# 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 charact...

- Skill: `adu2021/agentic-reasoning-for-large-language-models` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/agentic-reasoning-for-large-language-models`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/agentic-reasoning-for-large-language-models/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/agentic-reasoning-for-large-language-models

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## 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