Agentic Reasoning for Large Language Models

Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, exemplified by standard benchmarks in mathematics and code, they struggle in open-ended and dynamic environments. The emergence of agentic reasoning marks a paradigm shift, bridging thought and action by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we provide a systematic roadmap by organizing agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning establishes core single-agent capabilities, including planning, tool use, and search, that operate in stable environments; self-evolving agentic reasoning examines how agents refine these capabilities through feedback, memory, and adaptation in evolving settings; and collective multi-agent reasoning extends intelligence to collaborative scenarios where multiple agents coordinate roles, share knowledge, and pursue shared goals. Across all layers, we analyze system constraints and optimization settings by distinguishing in-context reasoning, which scales test-time interaction through structured orchestration and adaptive workflow design, from post-training reasoning, which optimizes behaviors through reinforcement learning and supervised fine-tuning. We further review and contextualize agentic reasoning frameworks in real-world applications and benchmarks spanning science, robotics, healthcare, autonomous research, and math, illustrating how different reasoning mechanisms are instantiated and evaluated across domains. This survey synthesizes agentic reasoning methods into a unified roadmap that bridges thoughts and actions, offering actionable guidance for agentic systems across environmental dynamics, optimization settings, and agent interaction settings. Finally, we outline open challenges and future directions, situating how agentic reasoning has developed while identifying what remains ahead: personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance frameworks for real-world deployment.