Researchers introduced Deep Reasoning, an inference-time approach that enables general-purpose agents to construct task-specific reasoning scaffolds through structured meta-reasoning. This method represents meta-reasoning as executable decompositions and allows encoding decomposition principles as in-context examples. The approach was instantiated in a general-purpose agent called DOLORES, which outperformed state-of-the-art scaffolding methods across four benchmarks by 24.8% on average. DOLORES distributes complex tasks across controlled reasoning threads, reducing premature termination and hallucinations. This improvement can be particularly significant for large models, as an 8B parameter version of DOLORES surpassed 32B baseline models in over half of the evaluated settings.