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This article provides an in-depth explanation of the principle of reasoning effort in large language models: by adjusting the reasoning effort level (low/medium/high) on the same model, more intermediate reasoning trajectories (serial autoregressive computation) are allowed before outputting the final answer, thereby making trade-offs among answer quality, response speed, and computational cost. The key is that the model has learned to organize longer generation processes into effective computation, rather than simply increasing parameters or network layers.
The article discusses where to add heavy reasoning using Ring-2.6-1T in agent workflows to guard against failure points such as state corruption, tool-contract mismatch, or the final external action.