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README.md

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 Multimodal Large Language Models (MLLMs) have achieved notable progress in visual–language understanding and cross-modal reasoning, yet their capabilities remain limited when applied to the highly specialized task of spectral understanding. These limitations are further obscured by existing benchmarks, which either emphasize general object recognition or focus on simple chart-based data retrieval, lacking the scientific grounding needed to accurately assess or diagnose model performance in this domain.
 
-To address this gap, we introduce SpecVQA, an expert-curated benchmark that targets the key failure modes of MLLMs in spectral interpretation. By focusing on seven essential spectrum types, it enables concise yet rigorous evaluation of scientific accuracy and domain-knowledge usage, providing clearer guidance for developing more domain-aware multimodal models. The seven spectrum types and their core interpretation requirements are:
+To address this gap, we introduce SpecVQA, an expert-curated benchmark that targets the key failure modes of MLLMs in spectral interpretation. By focusing on seven essential spectrum types, it enables concise yet rigorous evaluation of scientific accuracy and domain-knowledge usage, providing clearer guidance for developing more domain-aware multimodal models. The seven spectrum types are:
 
 -  NMR (Nuclear Magnetic Resonance) 
 -  IR (Infrared Absorption Spectroscopy)