As large language models (LLMs) gain momentum worldwide, there’s a growing need for reliable ways to measure their performance. Benchmarks that evaluate LLM outputs allow developers to track ...

As new large language models, or LLMs, are rapidly developed and deployed, existing methods for evaluating their safety and discovering potential vulnerabilities quickly become outdated. To identify ...

To stay up to date and work forward in their fields, scientists must have at their fingertips and in their minds thousands of ...

As large language models (LLMs) continue to improve at coding, the benchmarks used to evaluate their performance are steadily becoming less useful. That's because though many LLMs have similar high ...

The rivalry between Qwen 3.5 and Sonnet 4.5 highlights the shifting priorities in large language model development. Qwen 3.5, ...

This study introduces MathEval, a comprehensive benchmarking framework designed to systematically evaluate the mathematical reasoning capabilities of large language models (LLMs). Addressing key ...

Companies are spending enormous sums of money on AI systems, and we are now at a point where there are credible alternatives ...

In updated tests published to the Humanity's Last Exam website, Gemini's 3.1 Pro model achieved 45.9 percent accuracy, with a ...

Though new regulatory frameworks address fairness, accountability, and safety in AI systems, they often fail to directly ...