mlsanityData to 7 Oct 2026

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

arXiv 2609.38465 ↗First posted 29 Sep 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 51 references that are in the map. First posted on arXiv 29 Sep 2026.

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References · 35

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23 more
  1. 2025Show-o2: Improved Native Unified Multimodal Modelscited by 242
  2. 2022Multi-Task Learning as a Bargaining Gamecited by 209
  3. 2024MetaMorph: Multimodal Understanding and Generation via Instruction Tuningcited by 205
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  19. 2026Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Modelscited by 3
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  23. 2026Uncovering Understanding-Generation Synergy in Native Unified Multimodal Models: From Representation, Task to Systemcited by 2

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