From Multimodal Indicators to Intervention Eligibility: Testing the Assumptions Behind Adaptive Intervention

Multimodal inference increasingly makes per-input decisions about which sensors, modality subsets, or experts to execute. Yet when adaptation underperforms, aggregate accuracy or routing gain does not reveal whether the problem lies in the action space, intervention usefulness, or the evidence used for selection. We introduce Perception Harness, a diagnostic framework that evaluates executable alternatives before controller development. With actions, fallback, evidence, utility, and harm conditions held fixed, it separates action identity, recoverability, and pre-action actionability. Three benchmarks reveal distinct development needs. On MMAct, degradation is detectable but masking is harmful on average, motivating fallback retention or masking redesign. On MCubeS, identical modality labels yield different learned action landscapes, calling for model-specific action-space profiling. On DeLiVER, substantial safe opportunity exists within frozen proposals, but admissible pre-action evidence exposes little of it, motivating evidence and proposal redesign. Locating the failed transition directs the next development step. The diagnosis motivates redesign or eligibility for separate controller validation. Eligibility is not deployment authorization.