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arxiv: 2605.25328 · v1 · pith:I4HMQWPPnew · submitted 2026-05-25 · 💻 cs.CV · cs.MM

DIVA: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement

Renjie Lu , Xulong Zhang , Xiaoyang Qu , Shangfei Wang , Jianzong Wang This is my paper

Pith reviewed 2026-06-29 23:06 UTC · model grok-4.3

classification 💻 cs.CV cs.MM
keywords unified multimodal modelsrepresentation divergencemutual information estimationvisual understandingimage generationpost-trainingfactorizationinformation flow
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The pith

Unified multimodal models can convert representation divergence between generation and understanding into mutual gains by factorizing visual features into shared and unique parts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Unified multimodal models face interference because generation favors high-fidelity reconstructible features while understanding favors invariant semantic embeddings, and training both in one backbone causes mutual impairment. The paper reveals a complementary structure in the internal representations that arises from these distinct supervision signals. DIVA addresses this by factorizing visual representations into shared and unique components using two complementary information flows and mutual information estimation. This allows beneficial transfer between branches while protecting unique information from cross-interference. The approach yields consistent gains of 7.82 percent on understanding and 8.46 percent on generation tasks.

Core claim

Optimizing complementary but non-equivalent objectives within a monolithic backbone leads to mutual impairment instead of enhancement. DIVA transforms the representation divergence into interior synergy by explicitly factorizing the visual representation into shared and unique components based on two complementary information flows, enabling both the understanding and generation branches to achieve beneficial transferring while preserving the integrity of unique information from cross-flow interference via mutual information estimation.

What carries the argument

Explicit factorization of visual representation into shared and unique components based on two complementary information flows with mutual information estimation to prevent cross-flow interference.

Load-bearing premise

The root cause of performance issues is representation divergence induced by distinct supervision signals, and explicit factorization via mutual information estimation on complementary flows can separate shared and unique components without losing task-critical information.

What would settle it

Running the DIVA post-training procedure on a unified multimodal model and measuring no improvement or a decrease in both understanding accuracy and generation fidelity metrics would falsify the claim that the factorization produces beneficial transfer.

Figures

Figures reproduced from arXiv: 2605.25328 by Jianzong Wang, Renjie Lu, Shangfei Wang, Xiaoyang Qu, Xulong Zhang.

Figure 1
Figure 1. Figure 1: Illustration of the gap and base for synergy within UMMs. While the conflict induced by inductive biases from understanding and generation exists, the information flows constructed from same image-text pairs share the semantic anchor, providing the basis for transforming the conflict into mutual reinforcement. and image generation with a unified architecture (Team, 2024; Pan et al., 2025a; Ge et al., 2024;… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of the representation divergence and synergy. (a) shows the severe conflicts occurs in the shallow and deep layers while the mitigation is observed in the middle layers. Meantime, based on the two information flows that are described in Sec. 3.1, the effective rank between different flows increases in the middle layers and decrease again in the deep layers as presented in (b). And we conduct … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the self-improved mutual reinforcement (DIVA) pipeline. We propose a post-training paradigm that explicitly align the shared information, while preserve the integrity of unique information between the understanding and generation flows. Both flows are constructed base on the same sample pair to ensure the shared anchor. analysis in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results on image generation. We use Nexus-Gen as baseline for comparsion. It can be observed that after post-train with DIVA, the model’ s ability of handling the complex attribute, spatial layouts and multiple objectives has significant improved [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The t-SNE visualization of our extracted shared factors. Points of different colors indicate different samples. "Und" im￾plies the shared information from understanding flows and "Gen" represents the shared factors from generation. Image Editing. In addition to Bagel, we conduct experi￾ments on AnyEdit (Yu et al., 2025), UltraEdit (Zhao et al., 2024b) and FLUX.1-Kontext (Labs et al., 2025). As shown in [P… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of breakthrough of capability between understanding and generation branches under unified training. Mask Patterns. The results in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Image Generation results. The generating process encompasses multiple dimensions, including world knowledge acquisition, multi-objective scenarios, complex attribute control, spatial layout, and counterfactual generation. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
read the original abstract

