Same price, different reason: Probing AI pricing agents for collusion

A research sprint project tested whether linear probes on an LLM pricing agent's activations can distinguish collusive cues ("my rival will hold $1.90 if I don't undercut") from innocent ones ("my rival's costs rose, so it charges $1.90") when both produce the same price. On held-out wordings, probes reached 98.7% (Qwen2.5-7B), 99.6% (Qwen2.5-14B) and 89.3% (Llama-3.1-8B), versus 81.6%, 85.3% and 81.1% for TF-IDF word matching and about 50% for a price-only baseline, though the author notes the cues may differ in sentence structure and that probes trained on instructed cues transferred only weakly to uncued collusion in price histories.