinforcement learning are actually memorization of training data patterns. Additional papers covered: advances in long-context processing; multimodal reasoning combining vision and language; agent systems for complex multi-step task completion; and evaluation methodology improvements for more reliable AI capability assessment.