Energy Reinforced Diffusion for Structured Representation from Heterogeneous Spectral Observations

Heterogeneous spectral observations provide high dimensional molecular signals for knowledge discovery and structured evidence learning. However, global representations often obscure informative relationships among local peak groups, while insufficient structural guidance makes these dependencies difficult to preserve during evidence exploration and policy optimization. Consequently, learned representations may rely on isolated peaks or background patterns, rather than informative group dependencies with consistent structural semantics. To this end, we propose Knowledge-Energy Reinforced Diffusion (KERD) for structured evidence learning from heterogeneous data. KERD organizes peak clusters into a Peak Graph and learns structured relations among band groups through data driven consistency modeling. The learned knowledge energy guides diffusion sampling toward band group combinations with stable structural semantics. Next, we construct class conditional spectral prototypes from peak cluster relations to improve the stability of policy learning. When paired cohort variables are available, KERD optionally refines the structured state through a shared relation graph that strengthens the alignment between peak substructures and auxiliary factor groups. Finally, we evaluate KERD on more than 70,000 measurements from multicenter cohorts. KERD achieves consistent gains across the evaluated heterogeneous datasets covering different sources and biological sample types.