Description du poste
TL;DR
I took part in Apart's AI Collusion Research Sprint, where I built a realistic loan-review website. I tested whether AI reviewers are influenced by a colleague's note, even when told to judge independently, and whether a monitor can detect it, compared with a new tool I built for the sprint, the flow audit. Both reviewer models used the colleague's note in their ratings, and Claude said in its reasoning that its rating was independent in 21 out of 24 runs. The full-log monitor I used, Sonnet 5.5, caught only 1 of the 6 Claude cases, while the flow audit caught all 6, with no false alarms across 67 independent cases. On Gemma, the flow audit didn't do much better; both performed about equally. This is a small study: one domain and only 6 Claude cases.
Why I tested this
AI agents are increasingly working in shared spaces, and the concern about AI collusion is growing. Especially in situations where we want AI models to give an independent review, they can be influenced by other comments or posts, even when specifically told not to. That's what I am testing here.
The usual way to check an agent is to give its transcript to another model and have that model review it. Usually the monitor is a weaker model, so if the agent is a frontier model, it is less likely to be caught when it is wrong, because it may be capable of fooling the weaker monitor.
I tested two questions here:
• Does an AI reviewer that's told to do its task independently follow that instruction?• Can we detect the influence using another model that reads the reviewer's explanation and activity logs?Other studies have already shown that AI judges can be influenced by earlier scores and by other agents' work (Kapetanovic et al., Shu, Hashemi et al.), and that a model's reasoning doesn't always show why it gave one answer and not another (Turpin et al.). In this study, I test whether the model's reasoning is accurate when it claims independence, by checking its rating under different conditions, wit