“I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they ...

Large reasoning models often show counterintuitive behavior, putting more computational effort into simple tasks than difficult ones while producing worse results overall. Researchers have established ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

DeepAgent is a reasoning agent with scalable toolsets, capable of tackling general tasks by searching for and using the appropriate tools from over 16,000 RapidAPIs in an end-to-end agentic reasoning ...

Long-running LLM agents equipped with strong reasoning, planning, and execution skills have the potential to transform scientific discovery with high-impact advancements, such as developing new ...

Most current benchmarks, such as GSM8K and MATH, evaluate LRMs by asking one question at a time. While effective for initial model development, this isolated question approach faces two critical ...

Recent research indicates that LLMs, particularly smaller ones, frequently struggle with robust reasoning. They tend to perform well on familiar questions but falter when those same problems are ...

AI reasoning models were supposed to be the industry's next leap, promising smarter systems able to tackle more complex problems and a path to superintelligence. The latest releases from the major ...