Closing the Chat Ended the Cartel: Four LLMs Did Not Reroute Collusion into Action Channels

Closing the chat that colluding AI agents talk through is the most direct countermeasure to collusion, but it might only push coordination into channels that are harder to monitor (the “balloon effect”). We tested this in a procurement auction where two firms run by the same LLM, allowed and paid to maximise joint profit, formed a cartel over chat for 40 rounds. We then closed the chat and measured, with permutation tests whose null is exactly zero, whether cost information moved into four routine action channels that stayed open. In a pre-registered experiment with four models (gpt-6-luna, claude-haiku-5-5, Qwen3.8-27B, Gemma-4-26B; 32 paired seeds), it did not: the flow was statistically equivalent to zero for every model. gpt-6-luna, the one model whose cartel worked, lost most of its cartel premium (profit per firm per round fell from 9.61 to 7.82). An exploratory control showed that the channels could carry the information: handed a code, Qwen sent 1.32 bits per round and its rival’s bids responded (p = 0.015). The limit was propensity, not capacity. Agents also kept using a promised but dead private channel, and their self-reports claimed covert signalling that never happened. These results cover only these four mid-sized, low-reasoning models in this setting.