LLM-Agents-Papers

🥇Gold

A repository listing papers on LLM-based agents, categorized by survey and enhancement techniques. Benefits operations teams by providing research insights for agent development and improvement.

2,2081340Updated 1w ago

Intermediate30min to implementautomation

Saves ~10 min per use

Quick InstallView Source

git clone https://github.com/AGI-Edgerunners/LLM-Agents-Papers.git

Works with:

Claude

Overview

About This Skill

A repository listing papers on LLM-based agents, categorized by survey and enhancement techniques. Benefits operations teams by providing research insights for agent development and improvement.

Use Cases

Researching the latest advancements in LLM-based agent technology for academic purposes.

Identifying effective techniques for enhancing LLM agents in automation workflows.

Exploring applications of LLM agents in specific fields like medicine, finance, and software engineering.

Staying informed about safety and ethical considerations in the deployment of LLM agents.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/AGI-Edgerunners/LLM-Agents-Papers

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Find the latest research papers on [TECHNIQUE] for LLM-based agents. Provide a summary of key findings and categorize them by [CATEGORY] such as survey or enhancement techniques. Focus on papers published in the last [TIMEFRAME] years.

Example Output

# Latest Research on Reinforcement Learning for LLM-Based Agents

## Key Findings
- **Paper 1**: Proposed a novel reinforcement learning framework that improves agent decision-making by 30%.
- **Paper 2**: Introduced a new reward shaping technique that enhances agent performance in complex environments.
- **Paper 3**: Demonstrated the effectiveness of hierarchical reinforcement learning in multi-agent systems.

## Categorization
### Survey Techniques
- Comprehensive review of reinforcement learning methods in LLM-based agents.
- Analysis of reward functions and their impact on agent behavior.

### Enhancement Techniques
- Novel algorithms for improving agent adaptability.
- Techniques for reducing training time while maintaining performance.