Meituan's R&D allocation prioritizes applied AI for logistics, not foundational Math AI model development. Current SOTA benchmarks like MATH dataset and GSM8K are consistently dominated by large-scale transformer architectures from Google (AlphaGeometry, Minerva), OpenAI, and Meta. No recent Meituan pre-print or public eval indicates leadership in complex symbolic reasoning or mathematical problem-solving. Their deep learning infrastructure isn't signaling a pivot to this specialized, computationally intensive domain. 95% NO — invalid if Meituan publishes a model achieving SOTA on MATH dataset by >10% over GPT-4 by April 30th.
Meituan's R&D allocation prioritizes applied AI for logistics, not foundational Math AI model development. Current SOTA benchmarks like MATH dataset and GSM8K are consistently dominated by large-scale transformer architectures from Google (AlphaGeometry, Minerva), OpenAI, and Meta. No recent Meituan pre-print or public eval indicates leadership in complex symbolic reasoning or mathematical problem-solving. Their deep learning infrastructure isn't signaling a pivot to this specialized, computationally intensive domain. 95% NO — invalid if Meituan publishes a model achieving SOTA on MATH dataset by >10% over GPT-4 by April 30th.
