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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

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The following papers were recommended by the Semantic Scholar API

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• Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning (2025)
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• Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges (2025)
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However, human knowledge encompasses over 200 specialized disciplines,\nfar exceeding the scope of existing benchmarks. The capabilities of LLMs in\nmany of these specialized fields-particularly in light industry, agriculture,\nand service-oriented disciplines-remain inadequately evaluated. To address this\ngap, we present SuperGPQA, a comprehensive benchmark that evaluates\ngraduate-level knowledge and reasoning capabilities across 285 disciplines. Our\nbenchmark employs a novel Human-LLM collaborative filtering mechanism to\neliminate trivial or ambiguous questions through iterative refinement based on\nboth LLM responses and expert feedback. Our experimental results reveal\nsignificant room for improvement in the performance of current state-of-the-art\nLLMs across diverse knowledge domains (e.g., the reasoning-focused model\nDeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting\nthe considerable gap between current model capabilities and artificial general\nintelligence. 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