Coding agents predict edit outcomes 25 steps ahead

This research provides a fascinating glimpse into the internal planning capabilities of language models driving autonomous agents, proving they hold a deeper implicit plan than their explicit reasoning tokens suggest. Mechanistic interpretability is effectively extended from static completions to dynamic, multi-step agent trajectories. The existence of a latent programming horizon suggests that models maintain an internal "mental map" of the code's future trajectory well before it materializes. These findings could enable early-stopping or trajectory-correction mechanisms, halting an agent if its hidden states predict a regression long before compute is wasted on writing and testing bad code.