Parallel Lying Looks Like Collusion: A Control Condition for Multi-Agent Monitoring
Swasti Choubey · Team HybridViz
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
I evaluated two LLM monitors for detecting collusion in a four-agent Diner’s Dilemma (Shi et al. (2026)) game across 30 episodes, comparing four evidence presentations against coalition, solo deception, independent deception, and baseline conditions. Although structured evidence initially achieved 0.92 AUROC, performance fell to chance when distinguishing colluding agents from independent deceivers. A simple counting rule achieved 0.83 AUROC, outperforming every LLM configuration overall. These findings highlight a fundamental limitation: detecting suspicious behavior does not establish collusion.
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@misc{choubey2026parallel,
title = {{Parallel Lying Looks Like Collusion: A Control Condition for Multi-Agent Monitoring}},
author = {Swasti Choubey},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/parallel-lying-looks-like-collusion-a-control-condition-for-multiagent-monitoring-kmye}},
url = {https://apartresearch.com/sprints/projects/parallel-lying-looks-like-collusion-a-control-condition-for-multiagent-monitoring-kmye}
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