arXiv — Machine Learning · · 3 min read

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

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Computer Science > Machine Learning

arXiv:2609.38465 (cs)
[Submitted on 29 Sep 2026]

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

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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.
Comments: 23 pages, 10 figures, 5 tables. Code will be made publicly available upon acceptance
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.38465 [cs.LG]
  (or arXiv:2609.38465v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.38465
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shuyang Jiang [view email]
[v1] Tue, 29 Sep 2026 19:57:47 UTC (2,589 KB)
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