Dynamic Cognitive Orchestration: Eliciting Metacognitive Planning in Large Language Models

ICLR 2026 Conference Submission18380 Authors

Keywords: Metacognitive Planning, Dynamic Reasoning Strategy, Prompt Engineering, Cognitive Modules, LLM Reasoning

TL;DR: This paper introduces the Dynamic Cognitive Orchestrator (DCO), a two-stage prompting framework that enables LLMs to first plan a bespoke reasoning strategy and then execute it, achieving state-of-the-art results on complex reasoning benchmarks.

Abstract: Large Language Models (LLMs) have demonstrated significant reasoning capabilities, yet existing prompting methods often enforce fixed, linear reasoning paths. These static approaches lack the adaptive strategy selection characteristic of expert human cognition. To address this, we introduce the Dynamic Cognitive Orchestrator (DCO), a novel two-stage prompting framework that explicitly separates metacognitive planning from execution. First, in the Planner stage, the LLM analyzes a problem and generates a bespoke, problem-dependent reasoning strategy by selecting from a toolbox of cognitive modules. Second, in the Executor stage, the model systematically follows its self-generated plan to derive a solution. This framework models the brain’s executive functions, prioritizing cognitive flexibility over rigid procedural adherence. We evaluate DCO on challenging benchmarks including MATH, Codeforces, and BIG-Bench Hard. Our results show that DCO achieves new state-of-the-art accuracies of 89.2% on the MATH dataset, 42.0% on Codeforces problems, and 89.5% on BIG-Bench Hard, representing a substantial improvement over the strongest baselines. A detailed analysis of the generated plans reveals that the model’s ability to dynamically sequence modules is a key driver of its performance, particularly its selection of ‘FormalDeduction‘ for algebra and ‘HeuristicApproach‘ for geometry. By compelling LLMs to first ”reason about how to reason,” DCO establishes a new path toward more robust, interpretable, and adaptive AI systems.

Primary Area: causal reasoning

Submission Number: 18380