Code Owns the Simulation, Jev Owns the Evaluation

Judgment models such as jev return, in a single call and without reasoning text, a probability for each described option. This makes them attractive as an agent's action-selection layer, but it is unclear which decisions they can be trusted with. We test jev on reflection tests, one-shot matrix games, the text game ALFWorld and robot control, and find a sharp boundary. jev succeeds when the right option can be judged from what the input describes, which we call evaluation. Specifically, it solves 99% of the counterintuitive Cognitive Reflection Test questions. However, it fails when the right option depends on simulation (i.e., predicting something not in the input), such as the opponent's action or the subgoal that must come first. In games, jev plays suboptimally as if its rational opponent acted at random, because the opponent's action is not given. In ALFWorld, jev favors commands that mention an object or place named in the task description. For example, given the task ``put a clean knife in the drawer'', jev carries an unwashed knife straight to the drawer instead of first washing it at the sink. Surprisingly, many of these failures are not due to a lack of knowledge. Asked separately what the opponent will do, jev usually answers correctly, and it responds well given the opponent's action. It fails when one call must both perform the simulation and evaluate based on it. This suggests letting code make the prediction or simulation. When code supplies it, such as a lookahead in ALFWorld and physics simulation in robot control, jev becomes an expert controller through its general evaluation ability.