Agentic Reasoning for Large Language Models
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, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.
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Tianxin Wei
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2026-01-23T09:38:58.368Z
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Tianxin Wei
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Ting-Wei Li
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2026-01-22T17:12:21.531Z
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Ting-Wei Li
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Zhining Liu
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Xuying Ning
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2026-02-02T17:01:10.211Z
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Xuying Ning
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Ze Yang
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Jiaru Zou
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2026-01-23T09:38:56.256Z
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Jiaru Zou
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Zhichen Zeng
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Ruizhong Qiu
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Xiao Lin
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2026-01-29T21:17:15.806Z
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Xiao Lin
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Dongqi Fu
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Zihao Li
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Zihao Li
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Mengting Ai
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2026-01-22T08:45:10.378Z
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Mengting Ai
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Duo Zhou
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2026-01-22T08:45:12.476Z
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Duo Zhou
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Wenxuan Bao
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Yunzhe Li
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2026-01-22T08:45:14.383Z
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Yunzhe Li
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Gaotang Li
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Cheng Qian
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Yu Wang
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Xiangru Tang
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Yin Xiao
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Liri Fang
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Hui Liu
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Xianfeng Tang
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Yuji Zhang
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Chi Wang
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Jiaxuan You
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Heng Ji
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Hanghang Tong
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Jingrui He
Agentic Reasoning for Large Language Models
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, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.
Agentic reasoning redefines large language models as autonomous agents capable of planning, acting, and learning through continuous interaction in dynamic environments across single-agent and multi-agent frameworks.
large language models agentic reasoning autonomous agents planning tool use search feedback memory adaptation collaborative settings coordination knowledge sharing reinforcement learning supervised fine-tuning in-context reasoning post-training reasoning real-world applications benchmarks thought and action world modeling scalable multi-agent training governance