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

2026-09-29

AI Summary

This study addresses the lack of empirical validation regarding whether gradient conflict metrics can effectively predict the understanding-generation trade-off in unified multimodal models. To this end, we introduce GRIDUMM, a controllable testbed that disentangles correlation from causation through controlled benchmarks, combining large-scale configuration sweeps with dose-response intervention experiments for systematic auditing. Furthermore, this work proposes a novel perspective emphasizing functional interference over directional conflict. Our findings demonstrate that conventional conflict metrics exhibit weak predictive power. We release a reusable auditing protocol as a community standard and advocate for empirically grounded rather than assumption-driven evaluation of the diagnostic value of conflict metrics. Ultimately, this research establishes a new paradigm for optimizing unified multimodal architectures.

Abstract

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.

Problem

• Unified Multimodal Models
• Gradient Conflict
• Understanding-Generation Trade-off
• Conflict-Metric Validity

Innovation

• Gradient Conflict
• Unified Multimodal Models
• Controlled Audit
• Understanding-Generation Trade-off
• Functional Interference