The noncomputability of immune reaction complexity: Algorithmic information gaps under effective constraints

Adaptive immune responses must turn variable antigenic contexts into valid molecular actions under constraints of recognition, specificity, tolerance, and execution. We study the informational difficulty of this task by representing an instance x together with candidate responses r, accepted when a validity predicate V(x,r) holds. A fixed input-blind executor receives only a self-delimiting advice string and outputs a candidate response. The minimum feasible realizer complexity M(x)=min_r:V(x,r)=1K(r) measures the shortest description of any response valid for x. We define the Normalized Advice Quantile (NAQ) as the percentile rank of M(x) within a reference pool, giving a scale-free measure of relative response difficulty. The Exact Realizer Identity shows that the minimum advice needed by an input-blind executor equals M(x) up to an additive constant. Feature maps split this burden into a description term and a selection term, separating the cost of specifying a response class from the cost of finding a concrete realization. We derive computability separations, resource-bounded variants, and an operational converse linking high-NAQ mass to advice and memory requirements. Finally, empirical NAQ may be approximated through calibrated code-length or compressor-based proxies, with sampling error controlled by the Dvoretzky-Kiefer-Wolfowitz inequality.