Knowledge-extractor: a self-evolving scientific framework for hydrogen energy research driven by AI agents

The rapid evolution of Artificial intelligence (AI) from passive “knowledge co-pilots” to autonomous “research partners” is initiating a paradigm shift in scientific discovery, a frontier now termed Agentic Science. However, applying general-purpose AI systems to dynamic, vertically integrated domains such as hydrogen energy reveals critical limitations, including a lack of deep domain knowledge, an inability to process real-time information, and insufficient autonomous planning capabilities. To address these challenges, we introduce Knowledge-Extractor, a self-evolving scientific framework for building domain-expert AI agents, which we implement and evaluate in the hydrogen energy domain via an agent named Hydrogen-Agent. The core of our framework is a Hybrid Knowledge Integration strategy, which synergistically combines a domain-fine-tuned large language model (LLM) as its "cognitive core" with a continuously updated, non-parametric knowledge base.This architecture is augmented by an autonomous toolset comprising a PolicyRetriever (for extracting information from policy documents), a WebBrowser (for retrieving online sources), and an ArxivAnalyzer (for analyzing scientific papers from arXiv). We demonstrate that through an autonomous knowledge loop, Hydrogen-Agent overcomes the static knowledge limitations of traditional models. Our experiments validate a “specialization effect” where domain-specific fine-tuning enhances factual accuracy on our HydroBench benchmark, outperforming its base model and powerful generalist LLMs. Furthermore, three case studies illustrates the ability of the agent to autonomously conduct complex, end-to-end research tasks, from multi-source data gathering to the generation of a strategic analysis report. Hydrogen-Agent serves as a robust prototype for future scientific agents, showcasing a viable path toward creating domain-expert AI that can accelerate discovery in critical scientific fields.