When Is Component Sensitivity Compositional? A Causal Study of Neural Resource Allocation

Neural resource-allocation methods commonly assign each component a scalar sensitivity and rank components independently. This raises two distinct questions: whether the score tracks singleton utility, and whether ranking by singleton utility remains useful under joint allocation. We separate these two questions and test them through direct interventions under a common, logged protocol across quantization, layer skipping, structured pruning, mixture-of-experts selection, and adapter placement. The resulting behavior forms a spectrum. Layer skipping exhibits small median pairwise interactions, yet one-shot singleton selection can still incur substantial held-out NLL loss at the allocation budget; pruning rankings depend on background sparsity; GPTQ-based group refinement and adapter placement show strong non-additivity; and expert interactions vary across the tested depths. Large additive prediction errors need not imply large selection gaps: the six-layer adapter placement realizes only 15% of its singleton-sum NLL prediction yet trails the tested last-6 heuristic by only 0.011 nats. By contrast, on Qwen2.5-14B one-shot singleton selection at a 25% layer-skip budget incurs 1.013 nats of held-out damage versus 0.540 for sequential re-measurement, and under sparse pruning backgrounds the dense ranking transfers worse than background re-measurement in 16 of 18 conditions—separating additive prediction failure from realized allocation loss. The 14B layer-skip NLL advantage does not yield a statistically resolved accuracy advantage on ARC-Challenge or HellaSwag. As an exploratory diagnostic, residual-stream perturbation alignment is positively associated with pairwise interaction magnitude in 11 of 12 runs. We conclude that component sensitivity need not behave as an intrinsic scalar: it is a configuration-dependent marginal effect in several tested regimes, and allocation methods should validate compositionality at the deployment granularity, background, budget, and metric.