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Agentic Reasoning For Large Language Models

ASecurity

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 char...

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  • Added September 9, 2026
ai-agents

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Scanned September 9, 2026

npx -y skills add ADu2021/skillXiv --skill agentic-reasoning-for-large-language-models --agent claude-code

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SKILL.md
---
name: agentic-reasoning-for-large-language-models
title: "Agentic Reasoning for Large Language Models"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.12538"
keywords: [Agent, Reasoning]
description: "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 charact..."
---

## Overview

This skill covers agentic reasoning for large language models. It addresses critical challenges in autonomous agent development.

## Key Concepts

The paper introduces novel approaches to:
- Agent evaluation and benchmarking
- Improving agent efficiency and reasoning
- Designing robust agent systems

## When to Use

Use this when working on:
- Agent-based systems and evaluation
- Autonomous reasoning and planning
- Multi-agent frameworks

## When NOT to Use

- Non-agent applications
- Tasks requiring implementation code (see the paper)

## References

- Paper: https://arxiv.org/abs/2601.12538
- PDF: https://arxiv.org/pdf/2601.12538

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