Temporal Abstraction and Precision Control through Latent Dynamics

Temporal abstraction is usually modeled with explicit options, skills, or hierarchical policies. We ask whether option-like temporal abstraction can instead be realized by recurrent latent dynamics without explicit option identities, option-specific policies, or learned termination functions. We study an input-driven recurrent action-value model in which normalized task inputs drive a shared latent dynamical system with learned timescales, while a prediction-error correction pathway provides a learned, context-dependent route for residual signals to update the latent state. We define option-like latent dynamics by operational criteria: persistence beyond one primitive step, behavioral influence on action values, input-dependent initiation, modulation or interruption, reuse, and absence of explicit option labels. In a controlled Four Rooms navigation setting, we test these criteria using impulse responses, latent perturbations, pathway-forcing analyses, precision-demand manipulations, and canonical-frame reuse under known $C_4$ symmetry. The central test is whether effective duration and correction change when temporal cost or observation reliability changes. This framing treats temporal abstraction as a measurable property of latent dynamics rather than as a discrete architectural object. The resulting evidence supports a scoped mechanistic account: recurrent systems can initiate, sustain, correct, and reuse option-like latent dynamics without selecting from an explicit library of options.