Interesting new work on adaptive reasoning depth for LLM agents.

Not every agent step requires the same level of thinking. Some steps need strategic planning. Others are routine execution.

This research introduces CogRouter, a framework inspired by ACT-R cognitive theory that dynamically adjusts reasoning depth at each decision step across four hierarchical cognitive levels.

Appropriate cognitive depth should maximize the confidence of the resulting action. Training combines supervised fine-tuning for stable cognitive patterns with policy optimization for step-level credit assignment.

A 7B parameter model achieved 82.3% success rate on agent benchmarks, outperforming GPT-4o while consuming 62% fewer tokens.

Why does it matter?

Adaptive reasoning is a more practical path to efficient agents than simply scaling model size. Think fast when you can, slow when you must.

Paper: arxiv.org/abs/2602.12662

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