MetaX Weekly AI Paper Review Week 35 2025 -- Performance Innovation Based on Reinforcement Learning and Self-Reward Maximizing Reasoning Capability and Efficiency, Application Expansion Beyond Agents and Robotics, Leap to Solving Difficult Problems in Specialized Scientific Fields: InternVL3.5 Advancing Open-Source Multimodal Models in Versatility Reasoning and Efficiency (arxiv 2508.18265): InternVL 3.5 is a next-generation open-source multimodal model that greatly improved versatility, reasoning capability, and efficiency. Through innovative technologies including Cascade RL for reinforcement learning to enhance reasoning and Visual Resolution Router (ViR) for efficiency, it dramatically improved reasoning performance and speed compared to the previous version. By supporting new functions like GUI interaction, it successfully closed the performance gap with top commercial models. Additional paper reviews covered: self-reward learning methods where models generate their own training signal for continued improvement; advances in mathematical reasoning through structured problem decomposition; robotic manipulation policies combining language instruction following with physical control; and scientific domain applications where AI models are being applied to drug discovery, materials science, and climate modeling challenges requiring integration of domain expertise with general reasoning capability.