Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models
Authors: Jitai Hao
Hao Liu
Xinyan Xiao
Qiang Huang
Jun Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Multimodal Language Models
Primary area: foundation or frontier models, including LLMs
TL;DR: We found that during training, severe conflicts arise between the text and visual modalities in both the shallow and deep layers of UMMs. Our proposed Uni-X mitigates this issue and achieves strong performance.

Abstract: Unified Multimodal Models (UMMs) built on shared autoregressive (AR) transformers are attractive for their architectural simplicity. However, we identify a critical limitation: when trained on multimodal inputs, modality-shared transformers suffer from severe gradient conflicts between vision and text, particularly in shallow and deep layers. We trace this issue to the fundamentally different low-level statistical properties of images and text, while noting that conflicts diminish in middle layers where representations become more abstract and semantically aligned. 
To overcome this challenge, we propose Uni-X, a two-end-separated, middle-shared architecture. Uni-X dedicates its initial and final layers to modality-specific processing, while maintaining shared parameters in the middle layers for high-level semantic fusion. This X-shaped design not only eliminates gradient conflicts at both ends but also further alleviates residual conflicts in the shared layers.
Extensive experiments validate the effectiveness of Uni-X. Under identical training conditions, Uni-X achieves superior training efficiency compared to strong baselines. When scaled to 3B parameters with larger training data, Uni-X matches or surpasses 7B AR-based UMMs, achieving a GenEval score of 82 for image generation alongside strong performance in text and vision understanding tasks.
These results establish Uni-X as a parameter-efficient and scalable foundation for future unified multimodal modeling.
Our code is available at https://github.com/CURRENTF/Uni-X.

Published as a conference paper at ICLR 2026
UNI-X: MITIGATING MODALITY CONFLICT WITH A TWO-END-SEPARATTE ARCHITECTURE FOR UNIFIED MULTIMODAL MODELS
Jitai Hao
Hao Liu
Xinyan Xiao
Qiang Huang Jun Yu--r
School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)
Baidd Inc.
jitaihac&out outooouulook com, thuanggiang, yujun) (hjun)@hitteeee (liuhao24, xiaoxinyan @baidu com 'qqual contribution.
|Corresponding authors.
ABSTRACT
Unified Multimodal Models (UMMs) built on shared autoregressive (AR) trans- formers are attractive for their architectural simplicity. However, we identify a critical limitation: when trained on multimodal inputs, modality-shared transform- ers suffer from severe gradient conflicts between vision and text, particularly shallow and deep layers. We trace this issue to the fundamentally different low- level statistical properties of images and text, while noting that conflicts diminish in middle layers where representations become more abstract and semantically aligned. To overcome this challenge, we propose Uni-X, a two-end-separatee middle-shared architecture. Uni-X dedicates its initial and final layers to modality- specific processing, while maintaining shared parameters in the middle layers for high-level semantic fusion. This X-shaped design not only eliminates gra- dient conflicts at both ends but also further alleviates residual confficts in the shared layers. Extensive experiments validate the effectiveness of Uni-X. Under identical training conditions, Uni-X achieves superior training efficiency com- pared to strong baselines. When scaled to 3B parameters with larger training data, Uni-X matches surpasses 7B AR-based UMMs, achieving GenEval score of 82 for image generation alongside strong performance text and vision understanding tasks. These results establish Uni-X parameter-efficien and scalable foundation for future unified multimodal modeling. Our code available at mthttps:///ithhbt com/ COO//CURENTE /UniXX
INTRODUCTION
Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal understand- ing and reasoning, enabled by combining Large Language Models (LLMs) with powerful visual encoders (Liu et al., 2024c bj Wang et al. 2024b Team al. (024b). Motivated by this success, re- cent efforts to add image generation have led to Unified Multimodal Models (UMMs) (Team] 2025 Wang al. 2024c Wu al. 2024b). However, many advanced UMMs rely on increasingly complex system designs to boost performance, including the addition of semantic image encoders (Wu et al.
2024a Chen al. 2025b Deng et al. 2025; Wu et al. 2025a Lin al. 2025). ) the hybridization of autoregressive and diffusion paradigms (Wu et al.) 2025a ZZho et al. 2024 Ge et al. 2025 Deng ct al. 2025 Xie et al. 2025 Zhou et al. 2024), or the introduction task-specific branches and
experts (Deng et al. 2025 Liao al. 2025 Li ct al., (025d). While effective, this added complexity hinders scalability, limiting the degree of parameter sharing and reducing the potential for mutual benefits across tasks and modalities.
In contrast, autoregressive (AR) UMMS offer a simple yet powerful alternative. By treating visual inputs "foreign language" through vector quantization (VQ) (yan den Oord et al. 2018 Esser ct al., 2021b), they unify text and vision into a consistent token sequence, naturally extending the language-centric paradigm of L.MMs (Wu et al. 2025c Wang al. 2024c). Despite this simplicity,

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experiments reveal fundamental challenge: fully modality-shared transformers trained jointly on multimodal inputs exhibit severe gradient conflicts. Originally studied in multi-task learning Yu et al. 2020; Shi et al. 2023), we the first to transfer this concept to UMMs, uncovering inter-modality conflicts that hinder convergence and performance.
As illustrated in Figure T] these conflicts most pronounced in shallow (input) and deep (output) layers due differing text-image statis- tics. In contrast, the middle layers, where rep- resentations become increasingly abstract and semantic (Meng et al. 2022 Geva et al. (2021b;
Shared-Tranffome Transformer
Uni-X (Ours)
Gradient Conflict
0.4
Sun et al. 2025), show reduced conflicts and stronger cross-modal alignment. This suggests that an effective UMM should respect modality-
0.2
specific differences rather than enforcing uni- form parameter sharing across all layers.
0.0
Guided by this observation, we introduce Uni-X. a le--end-pppaaaee middle-shared architecture for unified multimodal modeling. In Uni-X, the shallow and deep layers are modality-specifet enabling specialized processing of distinet low- level distributions in text and vision, while the middle layers are shared to capture high-level semantic abstractions common to both. This X-shaped architecture not only mitigates the severe gradient conflicts at the two ends but also further alleviates residual conflicts in the shared middle layers by leveraging natural semantic alignment between modalities (Figure T..
10
20
Layer Index
Figure 1: Gradient conficc analysis of down- projection weights the FFN of modality-shared transformer. The shared transformer exhibits se- vere conflicts in shallow and deep layers, with only partial mitigation in intermediate layers. In con- trast, Uni-X avoids conflicts at both extremes and further alleviates them in the middle layers.
To demonstrate the effectiveness of Uni-X, we conduct extensive experiments under controlled training budgets and scaling regimes. Results show that Uni-X improves training efficiency and achieves stronger performance under identical conditions. Moreover, with larger data and model scales, 3B 3B-parameter Uni-X matches surpasses the performance of existing 7B AR-based UMMs across both understanding and generation benchmarks, demonstrating its scalability and competitiveness. Our contributions threefold:
Empirical Analysis: We identify and quantify gradient conflicts between text and vision modal- ities in the shallow and deep layers of shared autoregressive transformers, attributing them to fundamental differences in their low-level statistical properties.
We propose Uni-X, novel wwo--nd-separatee middle- middle-shared architecture that aligns model structure with modality characteristics by using modality specific layers for low-level processing and shared core for high-level semantic fusion.
