# MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer

arXiv: https://arxiv.org/abs/2506.13037 · HTML: https://mlsanity.com/paper/2506.13037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes](https://mlsanity.com/paper/2310.10648/) (2023)
- [Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring](https://mlsanity.com/paper/2404.04941/) (2024)
- [LLM-as-a-tutor in EFL Writing Education: Focusing on Evaluation of Student-LLM Interaction](https://mlsanity.com/paper/2310.05191/) (2023)
- [Automatic Short Answer Grading and Feedback Using Text Mining Methods](https://mlsanity.com/paper/1807.10543/) (2018)
- [Are Large Language Models Good Essay Graders?](https://mlsanity.com/paper/2409.13120/) (2024)
- [Evaluating Language Models for Generating and Judging Programming Feedback](https://mlsanity.com/paper/2407.04873/) (2024)
- [Automated Evaluation for Student Argumentative Writing: A Survey](https://mlsanity.com/paper/2205.04083/) (2022)
- [Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets](https://mlsanity.com/paper/2502.19339/) (2025)

## Built on by (most cited first, 2 of 2)

- [Beyond Holistic Scores: Automatic Trait-Based Quality Scoring of Argumentative Essays](https://mlsanity.com/paper/2602.04604/) (2026)
- [GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop](https://mlsanity.com/paper/2512.05502/) (2025)

## Abstract (arXiv)

Automated Essay Scoring (AES) and Automatic Essay Feedback (AEF) systems aim to reduce the workload of human raters in educational assessment. However, most existing systems prioritize numerical scoring accuracy over feedback quality and are primarily evaluated on pre-secondary school level writing. This paper presents Multi-Agent Argumentation and Grammar Integrated Critiquer (MAGIC), a framework using five specialized agents to evaluate prompt adherence, persuasiveness, organization, vocabulary, and grammar for both holistic scoring and detailed feedback generation. To support evaluation at the college level, we collated a dataset of Graduate Record Examination (GRE) practice essays with expert-evaluated scores and feedback. MAGIC achieves substantial to near-perfect scoring agreement with humans on the GRE data, outperforming baseline LLM models while providing enhanced interpretability through its multi-agent approach. We also compare MAGIC's feedback generation capabilities against ground truth human feedback and baseline models, finding that MAGIC achieves strong feedback quality and naturalness.