Evaluating language models for mathematics through interactions.

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs; this is insufficient for making an informed decision about which LLMs are best to use in an interactive setting, and how that varies by setting. Static assessment therefore limits how we understand language model capabilities.

Solving olympiad geometry without human demonstrations.

Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges, resulting in severe scarcity of training data.

Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators in the Diagnosis and Subtype Classification of Rheumatoid Arthritis.

m6A modification is the most abundant mRNA modifications and plays an integral role in various biological processes in eukaryotes. However, the role of m6A regulators in rheumatoid arthritis remains unknown. To determine the expression of m6A RNA methylation regulators in rheumatoid arthritis and their possible functional and prognostic value.

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems.

Association of carotid atherosclerosis and recurrent cerebral infarction in the Chinese population: a meta-analysis.

Stroke, when poor blood flow to the brain results in cell death, is the third leading cause of disability and mortality worldwide, and appears as an unequal distribution in the global population. The cumulative risk of recurrence varies greatly up to 10 years after the first stroke. Carotid atherosclerosis is a major risk factor for stroke.

STDP-Compatible Approximation of Backpropagation in an Energy-Based Model.

We show that Langevin Markov chain Monte Carlo inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similar to backpropagation. The backpropagated error is with respect to output units that have received an outside driving force pushing them away from the stationary point. Backpropagated error gradients correspond to temporal derivatives with respect to the activation of hidden units.