Replacing an LLM Pricing Agent Does Not Restore Competitive Prices

Replacing a flagged pricing algorithm is an obvious remedy for collusion, and in simulations it breaks Q-learning cartels, whose policies are fitted to their partners. The LLM agents here carry no learned state, only the price history in their prompt, so replacing one may not restore competition. We used six markets in the sprint's repeated Bertrand duopoly that crossed a pre-registered high-price threshold: four gemini-2.5-flash markets and two qwen3-14b markets instructed to cooperate. Each was continued for 50 rounds with one or both firms replaced and compared with a no-swap control from the same state, scoring the last 20 rounds. Replacing one firm with a same-family model, another provider's model, or the incumbent's own model told to avoid collusion changed the mean price index (0 = competitive, 1 = monopoly price) by about 0.05 or less. Replacing both gemini firms with gpt-4.1 kept prices high in 6 of 8 continuations. In exploratory arms, two current models undercut the incumbent, lowering the index: Claude Haiku 5.5 by 0.30 (95% CI 0.15 to 0.44) and GPT-5.6 Sol by 0.40 (0.21 to 0.58). Yet on the pre-registered from-scratch measure each kept high prices in 1 of 4 markets, as gpt-4.1 did. Four added markets gave the same conclusions, and no continuation's scored average returned to the competitive price. Replacing an agent cannot be assumed to end high prices; evaluations should test how an incoming agent behaves in a market that already has them, as well as whether it colludes from scratch.