Model Design:
Comprehensive Validation: Extensive experiments demonstrate that Uni-X improves training efficiency and scales effectively, enabling 3B model to achieve performance competitive with much larger 7B models across diverse multimodal benchmarks.
RELATED WORK
Visual Language Models (VLMs). The remarkable progress of LLMs (Touvron et al.) 2023 Yang
et al. 2024 Brown et al. 2020) has motivated researchers to extend them with visual cognition, giving rise to VL.Ms (Liu et al. 2024c] Achiam et al., 2023). Most VL.Ms leverage pre-trained visual encoders such CLIP (Radford et al. [222 or LiP (Tschannen et al.) 2025) extract semantic features from images, which are projected into the LLM's semantic space via multimodal adapters (Liu et al. 2024c Beyer et al. 2024 Team al. 2024a Li al. 2025a). This design enables strong multimodal understanding and reasoning but remains asymmetric: VLMs treat images only inputs and cannot generate them, limiting synergy between perception and synthesis.
Unified Multimodal Models (UMMs). To enable such synergy, recent efforts have shifted toward UMMs, which aim to support both understanding and generation within single framework (Team)
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Text
Image
Tofal Loss
Loss
8
9 (b) Sum of Losses
9
(a) Modality Objectives
(c) Optimization Trajectory
Illustration of gradient conflict. The loss landscapes of different modalities exhibit distinct geometries, creating potential conflicts in optimization direction. (b) The optimum of the sum of losses different from the optimum of any single modality's loss. (c) the presence of gradient conflict, the optimization trajectory becomes oscillating and suffers from slow convergence.
Figure 2:
2025 Jin et al., 2024 Wu et al.) 2024b). A natural extension is to adopt the autoregressive (AR) paradige gm of LLMs by treating visual tokens as a "foreign language" via vector quantization (Wu al. 2025c Wang et al. 2024c). However, the distinct statistical properties of text and images often lead to modality conflicts, degrading performance in shared transformers (Team) 2025).
To mitigate this, several approaches increase architectural complexity: Mixture-ou Tf-araaffoorrrrr
(MoT) designs (Liao et al. 2025 Deng et al. 2025; Shi et al. 2025a) separate understanding and
generation with distinct branches; Hybrid AR difffffuioon frameworks (Zhao et al.] 2024 Wu et al.)
2025a Dong et al. 2023) Ge et al. 2025) combine next-token prediction with diffusion based image synthesis; and branching strategies such UniFork (Li al. (025d) add task-specific deep heads. While effective on benchmarks, these methods sacrifice parameter sharing, complicate training, and weaken cross-modar benefits-th very goals UMMs were meant to unify.
Comparison with Uni-X. Uni-X builds on these insights but takes a different path. Instead of adding modules, Uni-X retains the simplicity of pure AR UMMs while mitigating modality conflict through two-end-separated middle-shared architecture: shallow and deep layers use modality parameters process low-level statistical differences, while intermediate layers are shared exploit high-level semantic alignment. This X-shaped design avoids the rigidity of MoT UniForrk and the complexity of AR-diffusion hybrids, offering lightweight yet effective solution. We further discuss other related works in Appendix A.T
3 UNI-X
3.1
OBSERVATIONS
Before introducing the Uni-X architecture, we first analyze the gradient conflicts that arise when training modality-shared autoregressive transformers multimodal data. We also provide information-theorent perspective to explain why such conflicts emerge.
Definition of Gradient Conflict. As illustrated in Figure 2 gradient conflict occurs when different optimization objectives induce gradients pointing in divergent directions, making joint optimization unstable and inefficient. To quantify gradient conflict in multimodal training, we checkpoint of fully shared transformer. From this model checkpoint, we compute the average gradients for specific parameter groups (c.g.. FFN down-projection weights) using over 60 mini- batches and totaling 2M tokens, to ensure obtaining stable gradients.
early
Specifically, we first compute the average text gradient, gtext. This is obtained by exclusively performing forward and backward passes on Drext, a text-only subset filtered from the pre-training data. Next, we compute the average image-text gradient, g mgg using an analogous subset D Dimg containing image-text pairs. The raw inter-modal similarity is then measured as the cosine similarity between these two average gradients:
Sinter cos(groxxt 91mg).
(1)
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However, since transformer layers have distinct roles at depth (Sun et al. 2025 Geva al. 202 2021a),
the resulting raw similarity Sinter is biased and cannot be directly compared. To correct for this, estimate baseline similarity Sease that reflects the model's 's intrinsic gradient consistency unified data distribution. We randomly shuffle the full multimodal dataset Da11 and split it into two disjoint halves, D1 Pay and D2 any Their respective average gradients, Jany and Jany yield:
Sease cos ganys 2any)
This value represents the expected gradient similarity when gradients originate from the same underlying distribution. Thus, we define the gradient conflict Cy as the deviation from this baseline:
Cg = -- - -Saase)
high Sease indicates the model's gradients are stable, whereas a much lower Sinter suggests that the text-only and image-text data push the shared model parameters in conflicting directions, resulting in large positive Cg. This provides principled, layer-wise measure of inter-modal disagreement and reveals where and why conflicts are most severe.
Empirical Findings. Figure I shows gradient conflict profiles (Cy) across depth. In modality- transformers, conflicts are most pronounced in shallow layers (near input) and deep layers (near output). while intermediate layers exhibit weaker conflicts. Experiments further reveal that Uni-X avoids conflicts at both extremes and reduces residual conflicts in the middle, validating its structural design. Additional analyses of other modules and the relationship between gradient conflict and data, as well as its impact on model performance, are provided in Appendix A..5
Why Do Conflicts Arise? Vision as a "Foreign Language" To explain these observations, exam- ine whether vision behaves like "foreign language" when tokenized. Using the VQ tokenizer (Team) 2025), images represented as discrete token sequences, formally similar to text. Then, we define conditional entropy based on n-gram. When n 1, the calculation reduces to ordinary information entropy. For > the conditional entropy is computed as follows:
Hn Σ p(w, W1, W2.-"" wn-1) log p( Ww,
W1. W22 10 Wn-1). 1)).
Results (Figure 3) show that image tokens ex-
hibit far higher entropy than natural languages such as English, German, or Chinese. While languages differ in grammar and lexicon, their token statistics remain closer to each other than to images. This means visual sequences are in- herently harder to predict, requiring modeling of long-range, spatially entangled dependencies.
English
Conditional Entropy (bits)
10
German
Chinese
Image
As information theory suggests, sequences with higher (conditional) entropy are inherently harder predict, requiring models to learn longer-range dependencies and more complex patterns. Thus, when shared transformer jointly processes low-entropy, grammatical text with high-entropy, spatially complex vision, shallow and deep layers are forced to recon- cile conflicting low-level distributions, produc- ing strong gradient conflicts. In contrast, inter- mediate layers, where representations become more abstract and semantic, naturally align across modalities, explaining the reduced conflicts observed in practice.
2
1.5
2.5
3.0
3.5
N-gram (n)
Figure 3: Conditional entropy of images and nat- ural languages. Image token sequences encoded by the VQ tokenizer exhibit substantially higher entropy, indicating greater difficulty in prediction.
3.2 MODEL ARCHITECTURE
Motivated by these findings, we propose Uni-X, an architecture designed to explicitly align model structure with modality characteristics.
Core Principle. As illustrated in Figure Uni-X follows a two-end-separated, middle-shared design. The shallow and deep layers are duplicated into parallel modality-specific branches, ensuring
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Input Tokenization
Tokens
Vision Tokens
A beautiful ful and lovely owl...
