The paper "Agentic Reasoning for Large Language Models" provides a comprehensive roadmap for reframing Large Language Models (LLMs) as autonomous agents capable of planning, acting, and learning through continual interaction with their environments. This transition marks a shift from static sequence prediction to dynamic, goal-oriented decision-making.

The survey organizes agentic reasoning along three complementary layers:

• Foundational Agentic Reasoning: Establishes core single-agent capabilities, specifically planning, tool use, and search.
• Self-Evolving Agentic Reasoning: Examines how agents refine their internal states and policies through feedback, memory, and iterative adaptation over time.
• Collective Multi-Agent Reasoning: Focuses on collaborative scenarios where multiple specialized agents coordinate roles and share knowledge to solve complex tasks.

The authors further distinguish between two primary optimization modes: in-context reasoning, which scales test-time compute through structured orchestration without parameter updates, and post-training reasoning, which uses reinforcement learning and fine-tuning to internalize reasoning strategies into the model's weights.

The paper contextualizes these mechanisms across diverse real-world applications, including science, robotics, healthcare, autonomous research, and mathematical exploration. Finally, it identifies critical future frontiers, such as user personalization, long-horizon credit assignment, world modeling, and the governance of autonomous agentic systems.