Agentic Reasoning for Large Language Models (arxiv.org)

A recent survey on "Agentic Reasoning for Large Language Models" introduces a transformative perspective on how these models can operate as autonomous agents. Unlike traditional approaches that excel in closed environments, agentic reasoning enables LLMs to plan, act, and adapt in open and dynamic settings through continual interaction. The framework categorizes reasoning into three levels: foundational agentic reasoning focuses on core capabilities such as planning and tool use; self-evolving reasoning emphasizes adaptation via feedback; and collective reasoning explores collaboration among multiple agents. This development is significant for the AI/ML community as it bridges the gap between thought and action, pushing the boundaries of current LLM applications in real-world scenarios like healthcare and robotics. The survey outlines key methodologies for enhancing LLMs, including in-context reasoning and post-training optimization strategies. Additionally, it highlights future challenges such as personalization, long-horizon interactions, and governance, setting a roadmap for integrating these advanced reasoning capabilities into practical systems. This shift promises to expand the utility and efficiency of LLMs, making them more effective in complex, unpredictable environments.