Unified Multimodal models (UMMs) built on a single architecture have shown impressive performance in both understanding and generation. We identify a fundamental challenge that lies in inductive biases induced by distinct supervision signals: generation branch prefers high-fidelity, fine-grained representations capable of reconstruction, while the understanding favours semantically discriminative embeddings that remain invariant to task-irrelevant factors. Consequently, optimizing these complementary but non-equivalent objectives within a monolithic backbone leads to mutual impairment instead of enhancement. In this paper, we first analyze the root cause of this interference in unified backbones and reveal a complementary structure in their internal representations. Motivated by the observation, we propose DIVA, a self-improved post-training framework that transforms the representation divergence into interior synergy. By explicitly factorizing the visual representation into shared and unique components based on two complementary information flow, DIVA enables both the understanding and generation branches to achieve beneficial transferring while preserving the integrity of unique information from cross-flow interference via mutual information estimation. Despite its generality, our method consistently achieves improvements across visual understanding (+7.82%) and generation (+8.46%). The official code is available at: https://github.com/Jayyy-H/DIVA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper identifies a representation divergence in unified multimodal models (UMMs) arising from distinct supervision signals: generation favors high-fidelity fine-grained features while understanding favors semantically invariant embeddings. It proposes DIVA, a post-training framework that explicitly factorizes visual representations into shared and unique components via complementary information flows and mutual information estimation, enabling beneficial transfer between understanding and generation branches while blocking cross-flow interference. Reported gains are +7.82% on visual understanding and +8.46% on generation tasks.

Significance. If the MI-based factorization cleanly separates shared and unique components without bias, variance, or information loss in high-dimensional embeddings, the approach would convert an identified source of mutual impairment into interior synergy and offer a general post-training recipe for UMMs. The claimed generality and consistent gains across tasks would be notable if substantiated by controlled ablations and comparisons.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'explicit factorization ... via mutual information estimation' separates shared/unique components while 'preserving the integrity of unique information from cross-flow interference' is stated without any equation, estimator definition, or loss formulation; standard MI estimators are known to be biased or high-variance in high-dimensional visual features, so the claimed beneficial transferring does not automatically follow.
  2. [Abstract] Abstract: the motivation rests on the untested assertion that 'optimizing these complementary but non-equivalent objectives within a monolithic backbone leads to mutual impairment'; no quantitative evidence (e.g., representation similarity metrics, gradient conflict measures, or ablation removing one branch) is supplied to establish that the observed divergence is the root cause rather than a symptom.
minor comments (2)
  1. [Abstract] Abstract: headline percentages (+7.82%, +8.46%) are given without reference to the exact baselines, datasets, or evaluation protocols, preventing assessment of effect size or comparability.
  2. [Abstract] Abstract: the statement 'the official code is available at https://github.com/Jayyy-H/DIVA' should be accompanied by a reproducibility checklist or commit hash in the manuscript body.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below, clarifying how the manuscript handles the technical details and evidence.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'explicit factorization ... via mutual information estimation' separates shared/unique components while 'preserving the integrity of unique information from cross-flow interference' is stated without any equation, estimator definition, or loss formulation; standard MI estimators are known to be biased or high-variance in high-dimensional visual features, so the claimed beneficial transferring does not automatically follow.

    Authors: We agree the abstract omits equations and estimator specifics due to length constraints. The full manuscript details the MI estimator (a regularized neural estimator designed to control bias/variance in high-dimensional features), the complementary flow definitions, and the full loss in Section 3 with supporting equations. Ablations in the experiments confirm stable factorization and transfer gains without notable information loss. We will revise the abstract to reference Section 3 explicitly. revision: partial

  2. Referee: [Abstract] Abstract: the motivation rests on the untested assertion that 'optimizing these complementary but non-equivalent objectives within a monolithic backbone leads to mutual impairment'; no quantitative evidence (e.g., representation similarity metrics, gradient conflict measures, or ablation removing one branch) is supplied to establish that the observed divergence is the root cause rather than a symptom.

    Authors: The manuscript provides this analysis immediately after the abstract, including quantitative representation similarity metrics (e.g., cosine similarity across branches), gradient conflict measurements, and branch-removal ablations that isolate the impairment effect and confirm it as the root cause rather than a symptom. These appear in the motivation and preliminary analysis sections. No revision is required on this point. revision: no

Circularity Check

0 steps flagged

No circularity: method is a proposed post-training factorization using MI estimation

full rationale

The provided abstract and description introduce DIVA as a new framework that factorizes representations via mutual information estimation on complementary flows, motivated by an observed divergence. No equations, fitted parameters renamed as predictions, or self-citation chains are present that reduce the claimed gains or factorization to the inputs by construction. The derivation chain consists of analysis followed by an introduced technique whose validity rests on external empirical results rather than definitional equivalence or load-bearing self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method relies on mutual information estimation and factorization whose implementation details are absent.

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discussion (0)

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Reference graph

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    Related work

    12 DIV A: Harnessing the Representation Divergence in Unified Multimodal Models for Mutual Reinforcement A. Related work. A.1. Unified Multimodal Models (UMMs) Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal understanding and reasoning, enabled by combining Large Language Models (LLMs) with powerful visual encoders (Liu e...

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    select discretize visual data into a sequence of tokens, and then jointly model it with text in the same Transformer. Despite its simple architecture, experiments (Zhang et al., 2025; Deng et al.,

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