Compliant but Not Candid: How Reasoning Changes LLM Seller Collusion Under Oversigh
Konstantinos Mereos, Ali Kodratallah Ostowar · Team OmniaMen
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
LLM agents trading for firms can collude, and monitoring their messages is a natural defense. We ask what a seller coalition does when it learns it is being watched. In a double auc- tion of DeepSeek V4.1 Flash agents, we vary what sellers are told about monitoring against what is actually monitored: a bare prohibition, full review, 25% review, a bluff announcing full review but checking 25%, and a notice ar- riving after a coalition has formed. We run the conditions with seller reasoning on (5 ses- sions each) and off (10, pre-registered), and compare monitors that read sellers’ messages, notes and hidden reasoning, checked against two human raters. With reasoning on, sellers stop messaging under every notice, even a pro- hibition with no monitor, and their talk never raised prices. With reasoning off, sellers keep talking: announced review turns their messages bland (mean monitor score from 3.89 to 1.1– 1.4), while the prohibition barely changes them. Here talk raises prices, and every form of over- sight returns them to the no-communication level (about $3.5–4 lower), with no sign of coordination moving into asks, although the pre-registered cartel-hold tests were not signif- icant. Notes miss most collusive intent; only the hidden reasoning reveals sellers urging oth- ers to hold prices while undercutting them (23– 51% of unmonitored message turns). A bluff matched full review, and without reasoning a 25% monitor caught blatant collusion within two hours in 85% of cases. For the model we test, announced oversight mainly tests compli- ance, and cheap partial monitoring suffices.
Reviews
No public critique yet.
Cite this project
@misc{mereos2026compliant,
title = {{Compliant but Not Candid: How Reasoning Changes LLM Seller Collusion Under Oversigh}},
author = {Konstantinos Mereos and Ali Kodratallah Ostowar},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/compliant-but-not-candid-how-reasoning-changes-llm-seller-collusion-under-oversigh-f2vo}},
url = {https://apartresearch.com/sprints/projects/compliant-but-not-candid-how-reasoning-changes-llm-seller-collusion-under-oversigh-f2vo}
}More from AI Collusion Research Sprint
- View project: Collusion, Competion and Collapse
Collusion, Competion and Collapse
Atelier Alex Cline
Do AI traders collude when nobody tells them to? We built agora, a six-market benchmark with exact competitive and collusive benchmarks, and ran seven open-weight 2-4B models in 590 completed games under five …
- View project: Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Making AI Guardrails Understandable and Testable: A Framework Illustrated by a Simulated Bond Market
Rogue Agents in Jail 🕵️🕵️♀️👮♂️
In order to advance AI safety research into shaping public policy, the development of technical controls needs to have a clear purpose, evidence, and limitations that are understandable for the people responsible for …
- View project: Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Extending Information-Theoretic Collusion Detection to Large Language Model Agents
Gamma Ray
We test whether the information-theoretic collusion detector of Bonjour et al, validated on scripted agents, works on LLM agents. Gemma, Mistral and Qwen played 1000 rounds of three-player Rock-Paper-Scissors in three …