Update ResearchRadar layout

Author: strickvl

research-radar/README.md

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 # 🚀 ResearchRadar: AI Literature Discovery and Classification Pipeline
 
-## 📑 Overview
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 This project demonstrates how AI researchers can build an automated pipeline to discover, classify, and organize research papers relevant to their specific subfields. Using ZenML's end-to-end ML pipelines, researchers can train models to automatically identify papers that align with their research interests—whether it's reinforcement learning, computer vision, NLP, or emerging areas like multimodal models—from across various sources including preprint servers, journals, and conference proceedings.
 
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 The system showcases how researchers can fine-tune models to recognize subtle distinctions between related subfields, saving countless hours normally spent manually filtering through the overwhelming volume of AI research being published daily. The comparative analysis pipeline demonstrates whether custom-trained models outperform commercial APIs when identifying highly specialized technical content, helping research teams make informed decisions about their literature review infrastructure.
 
 This framework is ideal for academic labs, industry research groups, and individual AI researchers who need to maintain comprehensive awareness of relevant publications without spending hours each week manually sifting through papers. By deploying the trained model to Hugging Face, teams can create shared resources that help all members stay current with literature relevant to their collective research agenda, streamlining collaboration and reducing duplicate effort in literature reviews.