Counterfactual Resampling to Analyse Model Behaviour
Tomás Korenblit
Abstract
Done as my final project for a BlueDot Impact Technical AI Safety sprint, facilitated by BAISH (Buenos Aires AI Safety Hub) TL;DR: I measured Value Leakage (Betley et al.) per conversation, cutting CoTs at different points and resampling under a prompt that flips which answer serves the model's values. Betley et al.’s Value Leakage bias metric is a population average so it can’t discern individual conversations where a model’s bias influenced its decision. I designed a method to measure the same bias one conversation at a time by cutting the CoT and resampling under a prompt that flips which answer serves the model’s values. Qwen3.5 decides which side of the threshold it will land on about 20% through its CoT — before writing any estimate. By that point the text carries 88% [80%, 97%] of the bias measured at t = 0, and putting the bet back into the prompt changes almost nothing. Denials don’t cluster on the influenced traces. CoTs that deny being influenced lean towards the good side slightly less than admitting ones, this could provide evidence against the model selectively covering its tracks. Intent statements are done in retrospective. When a CoT contains which side the model m