# Hidden-State Trajectory Features

Using paired enabled/bypassed executions of the final attention layer to produce signed next-token loss differences in frozen SmolLM3-3B-Base and Qwen3.5-4B-Base, the study tests whether hidden-state extrapolation error, curvature and error change improve attention routing beyond uncertainty, one-step displacement, position and state projections; on 100 held-out PG-19 books at an identical causal 20% invocation quota, none of six prespecified comparisons shows a positive gain after Holm correction, and in Qwen3.5 a parameter-matched fixed-projection control lowers NLL by 0.00356 nats/token relative to the trajectory router.

Using paired enabled/bypassed executions of the final attention layer to produce signed next-token loss differences in frozen SmolLM3-3B-Base and Qwen3.5-4B-Base, the study tests whether hidden-state extrapolation error, curvature and error change improve attention routing beyond uncertainty, one-step displacement, position and state projections; on 100 held-out PG-19 books at an identical causal 20% invocation quota, none of six prespecified comparisons shows a positive gain after Holm correction, and in Qwen3.5 a parameter-matched fixed-projection control lowers NLL by 0.00356 nats/token relative to the trajectory router.