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Sprint projectOct 25, 2026India

Auditing Belief-Correction Prompts for LLM Pricing Agents with a Ground-Truth Rival

Sachin Ruhil · Team X

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Auditing Belief-Correction Prompts for LLM Pricing Agents with a Ground-Truth Rival

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We audit belief-correction prompts for an LLM pricing agent against a ground-truth scripted rival in a Calvano duopoly. The LLM reports a retaliation forecast each round; we compare control, placebo, and corrected (true rival policy) over 21 episodes (7×3×120 rounds). Supra-Nash pricing is rare but real and one-sided (rival stays near Nash): clear high-price runs in 2/7 control and 2/7 corrected, 0/7 placebo. Mean late-window elevation: control +0.44, placebo +0.02, corrected +0.78. Belief correction did not reliably reduce supra-Nash pricing; outcomes are path-dependent. Cut-round Brier stayed poor (corrected 0.42 vs control 0.43, both worse than a 50% forecast), so the main finding is that this correction prompt barely changed beliefs, not that correct beliefs fail to lower prices.

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Cite this project

@misc{ruhil2026auditing,
  title = {{Auditing Belief-Correction Prompts for LLM Pricing Agents with a Ground-Truth Rival}},
  author = {Sachin Ruhil},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/auditing-beliefcorrection-prompts-for-llm-pricing-agents-with-a-groundtruth-rival-zs5g}},
  url = {https://apartresearch.com/sprints/projects/auditing-beliefcorrection-prompts-for-llm-pricing-agents-with-a-groundtruth-rival-zs5g}
}

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