Vision
Text Tokenizer
Tokenizer
Grad conflict due diserepancy modal properties
Separate
T-Layer
V-Layer
Shared Transformer
Grad conflict weakened due more semantic representations
Shared-L.ayer
Share
Grad conflict due to discrepancy modal properties
Separate
T-Layer
V-Layer
Figure 4: Illustration of the proposed Uni-X architecture compared with standard modality- transformer. The baseline shared transformer (left) encounters gradient conflicts in shallow and deep layers due to the mismatched statistical properties of vision and text tokens. In contrast, Uni-X (right) adopts two-end-separated, middle-shared design: modality-specifie layers both ends handle low-level feature processing, while shared central block performs high-level semantic fusion. This structure aligns the architecture with the inherent characteristics of each modality and effectively mitigates gradient conflicts.
independent handling of text and vision during early feature extraction and final token projection. The intermediate layers remain shared, enabling high-level semantic fusion across modalities. This X-shaped separation-ani aaddanaariii balances modality specialization with semantic alignment.
Input Tokenization. For visual inputs, we employ the VQGAN tokenizer (Esser et al., 202 la) from Chameleon TTam 2025) encode 512 x 512 images into a 32 x grid of visual tokens from an 8, 192yentry codebook. To accommodate these new tokens, we expand the vocabulary and corresponding embedding matrix of the base LLM. Textual inputs are processed via the standard BPE tokenizer. The final token sequence is structured as <BOI> [mage] <BOI> [Text] <BOS> for image understanding tasks and (Text] <BOI> [Image] <EOI><BOS> for image generation tasks. This unified tokenization enables AR training across modalities. While Uni-X can handle interleaved multimodal sequences, this study focuses on non-interleaved inputs.
Forward Propagation. Given a pre-trained LLM with L layers, denoted as (Layer? 1-11 - we partition them into three sections: The initial N layers and the final M layers constitute the "separated layers," while the intermediate layers form the "shared layers." Within the separated blocks, we introduce new set of vision-specific layers, (Layer(), which operate in parallel with the original text layers, {Layer]].
To manage the data flow, introduce a binary mask M, E {0, 1)" identify the positions of visual tokens. At any given layer 1, the complete hidden states H' can be partitioned into text-specific states H H' M.] and vision-specific states H [M,)).
The forward propagation Uni-X is defined as follows:
Layer (H)) [ay))r((H)]
< L M,
(5)
otherwise,
where x E {t.v) denotes the modality (text or vision). In the "otherwise" case, [ indicates selecting the subset of the output hidden states corresponding modality
Importantly, unlike other architectures (Li et al. 2025d Deng et al. 2025 Shi et al. 2025a), the vision and text modalities remain strictly isolated within the separated blocks, with cross-modal

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Published as a conference paper at ICLR 2026
interaction. This forces the model to learn robust unimodal representations before they are fused in the shared block and after they are separated for modality output generation.
Training Objective. Following the standard paradigm for AR models, Uni-X is trained to predict the next token a sequence containing both text and visual tokens. The training objective is to minimize the cross-entropy loss over the vocabulary for each token. The loss function L for given sequence S = ($1, 82,
,T) (T) is defined as:
T --- log P(s; s21).
(6)
This simple yet effective objective enables the model to learn both understanding and generation capabilities across modalities within a single, unified framework.
Design Rationale. Unlike prior architectures that rely auxiliary semantic encoders (Wu et al.
2024a [Chn et al.] 2025b), hybrid AR diffiffusoon pipelines (Wu al., 2025a Zhao et al. 2024), or
task-specific branching structures such MoT (Liao al. 2025) and UniFork (Li et al. (025d),
Uni-X maintains the simplicity of a pure autoregressive framework. Its poo-end-sepprated middle- shared structure is motivated directly by empirical evidence of gradient conflicts, aligning the model design with the statistical characteristics of each modality. By isolating low-level modality-specifff processing while preserving shared semantic core, Uni-X avoids the complexity and overhead of multi-expert or dual-paradigm systems, yet achieves competitive or superior performance. This balance of architectural simplicity, empirical grounding, and scalability makes Uni-X practical foundation for unified multimodal modeling.
4 EXPERIMENTS
We evaluate Uni-X from two complementary perspectives: (1) Efficiency under identical training conditions, where Uni-X and baseline architectures are trained on the same data and resources, enabling fair comparisons of efficiency and performance. (2) Scaling within resource constraints, where we maximize dataset size and training duration to examine Uni-X's s scalability and competi- tiveness against larger state-of-the-art models.
4.1 EXPERIMENTAL SETUP
Pre-training Datasets. Our pre-training stage was designed to build strong foundation in both language and vision. To preserve general text generation capabilities, we utilized diverse set of text corpora: the high-quality Chinese dataset CCI3-H (Wang et al., (024a), English datasets DCLM (Li et al. 2025c) and Fineweb-Edu (Penedo et al. 2024), and the StarcoderData (Li et al. 2023b) corpus, as integrating code is known boost general model performance (MA et al.) 2024).
For multimodal pre- trainigin we used public benchmarks like ImageNet (Russakovsky et al. 2015)
and JourneyDB (Pan et al. 2023), complemented by substantial internally collected dataset of 40 million images, which were captioned using the powerful Intern-VI. VL. model Chen et al. (0024).
Following the methodology of Liquid (Wu et al. (025c),, diversified our training data by randomly reversing 20% of the text-to-image pairs to serve as image-captioning tasks. The final pre-training data consists of 72B text tokens and 65B vision tokens.
Supervised Fine-Tuning (SFT). We further refined the model with 3B SFT tokens. For vision understanding, we employed MiniGemini (Li et al. 2024a) and FineVision (HuggingFaceM4] 2025). To improve text understanding and general instruction-followe
we utilized OpenOrca (Mukherjee ct al. 2023). Additionally, to refine the quality of image generation, we leveraged Blip3o-60k (Chen et al. 2025a) and ShareGPT4o (Chen et al. 2023).
Benchmarks. Evaluation covered text-only, image generation, and multimodal understanding tasks. For text-only tasks, we employed IRC-Easy/Challenas (ARC-E/ARC-C) (Clark et al.) 2018). Wino- Grande (WinoG) (Sakaguchi et al. 2020). BoolQ (Clark et al. 2019), and MMLU (Hendrycks et al. used GenEval (Ghosh et al. (023) and DPG-Bench (DPG) (Hu et al. 2024b. For the GenEval benchmark, we followed Bagel (Deng et al., 2025) and employed an LLM to rewrite shorter prompts into more detailed ones better assess instruction following. For
1021 For image generation,
multimodal understanding, we used SEEDBench (SEED) (Li al. 2023a). .MEE (Fu et al. 2024
POPE (Li et al. 2023c), and MMBench (MMB) (Liu et al. 2024d).

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Table 1:
The text performance of Uni-X compared other models.
Model
# Params.
ARC-E
ARC-C
WinoG
BoolQ
MMLU
Avg. +
Janus-Pro (Cen el al.
2025b
7B
70.4
40.9
66.1
80.2
49.3
61.4
VILA-U Wu et al. 2024b
7B
51.6
34.0
57.3
70.6
25.5
47.8
Chameleon [Team_ 2025
76.1
46.5
81.4
65.3
Liquid Wu al.
