LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation

2025-10-01

AI Summary

The exponential growth of scientific literature impedes systematic reviews from simultaneously achieving comprehensiveness, timeliness, and readability; existing automated approaches predominantly focus on retrieval and screening, neglecting factual accuracy and textual quality during the writing phase. This paper introduces the first multi-agent collaborative framework specifically designed for review authoring, which emulates human workflow through four specialized agents—Outline, Writing, Editing, and Reviewing—that jointly perform end-to-end generation. The framework requires no domain-specific fine-tuning and integrates systematic literature retrieval, structured citation handling, and segment-wise controllable text generation. Evaluated on SciReviewGen and ScienceDirect benchmarks, it significantly outperforms baselines including AutoSurvey and MASS-Survey. Generated reviews achieve near-human performance in factual accuracy, linguistic readability, and citation conformity.

Technology Category

• Multiagent Systems: Teamwork
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing.

Problem

• Automating systematic literature review writing phase
• Improving readability and factual accuracy of reviews
• Generating cohesive comprehensive articles via multi-agent workflow

Innovation

• Multi-agent collaborative workflow emulates human review process
• Specialized agents handle outlining writing editing reviewing
• Agentic LLM workflows improve reliability without domain tuning