Published on February 16, 2026, the report describes four cognitive levels for adjusting reasoning depth in LLM agents and reports 82.3% benchmark success for a 7B model.
A report published on February 16, 2026 presents CogRouter as a method that uses four hierarchical cognitive levels to adjust reasoning depth at each step of an LLM agent. Its training combines supervised fine-tuning and policy optimization. The text reports 82.3% success on benchmarks for a 7B model; the available material does not specify which benchmarks were used or the comparison conditions.
Step-level control may help engineers weigh agent performance against token use, but the reported result alone does not show that the method will work well across other agents or tasks. To check the details, consult the original report and verify the benchmark definitions, test setup, and how success was measured. If you use AI to study or apply these ideas, do not submit sensitive organizational data until you have checked your organization’s protections.