Coverage ≠ Exposure: Auditable Control of Same-Support Tail Failures under Multimodal Missingness

摘要

Real-world multimodal systems inevitably face partial observability due to sensor dropout and degradation. Standard robustness methods can improve average performance, but they often remain unreliable in rare, adverse long-tail conditions. Under a locked same-support contract, we uncover a same-support tail failure where two runs with matched average performance still diverge on worst-case and shift-reweighted metrics computed on the same observable support. We attribute this instability to conditional interaction where environment coverage does not guarantee parameter exposure. Information is routed through different parameter groups, leaving some groups rarely updated even when environment coverage is complete, and tail-focused aggregation amplifies errors from these high-leverage but underexposed groups. This mismatch is auditable from gating logs, and we summarize it with TailPressure, an exposure-normalized statistic of tail-leveraged interaction. Guided by this diagnosis, we propose Heterogeneity-aware Closed-loop Exposure Stabilizer (H-CES), a controller that regulates per-group pressure via deterministic increment-branch gating and group-wise decoupled weight decay, without changing the task loss or adding inference branches. Experiments across diverse multimodal settings and backbones show that H-CES improves tail reliability under the same-support contract while preserving clean performance.