LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation
TLDR
LiRA is a multi-agent framework that automates literature review writing, improving readability and accuracy via specialized agents.
Reasoning
The paper addresses an under-explored area (writing phase of reviews) with a novel multi-agent approach and includes real-world evaluations, but its scope is limited to literature review generation rather than broader scientific discovery or experimentation.
Read-first score
Read-first score 54, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 57.
Field roles
Rank sensitivity
Stability: volatile; rank range: 37.
Keyword Scores
Deep Analysis
Innovations
- Multi-agent collaborative workflow that emulates the human literature review process
- Specialized agents for content outlining, subsection writing, editing, and reviewing
- Focus on improving readability and factual accuracy in automated review generation
Methodology
LiRA is a multi-agent framework with agents for outlining, writing, editing, and reviewing, designed to generate literature reviews. It is evaluated on SciReviewGen and a proprietary ScienceDirect dataset against AutoSurvey and MASS-Survey baselines, using metrics of writing quality, citation quality, and similarity to human reviews, along with real-world retrieval and robustness tests.
Key Results
LiRA outperforms AutoSurvey and MASS-Survey in writing and citation quality while maintaining competitive similarity to human-written reviews, and shows robustness to reviewer model variation.