Beyond Feature Modulation: Physics-Conditioned State Evolution for Hyperspectral Compressive Imaging

Physical acquisition information provides valuable guidance for reconstructing hyperspectral images from compressed measurements. Existing methods have incorporated acquisition information through mask-guided feature modulation or adaptive optimization, yet such strategies mainly affect current representations rather than explicitly governing state evolution that carries accumulated information into subsequent inference. Motivated by this, we propose Physics-Conditioned State Evolution ($\Phi$2State), a framework that uses state space models to extend physical guidance from feature modulation to state evolution. Specifically, we design a deep unfolding network that couples explicit physical modeling and implicit prior inference through a shared effective coding operator. For explicit physical modeling, a bounded correction of the nominal mask yields an effective operator that guides data-consistency updates. For implicit representation, the same corrected mask conditions selective state space models within the proximal network to regulate information injection, retention, and readout throughout the entire state evolution process. The evolving states enable acquisition-dependent information to be progressively accumulated, updated, and reused during proximal inference. Extensive experiments on hyperspectral compressive reconstruction with varying spectral dimensionalities demonstrate the effectiveness of the proposed framework. Ablation studies and state evolution analyses further validate the proposed guidance mechanism and characterize its role in shaping inference dynamics.