2025c
7B
75.6
49.0
72.7
81.0
56.0
66.9
Uni-X
3B /.5B
79.0
47.9
82.2
57.6
67.1
Table 2: The image generation and multimodal understanding performance of Uni-X compared to other models. In the # Params column x/y. I and represent the number of active parameters and the total parameters, respectively. represents the model yariant that performs semantic alignment. represents the rewriting of the prompt during evaluation. indicates that it has been trained on more image-text data.
Model
# Tokens
# Params.
GenEval
DPG
MME
POPE
MMR
SEED
Autoregressive meets Diffiasion
Bagel
Deng et al.
2025
5.1T
7B 14B
88
85.0
85.0
Bilp30
CChen
2025a
79.3
1,527.7
78.6
73.8
X-Omnl
Geng et al.
2025
-1T
10B / 20B
831
87.6
89.3
74.8
74.1
Show-o
Xic al.
2024
-500B
1.3B
68
1,097.2
80.0
Show-o
Xic 2024
-500B
1.3B
1.232.9
84.5
Autoregressive w/ Semantic Encoder
NextStep1
Teea al.
2025
-1T
14B
73
85.2
Janus-Pro
Chen el 20256
<300B
7B
80
84.1
87.4
79.2
72.1
VILA-U
Wu ct al.
20246
7B
1,336.2
83.9
56.3
Liquid
Wa et al.
2025c
8B
1,448.0
83.2
Autoregressive w/o Semantic Encoder
Chameleon (Temm 2025
9.2T
604.5
32.7
LWM Liu et al. 2024a
-500B
7B
47
75.2
EMU3 Wang al. 2024c
8B
66
80.6
1,243.8
85.2
58.5
68.2
Liquid
Wu al.
2025c
-90B
7B
68
79.8
1,107.2
81.1
Uni-X
140B
3B 4.5BB
82
79.8
1,158.3
83.6
59.3
60.2
Uni-X
240B
3B
4.5B
83
80.3
1,228.2
84.6
62.7
Implementation Details. We conducted ablation studies Qwen2.5-1.5B (Yang al. 2024) and scaled to Qwen2.5-3B. We used the VQGAN tokenizer (Esser et al. 2021a) from Chameleon (Team) 2025) encode 512 512 images into 32 32 discrete tokens. Our codebase is built upon the Liquid (Wu et al. (202cc) and HuggingFace Transformers (Wolf et al. 2019) libraries. Training was accelerated using Flash Attention (Dao et al., 2022) and DeepSpeed ZeRO2 (Aminabadi al.
2022 When generating images, we uniformly set the classifier-fre guidance (CFG) to 4.0.
4.2 RESULTS AND ANALYSIS
Scaling Experiment. this experiment, we aim demonstrate that Uni-X can scale effectively and is not limited to small-scale training data. We expand the dataset size to 140B total tokens for Uni-X, using Qwen2.5-3B as the base model, and extend the training duration achieve improved performance. The evaluation conducted against SOTA models, some of which have been trained on trillions tokens. As presented in Table I Uni-X robustly maintains the strong language capabilities of its base model. With average score of 67.1 across five text benchmarks, our 3B Uni-X model outperforms several larger 7B models. This demonstrates that our design successfully mitigates modality conflict without sacrificing performance fundamental language understanding tasks.
For image generation, as detailed in Table 2 Uni-X achieves a strong score of 82 on GenEval, result that surpasses many models with more parameters and underscores the effectiveness of
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Table 3: Image edit results on ImgEdit-Bench o denotes models trained on extra image-text data.
Model
Params,
Add
Adjust
Extract
Replace
Remove
Background
Style
Hy Hybrid
Action
Overall
GPT-4o
4.61
4.33
2.90
4.35
3.66
4.57
4.89
4.20
ICEdit
12B
3.39
1.73
2.93
3.08
3.05
AnyEdii
4B
3.18
2.95
1.88
2.47
2.23
2.24
2.85
1,56
2.65
2.45
UltraFdit
4B
3.44
2.81
2.13
2.96
1.45
2.83
3.76
1.91
2,98
2.70
X-eptX-Edlit
1.76
3.06
Bagel
7B 14B
3.56
3.31
1.70
3.30
2.62
3.24
4.49
2.38
4.17
3.20
Uni-X
3B
3.57
3.18
2.06
3.94
3.82
3.38
4.21
3.16
3.63
3.44
Table 4: Performance and training efficiency comparison of different model architectures under identical training conditions. Training efficiency is measured by the number of tokens processed per second per GPU. indicates that the baseline has been adapted for our experimental setting (sce Appendix A.3 for specific details); o represents calculating the loss on the instruction part during the
training image-text data.
Model
#Params.
MMLU
GenEval
MMB
Avg. t
Efficiency
Shared Transformer
1,5B
50,0
33.6
30.3
38.0
16,380
MoT
Deng et al. 2025
1,5B 3B
48.0
26.0
30.0
34.6
12,658
HardMoF.
1.5B / 2.3B
50.3
42.8
30.7
41.3
14,657
UniFork
[Li ct 2025d
1.5B 2.3B
50.1
12.4
25.9
29.5
15,481
Uni-X (9:5)
1.5B / 2.3B
48.5
34.8
29.8
37.7
15,642
Uni-X (9:5)
1.5B 2.3B
43.3
31.5
41.6
15,595
our architecture in producing high-quality images. We also tested T2I-CompBench (Huang al. 2025) and MSCOCO (Lin et al.) 2015), shown in Appendix A.4 Uni-X similarly exhibited strong performance with fewer parameters. Regarding vision understanding (Table[), while Uni-X's scores are slightly lower than some state-of-the -ftt---rt models, we observe clear trend: models that incorporate an additional semantic image encoder, such as Janus-Pro (Chen et al. (025bb and the semantically aligned variants of Liquid (Wu et al.) (025c) and Show-o (Xie et al. 2024). tend to achieve substantially higher performance on understanding benchmarks like MMBench and SEED.
In contrast, among models that do not rely on separate semantic encoder, Uni-X's performance is commendable and holds its ground against strong competitors like EMU3 (Wang et al., 2024c). This suggests that architecture effectively harnesses the inherent capabilities of the autoregressive framework for vision understanding. We speculate that the relatively weaker understanding perfor- mance might be partially caused by the insufficient utilization of the VQ tokenizer's codebook. We analyzed the token sequences encoded from million images and found that, although there are 8,192 tokens available the tokenizer, only 22,127 are being utilized. Meanwhile, EMU3 uses 4096 tokens to represent 512 x 512 image, which provides more fine- tne-grained information. However, this 4x token count severely impacts its image generation speed, shown in Appendix AA66]
For image editing, we conducted tests on ImgEdit (Ye et al. 2025) as shown in Table 3 Uni-X achieved better results than Bagel, even with less training data and fewer parameters than Bagel. This demonstrates that the high-level semantic unification of Uni-X enhances its image editing capabilities.
Identical Training Conditions. To validate the effectiveness of Uni-X, we conducted ablation experiments smaller dataset and slightly reduced base model Qwen2.5-1155h due to resource constraints. To ensure consistency performance comparisons, we limited the dataset to 28B tokens, of which 13.7B are vision tokens. The experiments were conducted using learning rate (LR) of x 10 warmup ratio 0.03, and constant LR scheduler, with batch size 17,560 tokens per GPU.
