Monitored Collusion in LLM Auctions

LLM agents that submit bids or set prices on behalf of firms could learn how to coordinate with one another, so monitoring their chats is a proposed safeguard. We ask whether agents aware threat their chats are being monitored moved coordination from what they say into what they say into how they bid and if chat monitors could catch them. I ran 80 games of a repeated first-price procurement auction(three firms, 20 rounds, chat then selected bids) in four conditions: compete (negative control), neutral, collude_open. (positive control) and collude-monitored(a stated but uninformed monitoring threat). The controls behaved : late-game was 0.06 under compete and 0.94 under collude_open. Under the stated threat, collusion fell to 0.78 and explicit price talk fell from 100% to 72% of messages, but did not hide. Chat-only monitors built on three model families (gpt-4o, DeepSeek, Qwen2.5-32B) flagged all 20 games in every non-controlled condition (AUC about 1.00). A model-free bid screen was weaker on the neutral condition(AUC 0.88), and adding bods to the LLM monitor did not help (gpt-4o AUC 0.87 and 0.89). The chat monitor also flags friendly tone at near-zero markup, so its ceiling performance may reflect tone as much as prices. Collusion here is instructed and the threat is unenforced; an enforced condition was designed but not run.