Say Less: Legal Framing Makes LLM Price Agreements Less Explicit

Competition authorities are extending leniency and informant rewards to algorithmic collusion, and AI-governance proposals would grant leniency to agents that report collusion. Both assume agents respond to the incentives these programs create. We test this in a reimplementation of Lee's (2026) LLM leniency experiment (a repeated Bertrand duopoly with cheap talk, reporting and an LLM authority) across six model families (754 supergames).

Three findings: Lee's single-model result does not generalise. Under a fine-only rule, where reporting cannot lower the reporter's own fine, the share of periods with a report ranges from 0.01 (Claude Haiku 5.5) to 0.94 (gpt-4o-mini), although all families state the fine correctly. The reporting is not a reaction to legal vocabulary. In a preregistered test, neutral wording reduced it in only one of three high-reporting families. Legal framing makes price agreements less explicit. In a preregistered test with fresh neutral arms, calling price agreements "illegal" cut the share of chats with an explicit agreement from 0.67 to 0.12 (Haiku) and from 0.85 to 0.29 (Llama-3.3-70B), both p < 10⁻⁶. Markups did not fall in these two families (95% CIs within ±0.09 Δ). Exploratory data show the pattern in four of five families, and gpt-4o-mini's markups do fall. The effect survives classifier-error and attrition checks, robustly for Haiku and except under an adversarial assumption for Llama. It fits avoidance of normatively flagged wording better than strategic concealment. Either way, monitors that read agent conversations see less explicit coordination once agents are told it is illegal.