tool-augmented reasoning

Tool-augmented reasoning lets the model stop guessing at things a tool can answer exactly and call that tool instead. Faced with a big multiplication, it writes the calculation and hands it to a calculator; needing today's price, it queries a database or the web; asked to analyse data, it writes and runs code. The model's job shifts from doing every step in its head to deciding what to compute, dispatching the work, and weaving the precise result back into its answer.

This plays directly to the model's strengths and patches its weaknesses. Language models are wonderful at choosing a strategy and terrible at exact mechanical computation or recalling fresh facts, which is exactly what tools are good at. The result is reasoning that is both more capable and more verifiable, because the hard numerical or factual steps now have a checkable source rather than a confident guess. The new failure mode is at the seams: calling the wrong tool, passing it bad inputs, or misreading what it returns, so the model still has to reason about its tools, not just with them.