The selected baselines include: (1) Shared Transformer, which continues multimodal pre-training based on Qwen2.5- 1-55B (2) M (MoT) (Deng et al. 2025). where prior work replicates additional transformer handle image generation tasks, while the original LLM backbone focuses on text-only and image understanding tasks. Under our experimental setup, vision tokens are allocated to the duplicated transformer; (3) Hard-Route MoE (HardMoE), which
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Figure Qualitative examples Uni-X image generation. The results highlight its ability to produce diverse, high-quality visuals that follow prompts with both creativity and fne-grained detail.
introduces vision expert specifically for the vision modality, assigning vision tokens to this expert for computation guided by the vision mask; and (4) UniFork (Li et al.) (025d), which creates a task-specific deep branch for image generation. For all the baselines, ignored the instruction during training to enhance cross-modal performance and ensure a fair comparison.
Results (T)ble|A) show that Uni-X achieves the best overall performance under consistent training conditions, Specifically, our Uni-X (9:5) configuration attains an average score of 41.6, significantly outperforming the standard baselines. While HardMoE competitive, achieving a score of 4 41.3, Uni-X still holds a slight advantage. Moreover, HardMoE and UniFork are orthogonal and can be combined. In terms of training efficiency, although the baseline shared transformer is the fastest due to having the fewest parameters, Uni-X X achieves high throughput, which is considerably more efficient than the less performant MoT architecture. These findings confirm that Uni-X's design offers more effective trade-off between performance and computational efficiency.
worth noting that the architectures MoT and UniFork have been adapted fit our VQ+AR setup avoid discrepancies in efficiency between paradigms such diffusion and AR+diffusion. Specific details can be found in Appendix A.3 A comparison training efficiency across paradigms lies beyond the scope of this work and will be considered in future research.
Case Study. In Figure 5 we present curated selection of images gener- ated by Uni-X to qualitatively assess its capabilities. Despite the relatively limited number of tokens used dur- ing training, the model demonstrates a strong ability to produce clear, aes- thetically pleasing images that exhibit capabili- ties. The examples showcase Uni-X's versatility in handling wide range of creative and complex prompts. For instance, the model can generate imag- inative fantasy scenes, such as gigan- tic library floating above the clouds, and surreal compositions, like real- istic elephant walking on the ocean floor. Furthermore, Uni-X success- fully adheres to specific artistic style requests, as seen in the detailed anime-style portrait, and renders fine details with high fidelity, exemplified by the intricate feather patterns of the owl. These case studies verify that Uni-X can effectively translate complex textual descriptions into high-quality visual outputs. The specific prompts used for these generations are provided in Appendix A.2
Input 2 examples and target image
This is sunny day.
This day.
Output: This miny day.
robust
There hirds.
There
Output: dogs.
Figure 6: Demonstration of Uni-X's in-context learning. The model follows few-shot examples to perform tasks such as image description (Ist line) and object counting (2nd line).

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In-Context Learning. Although Uni-X was not explicitly trained interleaved multimodal data, conducted an evaluation to assess its emergent in-context learning (ICL) capabilities. As illustrated in Figure [6] the model was presented with few-shot examples, where several image-text pairs were provided as context before a final query image was presented without its corresponding description.
The results demonstrate that Uni-X successfully interpret the contextual examples and apply the learned pattern to the target image. For instance, in the top row of Figure O the model correctly identifies the weather in the target image as a "rainy day," adhering to the simple descriptive format ("This is a... day.") established by the preceding examples. Also, Uni-X exhibits the ability to perform more reasoning tasks, such as object counting. This suggests that the model is not merely mimicking sentence structure but is performing cross- moosl reasoning at a semantic level.
Ignore Instruction in Training. Ignoring the loss of the instruction part during training is a common technique in supervised fine-tuning. However, its role in pretraining is rarely emphasized. Following Liquid (Wu et al. (025c), we applied the same "ignore instruction" strategy during pretraining.
Specifically, no loss mask was applied for pure text data. For text-image pairs, in text-to-inne text-to-image tasks, the loss calculation excluded the text instruction tokens. Similarly, for image captioning tasks, the loss corresponding to the image tokens was masked. As demonstrated in our experimental results, T this approach significantly enhanced the model's capability to generate images.
We believe there might be several rea- sons for this: 1) This mask forces the model to learn the relationship between the two modalities rather than relying the prior distribu- tion of images, thereby enhancing its instruction-followwe
Table 5: Performance comparison of different Uni-X configu- rations. Here, x y denotes the number of shallow separated layers z and deep separated layers y, respectively. The total number of layers is 28. The split points are x and n y. respectively.
capability. 2) It serves as a form of loss regularization. For text-image pair data, the number of image tokens is fixed at 1024, while the average number of text tokens is around 120. By masking, we ensure that the gradient magnitude generated by the loss is only dependent on the reverse ratio we set.
Configuration
MMLU
GenEval
MMB
Avg.
Uni-X (3:3)
48.7
37.3
30.7
38.9
Uni-X (7:7)
49.6
41.3
29.4
40.1
Uni-X (11:11)
49.7
37.5
32.1
39.8
Uni-X (3:11)
50.0
32,9
31.0
38.0
Uni-X (5:9)
39.2
28.0
39.1
Uni-X (9:5)
50.1
43.3
31.5
41.6
Uni-X (11:3)
49.8
25.1
31.9
35.6
Number of Separated Layers. We investigate how the number and distribution of separated layers affect performance (Ta Varying the total number of separated layers produces an n-shaped trend: more separation improves modality-specific low-level processing. but too many layers reduce shared middle layers, weakening semantic fusion and cross- modal reasoning. The best overall performance is achieved with 14 separated layers.
We then examine shallow-deep ratios under this setting. A 9:5 split (slightly more shallow than deep layers) performs best, indicating that early processing of low-level features, where text and vision differ most, benefits more from modality-specific capacity than the final generation stage. These results provide strong empirical support for the Uni-X design. We also explored text layers and vision layers with different numbers of separate layers, and the results are shown in Appendix A.7
5 CONCLUSIONS
In this work, we identified gradient conflicts as a fundamental limitation of shared AR UMMs. particularly in the shallow and deep layers where vision and text exhibit highly divergent low- level statistics. To address this challenge, we proposed Uni-X, a poo-enddseppratted middle-shared architecture that explicitly aligns model structure with modality characteristics. By isolating low-level processing into modality-specific branches while maintaining a shared semantic core for high-level fusion, Uni-X effectively mitigates inter-modal conflicts without adding architectural complexity. Extensive experiments show that this X-shaped design allows 3B 3B-parameter Uni-X model to deliver performance competitive with much larger 7B UMMs across diverse multimodal benchmarks. These findings establish Uni-X as both a scalable and parameter- efficciet foundation, paving the way for future research in unified multimodal modeling.
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ACKNOWLEDGMENTS
We would like to thank the anonymous reviewers for their insightful feedback and constructive suggestions, which have significantly improved the quality of this paper. This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 62125201, U24B20174, and U25B6003.
ETHICS STATEMENT
This research aims to advance the field of artificial intelligence, particularly in the area of Unified Multimodal Models. We recognize that, like other powerful generative models, the technologies proposed in this study also carry potential risks of misuse, such as the creation of misinformation. biased, or harmful content. Our primary objective is to explore architectural efficiency to build more powerful and scalable models, and we believe this will make a valuable contribution to science.
