MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Authors: Yanghao Li
Rui Qian
Bowen Pan
Haotian Zhang
Haoshuo Huang
Bowen Zhang
Jialing Tong
Haoxuan You
Xianzhi Du
Zhe Gan
Hyunjik Kim
Chao Jia
Zhenbang Wang
Yinfei Yang
Mingfei Gao
Zi-Yi Dou
Wenze Hu
Chang Gao
Dongxu Li
Philipp Dufter
Zirui Wang
Guoli Yin
Zhengdong Zhang
Chen Chen
Yang Zhao
Ruoming Pang
Zhifeng Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Unified
Multimodal Large Language Models
understanding
generation
hybrid tokenizer
Primary area: foundation or frontier models, including LLMs
TL;DR: We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe

Abstract: Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddings for image-to-text understanding and discrete tokens for text-to-image generation within a common semantic space. A unified autoregressive LLM predicts high-level semantics in the form of text and image tokens, with an auxiliary diffusion decoder subsequently translating the image tokens into pixels. The architecture, together with a unified training recipe over understanding and generation data, enables scalable joint learning of both capabilities. Manzano achieves state-of-the-art results among unified models, and is competitive with specialist models, particularly on text-rich evaluation. Our studies show minimal task conflicts and consistent gains from scaling model size, validating our design choice of a hybrid tokenizer.

Published as a conference paper at ICLR 2026
MANZANO: A SIMPLE AND SCALABLE UNIFIED MUL- TIMODAL MODEL WITH A HYBRID VISION TOKENIZER
Yanghao Lift Rui Qian" Bowen Pan Haotian Zhange1 Haoshuo Huang Bowen Zhang
Jialing Tong* Haoxuan You Xianzhi Du Zhe Gan" Hyunjik Kim Chao Jia
Zhenbang Chang Gao Dongxu Li Philipp Chen Chen Yang Zhao Ruoming Pang
Wang" Yinfei Yang* Mingfei Gao Zi-Yi Dout
Wenze Hu
Dufter Zirui Wang Guoli Yin Zhengdong Zhang
Zhifeng Chen
Apple
First authors
Core authors
Project lead
Work done Apple
ABSTRACT
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially re- duces this tension by coupling hybrid image tokenizer with well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddings for image-to-text understanding and discrete tokens for text-to-image generation within a common semantic space. A unified autoregres- sive LLM predicts high-level semanties in the form of text and image tokens, with an auxiliary diffusion decoder subsequently translating the image tokens into pixels. The architecture, together with unified training recipe over understanding and generation data, enables scalable joint learning of both capabilities. Manzano achieves state-of-the-ar results among unified models, and competitive with specialist models, particularly on text-rich evaluation. Our studies show minimal task conflicts and consistent gains from scaling model size, validating our design choice of hybrid tokenizer.
INTRODUCTION
Unified multimodal models OOpenAl 2025 Deng et al. 2025 Chen et al. [025d Wu et al. 2025a Zhou al., 2024), which integrate both understanding and generation capabilities, have become increasingly prominent within the research community. The appeal of this paradigm stems from the discovery that integrating these domains unlocks emergent capabilities (OpenAT 2025 Deng et al. 2025) in generation, such complex world reasoning, multimodal instruction following, and iterative visual editing. Yet, in practice, adding generation often degrades understanding. Existing unified
models (Deng et al. 2025 Fan al. 2025 Liang al.
2024
Chen et al. 2025d) consistently lag
far behind their understanding
counterparts (Seed
2025
Bai et al.) 2025 Zhang et al. 2024a).
especially on text-rich benchmarks (Mathew al. 202
Masry al. 2022).
A key reason for this gap is the conflicting nature of visual tokenization. Auto-regressive generation
usually prefers discrete image tokens (Chameleon) 2024 [W al.) 2024; Ma al. 2025) while
understanding typically benefits from continuous embeddings. Many models adopt dual-tokenizer
strategy (Wu et al.) 20256] Chen et al.) [2025d] Fan et al. 2025 Tong et al.) 2024b), using semantic encoder for rich, continuous features while a separate quantized tokenizer like VQ-VAE (Van
Den Oord et al. 2017) handles generation. However, this forces the language model to process two different image token types, one from high-level semantic space versus one from low-level spatial space, creating a significant task conflict. While some solutions like Sectuur-oo-Traasffrmmme (MoT) (Liang et al.) 2024 Deng al. 2025) can mitigate this by dedicating separate pathways for each task, they are purameter-imefhhiier and are often incompatible with modern Mixture-do of -f-eerrssss (MoE) (Fedus et al. 2022 Lepikhin et al. 202T architectures. An alternative line of work bypasses this conflict by freezing a pre are-trained mu multimodal LLM and connecting it to a diffusion decoder (Pan
al. 2025
Wu et al. 2025a c). While this preserves the understanding capability, it decouples

