Satvik Golechha

Using a red-team/blue-team auditing game, we find that black-box and naive model-internal methods fail to consistently distinguish sandbagging from benign underperformance. Training-based capability elicitation reliably restores full performance in sandbagging models but also risks false positives by boosting benign models.

We introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as a consequence of the game objectives. While most benchmarks saturate quickly, Among Us can be expected to last much longer, because it is a multi-player game far from equilibrium.