Does Gradient Conflict Predict the Understanding–Generation Trade-off? A Controlled Audit of Conflict-Metric Validity in Unified Multimodal Models

Unified multimodal models (UMMs) are increasingly designed and optimized around the notion of gradient conflict between the understanding and generation objectives, measured as cosine similarities between task gradients or conflict rates across layers. 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 the structural ingredients of UMM training—a fully shared trunk serving two objectives with asymmetric token budgets and task difficulty—while making the ground-truth trade-off exactly computable. Across 63 configurations (7 gradient-combination strategies × 3 data-mixing ratios × 3 seeds) and 372 measured checkpoints, no directional conflict metric—global or per-layer cosine, conflict rate, removed energy—reaches |ρ| ≥ 0.3 with a confidence interval excluding zero for conflict measured during training against the eventual trade-off (strongest |ρ| = 0.145, interval crossing zero), and the larger concurrent configuration-level associations are mutually sign-inconsistent. A dose–response intervention that monotonically suppresses conflict (level α) leaves the trade-off flat, separating correlation from causation. The one gradient-geometric quantity with association, the norm ratio, is a generation-failure detector, null among configurations that master generation. The functional interference measure eff_rank leads every directional conflict metric (Δρ CI 0.729–1.041) and retains its advantage inside that regime. Training loss, the outcome most often reported in place of benchmarks, tracks the tradeoff strongly—the failure is specific to gradient-conflict geometry. 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.