UC San Diego, Tsinghua University researchers just made AI way better at knowing when to ask for help

A team of computer scientists has developed a method that helps artificial intelligence understand when to use tools versus relying on built-in knowledge, mimicking how human experts solve complex problems.

The research from the University of California San Diego and Tsinghua University demonstrates a 28% improvement in accuracy when AI systems learn to balance internal knowledge with external tools — a critical capability for deploying AI in scientific work.

How scientists taught AI to make better decisions

“While integrating LLMs with tools can increase reliability, this approach typically results in over-reliance on tools, diminishing the model’s ability to solve simple problems through basic reasoning,” the researchers write in their paper. “In contrast, human experts first assess problem complexity using domain knowledge before choosing an appropriate solution approach.”

The new method, called “Adapting While Learning,” uses a two-step process to train AI systems. First, the model learns directly from solutions generated ...