$\texttt{APOLLO}$: A Self-Guided Multi-Agent System for Scientific Article Generation inspired by Human Thinking

Danilo Toapanta, Artemis Capari, Zahra Abbasiantaeb, Zi Long Zhu, Hosein Azarbonyad, Zubair Afzal, George Tsatsaronis

Keywords: LLM-based Multi-agent Systems, Long-form text generation, Knowledge Graph Construction, Fact Verification

TL;DR: We introduce Apollo, a multi-agent framework that generates comprehensive and fact-checked Wikipedia-style scientific articles with citations, outperforming existing methods in accuracy, structure, and diversity.

Abstract:

Automatic generation of Wikipedia-like articles through Retrieval-Augmented Generation (RAG) has recently gained increasing attention.
While recent advances in Large Language Models (LLMs) show considerable promise for synthesizing complex information, current RAG-based systems suffer from two fundamental limitations: they often rely on shallow retrieval strategies, leading to redundant content, and they lack effective mechanisms for factual verification and content organization.
To address these challenges, we present $\texttt{APOLLO}$, a multi-agent framework specifically designed to generate high-quality, comprehensive articles with citations to the given sources.
$\texttt{APOLLO}$ simulates the iterative research and editorial process of human contributors through a set of specialized agents that collaboratively retrieve, fact-check, and structure information.
To evaluate our method, we introduce SciWiki-2k, a dataset comprising 2,000 high-quality Wikipedia articles spanning 20 scientific domains.
Compared to baseline methods, $\texttt{APOLLO}$ produces articles with significantly improved structural coherence, content diversity, and factual accuracy. Human evaluations further establish the practical value of our approach for generating trustworthy, comprehensive articles. In this work, we target scientific Wikipedia‑style articles, using ``Wikipedia‑like'' solely to denote an encyclopedic structure with hierarchical sections and in‑line citations rather than coverage of all Wikipedia domains.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 17707