Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs.

With the rapid advancement of Large Language Models (LLMs), an increasing number of researchers are focusing on Generative Recommender Systems (GRSs). Unlike traditional recommendation systems that rely on fixed candidate sets, GRSs leverage generative capabilities, making them more effective in exploring user interests.

Researchers have developed a fully real-valued, end-to-end optical neural network chip that overcomes the physical limitation of non-negative light intensity to enable efficient, on-chip image generation.

A novel bioengineering strategy utilizing peptide display technology on the AAV1 capsid has successfully generated next-generation viral vectors with significantly enhanced specificity and efficiency for inner ear cell transduction, offering a promising advance toward targeted gene therapies for hearing and balance disorders.