MetaX Weekly AI Paper Review Week 36 2025 -- AI Agents Evolving Through Reinforcement Learning Opening New Horizons of Autonomous Intelligence, Latest Research Trends for Solving Real Problems from Code Security to Robot Control and 3D Generation: The Landscape of Agentic Reinforcement Learning for LLMs A Survey (arxiv 2509.02547): This paper is a comprehensive survey on Agentic RL -- a new paradigm utilizing large language models. Unlike conventional reinforcement learning treating LLMs as passive text generators, Agentic RL presents a conceptual shift viewing LLMs as autonomous agents making decisions in complex environments. The paper proposes a systematic taxonomy based on agent core capabilities and application domains, arguing that reinforcement learning is the key mechanism translating these capabilities into actual agent behavior. Synthesizing 500+ latest studies, the survey maps the current state of agentic RL and identifies the key research challenges: credit assignment in long multi-step tasks, exploration in sparse reward environments, generalization to novel task variants, and safe exploration constraints. Additional paper reviews covered: code security vulnerability detection using AI; advances in robotic control through learned policies; 3D object generation from 2D image inputs; and multi-agent coordination methods for complex task completion requiring specialized agent cooperation.