Unified multimodal models (UMMs) are increasingly designed around gradient conflict between understanding and generation objectives.
The premise that reducing these metrics improves the downstream understanding-generation trade-off has never been tested directly.
We audit it in a controlled testbed, GRIDUMM, which mirrors key structural ingredients of UMM training while making the ground-truth trade-off exactly computable.
Across 63 configurations and 372 measured checkpoints, no directional conflict metric reaches an absolute Spearman correlation of 0.3 with a confidence interval excluding zero for conflict measured during training against the eventual trade-off.
A dose-response intervention that monotonically suppresses conflict leaves the trade-off flat, separating correlation from causation.
The norm ratio is a generation-failure detector and becomes null among configurations that master generation.
Functional interference measures outperform directional conflict metrics, while training loss tracks the trade-off strongly.
Our results do not show that conflict is useless; they show that its validity as a diagnostic target must be established, not assumed, and we release the audit protocol as a reusable standard.