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Agentic Reasoning for Large Language Models

https://arxiv.org/pdf/2601.12538

The provided text outlines a comprehensive survey of Agentic Reasoning, a paradigm shift where Large Language Models evolve from passive generators into autonomous agents that plan, act, and learn. This evolution is categorized into foundational reasoning, involving core skills like tool-use and planning; self-evolving reasoning, where agents improve through feedback and memory; and collective reasoning, where multiple agents collaborate. The sources detail how these systems move beyond static inference toward iterative decision-making and adaptive intelligence. Practical applications are explored across diverse fields such as scientific discovery, software engineering, and healthcare, demonstrating the versatility of agentic frameworks. Furthermore, the survey evaluates these mechanisms using specialized benchmarks while identifying future challenges in trustworthiness, scalability, and long-horizon interaction. Ultimately, the text serves as a roadmap for transitioning from one-shot predictions to dynamic, self-improving ecosystems of artificial intelligence.

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