Francisco Guzmán

Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories

In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations. We show that successful and failed rollouts of the same task separate in this space, with simple per-task discriminators predicting held-out rollout outcomes at up to 0.95 AUROC across thre...