Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents

Can reasoning scaffolds and task descriptions help large language model (LLM) agents make better decisions? We study this question in a multi-agent testbed of auctions and matching, where economic theory specifies an optimal strategy and principles for making it easier to recognize. Across four model families, we vary the interface through which agents act and the descriptions accompanying their decisions. An ascending auction interface that makes truthful play obviously optimal substantially reduces bid errors. With the interface fixed, laying out payoff contingencies and stating why truth-telling is safe also improve choices, while prompts for forward planning or belief formation can worsen them. In auctions, these behavioral gains are not accompanied by improvements in measured verbal indicators of strategic understanding in agents' stated plans. Conversely, a scaffold that changes the targeted language leaves bids unchanged, and the interventions that change both worsen bids. Human-motivated theories of simplicity can therefore inform the design of decision environments for artificial agents. Their success should be evaluated through realized decisions alongside explanations: effective scaffolds and descriptions need not produce corresponding changes in measured verbal understanding.