The datasets used to train and fine-tune our models are primarily publicly available and widely used benchmark datasets in the academic community. For any internally collected data, we have ensured that its acquisition and processing adhere to principles filtered web-based datasets for bias, and therefore, the model may reflect social biases present in the data. We encourage responsible downstream use and further research into mitigating the potential negative impacts of generative models. Our work is intended solely for research purposes and is shared with the community to foster innovation and deepen understanding
responsibility. We have not specifically
REPRODUCIBILITY STATEMENT
We are committed to ensuring the full reproducibility of our work. To facilitate verification and future extensions, we provide detailed descriptions of the model architecture, training setup, datasets, evaluation protocols, and implementation details throughout the main paper and appendix.
Code. The complete source code for the Uni-X architecture, training pipeline, and evaluation scripts is publicly available at: https: h/gittub. com CURRENTF/ CUURRENTF/Uni- X The repository includes model definitions, data preprocessing utilities, configuration files, and instructions for reproducing both ablation and scaling experiments.
Architecture and Implementation. The description of the Uni-X architecture provided in Section 3.2. Implementation details, including the base models (Qwen2.5-1.5B and Qwen2.5-3B), the VQGAN tokenizer configuration, training framework (Liquid and HuggingFace Transformers), optimization tools (FlashAAtennionn 2 and DeepSpeed ZeRO2), and image generation settings (e.g., 4.0), are documented in Section 4.1 Adaptations made to baseline methods under the VQ+AR setting are fully described in Appendix A.3 ensure fair and transparent comparison.
Datasets and Evaluation Benchmarks. All pre-training datasets (including text and multimodal corpora) and supervised fine-tuning datasets are listed Section 4. along with token statistics. Evaluation benchmarks for text-only tasks, image generation, multimodal understanding, and image editing are also described in Section 4.1 and corresponding result tables. Where prompt rewriting or evaluation-specin adjustments were applied (e.g., GenEval prompt expansion), these procedures are explicitly documented.
Hyperparameters and Training Settings. Key training hyperparameters for ablation experiments, including learning rate, warmup ratio, batch size, and scheduler details, are specified Section 4.1 Scaling experiments and total token counts (c.g., 140B / 240B settings) are also clearly reported in Section 4.2 These details ensure that experimental comparisons are conducted under fully specified and reproducible conditions.
We believe that the combination of open-source code, precise architectural definitions, transparent dataset descriptions, and explicitly reported training hyperparameters enables the research community to faithfully reproduce our results and build upon the Uni-X framework for future unified multimodal modeling research.
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A APPENDIX
A.1
OTHER RELATED WORK
Other UMMs Recent advancements in Unified Multimodal Models (UMMs) have explored various architectural and methodological innovations. For instance, OneCAT (Li al. 2025b) proposes a decoder-only autoregressive architecture that utilizes modality-specine
oixture-of-Expert (MoE) structures to achieve efficient multimodal fusion without visual encoders. VARGPT (Zhuang et al.) 2025) unifies visual understanding and generation through seamless combination of next-token and next-scale prediction paradigms. To harmonize multi-granularity representations, the Harmon framework (Wu et al. 2025d) leverages shared masked autoregressive encoder. Furthermore, MetaQueries (Pan et al.
20025 introduces learnable queries to transfer reasoning capabilities from frozen MILLMs diffusion decoders.
UMMs for Efficient Agent The capabilities of lision-language models are continuously evolving. driving their application in specialized vertical domains and complex agentic tasks. For example, the emerging "Deep Research" paradigm (Shi et al. 2025b)), iterative self-incentivizzation frameworks (Shi et al. 2025c), and domain-specific benchmarks like DeepShop (Lyu al. 2025b) highlight the critical importance of tool utilization and autonomous multi-step interactions in real-world scenarios. Currently, translating these capabilities into complex tasks often relies on cumbersome multi-agent or multi-model pipelines. Instances of this include cooperative multi- -agntt systems for entity recognition (Wang et al., 2025) and alignment optimization (L.yu et al., 2025a), as well as specialized fragmented models for fine-grained document grounding (Zhou et al. 2025). search and recommendation (Zhao et al. 2026), and preference optimization (Wu et al. 2025b). b). However, these multi-model pipelines inevitably generate extremely long contexts and heavy computational burdens. While these efficiency issues can be partially alleviated by inference acceleration and efficient training methods like OmniKV (Hao al. 2025b), SnapKV (Li al. 2024b), and Low-Rank Clone (Hao et al. 2025a), relying on fragmented systems lacks architectural elegance (Shen et al.) 2026 Hao al. 2026). In contrast, UMMs offer more concise and efficient alternative, enabling single unified model to seamlessly handle diverse reasoning, tool-calling, and multimodal tasks without the cumbersome overhead of complex multi-agent interactions.
A.2 PROMPTS IMAGE GENERATION
Table lists the prompts corresponding to the generated images shown in Figure 5 The prompts are presented in the same order as the images: left to right, top to bottom. These examples highlight the diversity of tasks, ranging from descriptive captions to creative scene generation.
A.3 BASELINE IMPLEMENTATION DETAILS
To ensure fair comparisons, we adapt baseline methods to the VQ+AR setting used in our study.
For Cesture-of-Tranffrmme (Mo'T) (Deng et al. 2025 Shi al. 2025a Liao al. 2025 the
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Table 6: Prompts used for the image generation examples shown in Figure
No.
Prompt
gigantic library floats above the clouds, appearance resembling suspended castle.
Every book emits faint glow and drifts through the air with the gentle breeze.
2 A highly realistic close-up photo featuring beautiful 35 55-year-old red- oddhaired woman, writing her diary on her balcony. She is dressed in warm yet stylish clothing.
3
A happy snowman.
4 A woman and her little lion taking a selfie the grassland.
5 A beautiful owl with sleek feathers and lively eyes, its round head adorned with two furry cars. The elegant pattern is formed by the interweaving of snow-white down and deep brown flight feathers, making it appear both stunning and endearing.
A clearing in deep, mysterious forest, with mirror-like pond at its center, the water reflecting a night sky filled with the Milky Way.
handsome 24-year-old boy stands the center, with a sky-colored background. He wearing glasses, and the art style is very detailed, in anime style. A realistic photo of an elephant walking on the ocean floor.
8
9 An elegant and charming lady whose hair is entirely made up of blooming flowers, resem- bling masterpiece of nature. The flowers are of various types, possibly including delicate roses, fresh daisies, vibrant sunflowers, or other colorful blossoms.
10 A magnificent landscape photo depicting the northern lights dancing above the snow-capped mountain ranges Iceland.
Table 7: The 121-CompBench and MSCOCO performance Uni-X. indicates that it has been trained image-text data.
Model
# Params.
T21-Color
T21-Shape
Tel-Texture
T21-Avg.
MSCOCO CLIP-T
SDXL
3.5B
63.7
54.1
56.4
58.1
Janus
1.3B
75.5
47.7
62.1
61.8
Liquid
7B
71.5
52.3
65.1
63.0
30.7
EMU3
8B
61.1
47.3
61.9
56.8
31.3
UniTo
7B
71.2
51.8
66.7
63.2
Uni-X
3B / 4.5B
76.5
56.3
67.1
66.6
31.8
duplicated transformer is originally designed for image generation through diffusion. To remove the influence diffusion and isolate architectural effects, we reconfigure the duplicated transformer to operate directly image tokens. In this setup, the gkv sequences from the transformers concatenated within the attention module, allowing the model to incorporate visual information for both understanding and generation tasks. As result, the MoT results reported this paper reflect its effectiveness strictly within the VQ+AR paradigm, eliminating confounding factors introduced by diffusion-bart based processes.
