MetaX Weekly AI Paper Review Week 34 2025 -- From Science-Specialized Models and Self-Supervised Vision to Memory-Based Reasoning, Innovative Development in Multi-Agent Systems, Memory Organized Search, and 3D Object Reconstruction: Intern-S1 A Scientific Multimodal Foundation Model (arxiv 2508.15763): Intern-S1 is a multimodal foundation model specialized in the scientific domain, adopting an MoE structure with 28B active parameters and 241B total parameters. Pre-trained on 5T tokens including 2.5T scientific domain tokens and then reinforcement learned on 1,000+ tasks, it created training synergy effects on diverse tasks through Mixture-of-Rewards technique. This model shows competitive performance among open-source models on general reasoning tasks, and particularly surpasses closed-source SOTA models in the science domain for tasks like molecular synthesis planning and reaction condition prediction. Additional paper reviews covered: multi-agent coordination systems for complex scientific research tasks; memory-organized retrieval for improving LLM performance on knowledge-intensive tasks; 3D object reconstruction from multi-view images; and self-supervised vision learning methods that learn rich representations without labeled data.