Who Sits First: Seat Position Fools Interventional Collusion Audits of LLM Sellers

We audit stateless language-model sellers for coordination in the organisers' three-seller logit-demand market. We edit a rival's past prices; the KL divergence between the seller's next-price distributions before and after the edit is the rival's raw influence. Ground truth is a planted pair told to collude; we score it against the other pairs by AUROC (0.5 is chance) on eight open-weight models. With the pair in seats (0,1), where each seller lists its partner first, raw influence reaches AUROC 0.98 to 1.00 for seven models, and a 95th-percentile threshold on unplanted games detects 41 to 100 percent of planted games. With the pair in seats (1,2) it falls to 0.17 for Falcon3, 0.53 for Gemma-2 and 0.69 for Gemma-4. A score that cancels each seller's general responsiveness reverses with the seating in Falcon3, Gemma-2 and Llama, so it reads list position. Netting subtracts a scripted best responder's influence; it raises the two-seat average AUROC in three of seven models, by 0.03 to 0.34. The best responder's own score, which needs no query, reaches 0.83 to 0.99 in six models, and netting scores the unplanted pair (0,2) below chance in six of eight. A third seller told to best respond lowers raw influence to 0.21 for Llama and 0.27 for Mistral; with no planted pair, raw influence flags pairs with that seller at 0.92 and 0.88.