A.4 MORE EVALUATION RESULTS ON IMAGE GENERATION BENCHMARK
We conducted tests on the T21-CompBench Huang et al. 2025 and MSCOCO (Lin et al. 2015). Part of the results were excerpted from Unieooken (Jiao et al.) 2025). As shown Table Uni-X surpassed the recent strong autoregressive models EMU3 and Liquid in the newly added image generation benchmark. Uni-X also achieved better results than UniToken, which includes semantic information.
A.5 GRADIENT CONFLICT ANALYSIS
Analysis on Mainstream Models. We further demonstrate the effectiveness of the current gradient conflict metric through experiments. We conduct quantitative analysis mainstream models
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Table 8: Average gradient conflict between different domain data, Higher values indicate higher degree of conflict.
Model
Code vs. Math
Code vs. Wiki
Math Wiki
Qwen2.5-1.5B
0.158
0.330
0.262
Qwen2.5-3B
0.130
0.382
0.294
Qwen2.5- 5-Coeer-BB
0.182
0.317
0.275
Qwen2.5-7B
0,153
0.263
0.240
Llama3.2-2
0.297
0.351
0.360
Table 9: Domain performance of Qwen2.5-3B and Qwen2.5-Coder-33 under zero-shot settings.
Model
HumanEval (Code)
GSM8K (Math)
MMLU (Wiki)
Qwen2.5-3B
39.0
6.0
65.0
Qwen2.5-Coder-3B
45.7
26.1
60.8
such as Qwen and Llama, as shown Table g For each dataset, we utilized total of 2M tokens (accumulated over 60 batches) to compute gradients, to ensure minimal gradient noise.
All models in Table X exxibit consistent pattern: the gradient conflict between Code vs. Math is strictly lower than for both Code vs. Wiki and Math vs. Wiki. It is well-established in LLM pre-training that Code and Math tasks often mutually enhance each other (Shao et al. 2024 Ma et al. 2023). This phenomenon is precisely reflected in our gradient conflict analysis.
The relatively high gradient similarity (low conflict) between these two tasks implies that improve- ments in Code performance drive improvements in Math performance. We further verified this Table 9 Qwen2.5-Coder-3B which was fine-tuned from Qwen2.5-3B to specifically en- hance coding capabilities, simultaneously achieved substantial improvement in Math performance. This validates our hypothesis that lower gradient conflict correlates with positive transfer between modalitiesddmmains
Analysis on Other Modules. In Section 3.1 of the main text, we analyzed gradient conflicts in the complete picture
down-projection weights of the Feed-Forward Network (FFN). To develop and confirm that this issue is not confined to single component, we extend our analysis to additional modules of the transformer. In particular, we examine gradient conflicts in the output projection weights (O PR PROJ) and value projection weights VR_JO of the self-attention mechanism, both of which play critical roles multimodal representation learning.
0.6
Shared-Traie Transforrrn
0.8
Shared-Transfomen Transformer
Uni-X (Ours)
Uni-X (Ours)
Gradient Conflict
Gradient Conflict
0.6
0.4
0.2
0.2
0.0
0.0
0
5
10
15
20
25
0
5
10
15
20
25
Layer Index
Layer Index
Figure 7: An analysis of gradient conflict in attention of out projection weights.
Figure 8: An analysis of gradient conflict in attention of value projection weights
Using the same methodology for conflict measurement, Figures and reveal a consistent trend with that observed in the FEN layers. The modality shared transformer exhibits severe gradient conflicts in the shallow and deep layers of both the attention output and value projection weights, with only partial alleviation in the middle layers. In contrast, Uni-X effectively addresses these issues:
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Table 10: Inference throughput comparison. Settings: batch size 48, input length e 1,00 tokens, outputting one image.
Model
# Params.
Tokens/s
Images/min
Shared Transformer (Qwen2.5-3B)
3B
975.2
Liquid
7B
182.0
10.6
EMU3
8B
199.0
2.9
Uni-X
3B 4.5B
910.2
53.3
Table 11: Ratio between t-layers and v-layers within the separated layers.
Configuration
MMLU
GenEval
MMB
Avg. 1
14:8
48.2
37.8
26.1
37.4
14:14
49.6
41.3
29.4
40.1
14:20
50.1
42.6
31.0
41.2
modelity-specific layers at both ends prevent confficts in low-level processing and output stages, and (ii) the shared middle block further reduces residual conflicts by leveraging semantic alignment.
These results strengthen our hypothesis that gradient conflict stems from the intrinsic statistical mismatch between vision and text, and they demonstrate that Uni-X's two-end-separated, middle- shared design offers a robust and generalizable solution across multiple transformer components.
A.6 INFERENCE EFFICIENCY
We have conducted a comprehensive evaluation of inference efficiency on H800 PCIe (350W) GPU. As shown in Table 10 Uni-X demonstrates superior throughput compared standard autoregressive baselines. Uni-X achieves high throughput (910.2 tokens/s) even compared to the original Qwen2.5- 3B (975.2 tokens/s), despite the architectural changes and higher number of parameters (4.5B vs 3B). This efficiency gain stems from the computational complexity of the attention mechanism the separated layers.
Theoretically, the computational cost of the Uni-X architecture is lower, and the current inference speed still has slight gap because the current code has not been fully optimized. In the separated layers, sequence of length is effectively partitioned into vision tokens of length and text tokens of length 6 (where a + n1). Since the self-attention complexity O and the separated layers enforce strict modality isolation, the complexity reduces to proportional to a² + b². Since a² 22 (a + b)2 n the computational cost attention in these specific layers is strictly lower than in a fully shared transformer, leading to the observed speedup.
A.7 ABLATION STUDY ON RATIO BETWEEN TEXT AND VISION.
We conducted experiments maintaining the same hyperparameters and training volume as Table 5 and the results are shown in Table IT] We continued to use Qwen2.5-1.55 with total of 28 layers as the base model. The number of vision layers directly affects the performance related to image understanding and generation. Surprisingly, reducing the number of vision layers also decreases pure text performance. This may be because the shared layers in the middle have to process more low-level vision information, thereby leading decline pure text capability, This experimental result also proves the effectiveness of our proposed architecture from another perspective.
A.8 USE OF LARGE LANGUAGE MODELS
Large Language Models (LLMs) were used solely as writing aids during manuscript preparation. Their role was limited to language polishing, improving grammar, clarity, and readability, without influencing the conceptual design, experimental methodology, or analytical findings. All research ideas, model designs, and experimental results are the original contributions of the authors.
