EvoResearch: A Multi-Agent AI Framework for Automated Paper Analysis

Abstract

The rapid growth of scientific publications has intensified the challenge of efficiently extracting essential knowledge from large sets of research papers. Traditional summarization models lack cross-document reasoning, exhibit factual inconsistencies, and provide limited support for structured knowledge integration. To address these issues, this work proposes EvoResearch, a multi-agent AI framework designed for automated scholarly paper analysis, integrating summarization, knowledge extraction, knowledge graph construction, and hypothesis synthesis. The framework leverages advances in abstractive summarization [1], aspect-based modeling [5], optimization-based extraction [8], multimodal learning [16], and reinforcement-learning-driven knowledge graph techniques [4]. Experimental results demonstrate improvements in coherence, factual accuracy, and multi-document synthesis quality compared to existing systems. EvoResearch enables enhanced scientific reasoning and supports automated literature review pipelines