Interactions among viruses can help them succeed inside their hosts or impart vulnerabilities that make them easier to treat. Scientists are learning the ways viruses mingle inside the cells they infect, as well as the consequences of their socializing. Although it is debatable whether viruses are living things, they do compete, cooperate and share genome materials that can sometimes alter their responses to antiviral drugs, result in new variants or play a role in virus evolution. A paper today in Nature Ecology & Evolution by UW Medicine scientists looks at the evolution of poliovirus resistance to a promising experimental antiviral drug, pocapavir. While it seemed counterintuitive, the researchers demonstrated that lowering the potency of pocapavir could improve the situation by enhancing the survival of enough susceptible viruses to continue sensitizing the resistant ones.

Future events such as the weather or satellite trajectories are computed in tiny time steps, so the computation must be both efficient and as accurate as possible at each step lest errors pile up. A Kobe University team now introduces a new method that uses deep learning for creating tailored, accurate simulations that respect physical laws, while also being more computationally efficient.

In a new article, scientists from UChicago and universities in the US and EU look to computing’s past to chart quantum’s future. Their conclusion: the technology is maturing rapidly, and the next challenges are related to scaling.

Mathematical reasoning is a fundamental aspect of intelligence, encompassing a spectrum from basic arithmetic to intricate problem-solving. Recent investigations into the mathematical abilities of large language models (LLMs) have yielded inconsistent and incomplete assessments. In response, we introduce MathEval, a comprehensive benchmark designed to methodically evaluate the mathematical problem-solving proficiency of LLMs in various contexts, adaptation strategies, and evaluation metrics. MathEval consolidates 22 distinct datasets, encompassing a broad spectrum of mathematical disciplines, languages (including English and Chinese), and problem categories (ranging from arithmetic and competitive mathematics to higher mathematics), with varying degrees of difficulty from elementary to advanced. To address the complexity of mathematical reasoning outputs and adapt to diverse models and prompts, we employ GPT-4 as an automated pipeline for answer extraction and comparison. Additionally, we trained a publicly available DeepSeek-LLM-7B-Base model using GPT-4 results, enabling precise answer validation without requiring GPT-4 access. To mitigate potential test data contamination and truly gauge progress, MathEval incorporates an annually refreshed set of problems from the latest Chinese National College Entrance Examination (Gaokao-2023, Gaokao-2024), thereby benchmarking genuine advancements in mathematical problem solving skills.