AI RESEARCH
OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials
arXiv CS.CV
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ArXi:2603.17043v1 Announce Type: new The transition from optical identification of 2D quantum materials to practical device fabrication requires dynamic reasoning beyond the detection accuracy. While recent domain-specific Multimodal Large Language Models (MLLMs) successfully ground visual features using physics-informed reasoning, their outputs are optimized for step-by-step cognitive transparency. This yields verbose candidate enumerations followed by dense reasoning that, while accurate, may induce cognitive overload and lack immediate utility for real-world interaction with researchers.