A 3D Characterization Framework for Intelligent Sequential Decision Making

The paper introduces a three‑dimensional framework for comparing AI methods that solve sequential decision‑making puzzles. It maps each approach to a Markov decision process, evaluates autonomy via human ranking, and measures skill against computational cost. Applying this to the Tower of Hanoi, the authors compare graph‑based, reinforcement learning, and large language model techniques, finding that LLMs shift complexity to inference time, incurring higher memory and runtime costs.

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• llms
• agents
• reinforcement-learning
• benchmarks