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Reviews:
[{"review_id": "oi3sZGVgBv", "reviewer": "Reviewer_z7aM", "summary": "This paper introduces Uni-X, a new unified mllm architecture that addresses the \"gradient conflict\" phenomenon during unified training. By separating first and final layers while fusing different modalities in the middle layers, conflited gradients are alleviated compared to standard architectures. By scaling on data and compute, Uni-X achieves good performance in generation and understanding compared to its counterparts.", "questions": "1. Why does the authors choose cosine similarity as metrics for gradient-conflict? I believe more insights on this would be beneficial.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The presentation is clear and the paper is easy to follow.\n2. The code is already open-sourced, which is a plus for the community.", "weaknesses": "1. More evaluations on image generation benchmarks, e.g., MSCOCO, T2I-CompBench, should be considered to further validate the effectiveness of Uni-X.\n2. Some baselines like Blip3o and Bagel are also evaluated on image editing benchmarks like ImgEdit or OmniContext, why is Uni-X not evaluated on such benchmarks? I believe if Uni-X is more modality unified as claimed, it should be competent in tasks like image editing that requires both image and text understanding.\n3. The overall performance of Uni-X is mediocore compared with other baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uvXcxWrPQh", "reviewer": "Reviewer_v2vp", "summary": "The paper introduces a novel unified multimodal model architecture named Uni-X, which aims to address the gradient conflict issue in current unified multimodal models (UMMs) based on shared autoregressive (AR) Transformers. Uni-X employs an \"X-shaped\" structure design with \"separation at both ends and sharing in the middle,\" effectively alleviating conflicts between the visual and textual modalities while maintaining model simplicity. This design enhances training efficiency and performance in generation and understanding.", "questions": "1. Please explain why lower gradient similarity indicates stronger gradient conflict.\n\n2. Please elaborate on the similarities and differences between the Uni-X structure and the dual-tower architecture. If gradient conflicts imply the separation of the model, does it mean that a dual-tower architecture rather than a fully shared architecture is more suitable for generating and understanding integrated tasks?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper measures the degree of cross-modal gradient conflict by comparing the similarity of backpropagated gradients. It is found that visual and textual modalities cause severe gradient conflicts in both shallow and deep layers due to their vastly different underlying statistical properties, making it difficult for the model to optimize both simultaneously. In contrast, middle layers exhibit less conflict as they represent more abstract and semantically aligned features. Therefore, the authors propose designing model architectures based on modality characteristics rather than enforcing shared parameters across all layers. A series of experiments demonstrate the effectiveness of the proposed architecture.\n\nThe paper presents innovative insights, with solid experimental quality and clear expression, and holds significant importance.", "weaknesses": "1. The authors' explanation of using gradient similarity as a measure of gradient conflict is not particularly clear. Additional explanation is needed to clarify why lower similarity indicates stronger gradient conflict.\n\n2. Uni-X modifies the fully shared network structure by introducing separate branches for vision and text. This seems to revert to the classical dual-tower structure, with separated shallow and deep layers seemingly taking on some of the functions of a multimodal encoder-decoder. The authors do not elaborate on the similarities and differences between the Uni-X structure and classical structure.\n\n3. The paper lacks experimental comparisons with some recent models, such as blipo3-Next.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LStbqKiuE6", "reviewer": "Reviewer_7qva", "summary": "This paper propose Uni-X, a two-end-separated, middle-shared architecture, whose X-shaped design not only eliminates gradient conflicts at both ends but also further alleviates residual conflicts in the shared layers. When scaled to 3B parameters with larger training data, Uni-X matches or surpasses 7B AR-based UMMs, achieving a GenEval score of 82 for image generation alongside strong performance in text and vision understanding tasks. These results establish Uni-X as a parameter-efficient and scalable foundation for future unified multimodal modeling.", "questions": "1. The paper defines  $S_{base} = \\cos(g^1_{any}, g^2_{any})$ . How exactly are ( $g^1_{any}$ ) and ( $g^2_{any}$ ) computed? Are they averaged over multiple batches? For different types of multimodal data such as understanding, captioning, and generation, is ( $S_{base}$ ) stable? In other words, is this an inherent internal characteristic of the model?\n\n2. The paper compares the influence of different ratios between shallow separated layers ($x$) and deep separated layers ($y$). Another point of interest is the parameter ratio between t-layers and v-layers within the separated layers. I would like to see an ablation study regarding this aspect.\n\nIn summary, my primary concerns lie in **Weaknesses 1 and Weaknesses 2**. If the responses are reasonable and acceptable, I will raise the score.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It proposes the Uni-X architecture, which processes vision and text inputs separately, fuses them for computation only in the intermediate layers, and then separates them again for modality-specific outputs.\n2. It defines the concept of \"Gradient Conflict\" to represent the conflict between the vision and text components during model training. This concept is highly extensible to other multimodal models for analyzing the interplay between different modalities, making it valuable for future research in the field.", "weaknesses": "1. The paper defines *Gradient Conflict*, but the later sections lack further analysis. What I would like to understand is how Gradient Conflict reduces training efficiency or how it leads to model failures during inference. Is Gradient Conflict severe in mainstream models such as Qwen? Specifically, I hope to see a quantitative comparison of Gradient Conflict across different models and an analysis of its impacts.\n\n2. The experimental results in the paper show that Uni-X is comparable to other large models, but we notice that Uni-X performs worse than its base model Qwen2.5-3B on multiple benchmarks such as ARC-C and MMLU. I am concerned about whether such a Uni-X architecture is reasonable, and I hope to see further analysis of this phenomenon.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aEGLXEWriE", "reviewer": "Reviewer_xf1h", "summary": "This paper proposes Uni-X, a two-ended, middle-shared architecture: modality-specific blocks at the input and output are paired with a shared intermediate trunk for middle-level semantic fusion. This X-shaped layout mitigates gradient conflicts at both ends and further reduces residual interference within the shared layers. The design offers a clear architectural principle for unified generation models and merits wider exploration. Extensive experiments validate its effectiveness. Under identical training settings, Uni-X trains more efficiently than strong baselines. Moreover, the architecture scales to larger model sizes while delivering performance competitive with established large models.", "questions": "1. I am not convinced that the conflict factor $c_g$ is defined in a way that faithfully reflects gradient conflicts between image and text inputs. In particular, the meanings of $g_{\\text{any}}^{1}$ and $g_{\\text{any}}^{2}$ are unclear. Please provide precise definitions on what ``any'' indexes. Since $c_g$ underpins the paper’s claims, a clear mathematical specification would be very helpful.\n\n2. I noticed that Table 3 reports training efficiency. What about inference efficiency? Compared with a standard fully shared Transformer, the X-shaped split-and-share design may be less friendly to parallelization. I recommend evaluating Uni-X at inference time and discussing any trade-offs in latency and accuracy.\n\n3. What is the number of shard layers in Table 4?\n\n4. Uni-X’s in-context learning capability is demonstrated only qualitatively in Figure 5. Could you provide quantitative or statistical results to substantiate this claim?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "Uni-X’s strengths lie in a clear diagnosis of why shared autoregressive multimodal transformers struggle and a simple remedy that matches the problem: the paper pinpoints where vision–text conflicts arise across depth, then proposes a two-end-separated, middle-shared \n``X-shape'' layout that keeps early and late processing modality-specific while fusing semantics in the middle, yielding a lightweight design that reduces conflict without adding heavy subsystems. Empirically, the model shows strong results across understanding and generation, improves training throughput under matched settings, and scales efficiently, with a 3B variant achieving performance competitive with or better than several 7B baselines, which supports the claim that the approach is both practical and parameter-efficient for unified modeling.", "weaknesses": "The paper's weaknesses center on evidentiary clarity and completeness: the gradient-conflict metric $c_g$ is ambiguously defined (the roles of $g_{\\text{any}}^{1}$ and $g_{\\text{any}}^{2}$, indexing, aggregation, and normalization are unclear); inference efficiency is not evaluated, even though the X-shaped split-and-share design may impede parallelism relative to fully shared Transformers; Table 4 omits the exact number of shared layers and split points needed for replication; and the in-context learning claim is supported only by qualitative figures without quantitative benchmarks. However, I do not view these as major issues. The paper offers a systematic analysis of gradient conflict in unified generation models and provides useful insights for the community, so I believe it is a valuable contribution that deserves encouragement.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)