If an LLM agent can neatly explain why a strategy is "safe," does that actually prove it understands it? Even in auctions and matching markets where telling the truth is provably the best move, LLM agents still drift from optimal decisions. A team from Harvard and MIT tested whether changing the interface or adding textual scaffolds could fix this — and the results were striking. Simply switching a second-price auction to an ascending clock format pushed Gemma's mean deviation from -5.3 to a near-perfect -0.3. In matching markets, just telling the model "your offer only determines whether you get rejected" cut the error rate from 4.2% to 0.2%. 🔍 But the real twist comes next. In the payoff-safety intervention that substantially improved bidding, none of the 600 treated plans even mentioned the stated safety rationale. Meanwhile, belief prompts that asked the model to reason about opponents pushed opponent-modeling language from 2% to 43% — while the actual bids stayed completely flat. Title: Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents URL: In other words, what a model can articulate and what it actually does are nearly separate things. The practical lesson lands hard: judging an agent's understanding by its explanations alone isn't enough — you have to check the actual decisions too. #LLMAgents# #DecisionMaking#