---
title: "MARG: Multi-Agent Review Generation for Scientific Papers (2024)"
description: "The authors present MARG, which turns GPT-4 into a team of cooperating agents. 6.1 recall. 39 references, cited by 68 papers here."
authors: ["Mike D'Arcy", "Tom Hope", "Larry Birnbaum", "Doug Downey"]
year: 2024
venue: "arXiv:2401.04259"
source: "https://arxiv.org/abs/2401.04259"
doi: "10.48550/arxiv.2401.04259"
arxiv: "2401.04259"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/arcy-2024-marg-multi-agent-review/"
---
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# MARG: Multi-Agent Review Generation for Scientific Papers

**Authors:** [Mike D'Arcy](https://md.research.elamur.ai/free/en/authors/mike-d-arcy/), [Tom Hope](https://md.research.elamur.ai/free/en/authors/tom-hope/), Larry Birnbaum and [Doug Downey](https://md.research.elamur.ai/free/en/authors/doug-downey/) · **Year:** 2024 · **Venue:** arXiv:2401.04259 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2401.04259) · [PDF](https://arxiv.org/pdf/2401.04259) · [DOI 10.48550/arxiv.2401.04259](https://doi.org/10.48550/arxiv.2401.04259) · [arXiv 2401.04259](https://arxiv.org/abs/2401.04259) · [OpenAlex](https://openalex.org/W4390784029)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · [68](https://md.research.elamur.ai/free/en/papers/arcy-2024-marg-multi-agent-review/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models struggle to critique full scientific papers: these texts are long and technical, and they often exceed or underuse a model's context window. The authors present MARG, which turns GPT-4 into a team of cooperating agents. The paper is split at paragraph boundaries into chunks, and each worker agent holds one chunk. A leader agent coordinates the work by broadcasting messages and collecting replies, so the whole text stays available without truncation or summarization. The strongest variant, MARG-S, adds expert agents for three comment types (experiments, clarity, impact). A further multi-agent pass refines each draft comment or discards it. In a user study, MARG-S produced more than twice as many useful comments per paper as a single-agent GPT-4 baseline, and its comments were far less generic. It also beat the GPT-4 method of Liang et al. (2023). However, most generated comments were still judged poor, and the system is costly. LLM-generated peer feedback is therefore promising but not yet reliable.

## Key points
- MARG uses three agent roles: a leader, workers holding paper chunks, and optional experts. The leader exchanges natural-language messages with the others. Figures and tables are not included in the input. Code is available at github.com/allenai/marg-reviewer.
- In the user study, MARG-S averaged 3.7 good comments per paper. The single-agent baseline averaged 1.7 (a 2.2x gain), and the Liang et al. (2023) approach averaged 0.3.
- The share of generic comments fell from 60% to 29%, and 71% of MARG-S comments were rated specific. In automatic alignment-based evaluation, MARG-S gained 6.1 recall points over the strongest baseline.
- Weaknesses remain:
  - Across all methods, most comments were rated bad, and 38–48% were judged highly inaccurate.
  - The refinement stage missed invalid comments in 47% of analyzed cases, often because information was lost in PDF parsing or ignored by the model.
- Proposed future work includes cheaper models for simpler sub-tasks, use of background literature, and compressing inter-agent messages to handle longer inputs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Allen Institute for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/) — Research institute, Seattle, United States · 304 works · 106,137 citations
- [Hebrew University of Jerusalem](https://md.research.elamur.ai/free/en/universities/hebrew-university-of-jerusalem/) — University, Jerusalem, Israel · 69 works · 9,594 citations
- [Northwestern University](https://md.research.elamur.ai/free/en/universities/northwestern-university/) — University, Evanston, United States · 46 works · 2,850 citations

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## Cite

Cite as: Mike D'Arcy, Tom Hope, Larry Birnbaum and Doug Downey. 2024. MARG: Multi-Agent Review Generation for Scientific Papers. arXiv:2401.04259.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{darcy2024marg,
  title = {{MARG: Multi-Agent Review Generation for Scientific Papers}},
  author = {Mike D'Arcy, Tom Hope, Larry Birnbaum and Doug Downey},
  year = {2024},
  doi = {10.48550/arxiv.2401.04259},
  eprint = {2401.04259},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2401.04259}
}
```

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*Updated 2 November 2026*