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Published as a conference paper at ICLR 2026
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Tiee Trawking Desirer
Charox
20
30
38
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Manzano
GPT-40
Nano
GenEval
(a) Qualitative text-to-image generation on chal-
(b) Quantitative comparisons on popular understand- ing and generation benchmarks. Manzano 3B and 30B models achieve superior or competitive performance compared other SOTA unified multimodal L.MMs.
lenging prompts. Manzano handles counterintuitive, physics-defying prompts (e-g.. 'The hird is flying be-
low the elephant') comparably GPT-4o (Islam &
Moushi 2024) and Nano Banana (DeepMind] (025).
Figure 1: Comparison of qualitative and quantitative results for Manzano model.
generation, losing potential mutual benefits and limiting potential gains for generation from scaling the multimodal LLM.
To overcome the above challenges, we propose Manzano, a simple unified model that harmonizes the representations for understanding and generation. Manzano employs a unified shared visual encoder with two lightweight and specialized adapters: continuous adapter for understanding tasks and discrete adapter for generation. Because two adaptors originate from the same encoder, it yields hybrid representations from a homogeneous source, significantly mitigating task conflict in the LLM. We first pre-train the hybrid tokenizer with a small LLM decoder to pre-align the image features with the LLM feature space. Then the autoregressive multimodal L.M jointly trained on mixture of pure text, image understanding, and image generation data. Finally, we leverage diffusion image decoder (Peebles & Xie, 2023 CCen al. 2025al to render pixels by taking the generated image tokens as conditioning. By delegating pixel-level synthesis to the diffusion decoder, the LLM can focus more on modeling semantics through an unified autoregressive objective.
We train the unified multimodal LLM with joint recipe to learn image understanding and generation simultancously. This training consists of three stages: a pre-training stage on a large-scale corpus of text-only, interleaved image-text, image-to-text (IT), and text-to-image (TI) data; continued pre-trat pre-training stage on higher-quality IT and TI data; and a supervised fine-tuning (SFT) stage on curated text, IT, and TI instruction data to enhance instruction following capability and improve both understanding and generation tasks. Each stage serves a distinct role: pre-training on web-scale data builds broad cross-modal alignment, continued pre-training eurated data strengthens domain- specific capabilities such as document and chart understanding, and SFT teaches the model to follow diverse user instructions for both understanding and generation.
We demonstrate that Manzano achieves state-of-the-art performance on both understanding and generation tasks. As shown in Fig. la and LE our 3B model, despite its smaller LLM size, achieves competitive generation performance compared to other unified multimodal LLMs. Simultancously. it delivers significantly better understanding performance, especially on text-rich benchmarks that demand precise perceptual capabilities. Our ablations on the training recipes also indicate minimal cross-task conflict under joint training (Fig. (3). These findings suggest that the architecture and the training recipe effectively mitigate the conflict between understanding and generation, even in compact model.
Facilitated by the simplicity of the architecture and the joint training recipe, we further investigate the scaling behavior of Manzano. Our scaling studies Sec. 4.3 show substantial improvements across both understanding and generation benchmarks when scaling the LLM decoder (from 300M to 30B). In addition, enlarging the diffusion decoder also leads to significant gains in image structural integrity, as validated by large-scale human evaluations.
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RELATED WORK
2.1 MLLMS FOR IMAGE UNDERSTANDING
Recent advances in Multimodal Large Language Models (MLLMs) have led widely adopted architectural pattern that links a vision encoder with a language model through a trainable interface. Typical vision encoders include CLIP (Radford et al. 2021ap. SigLIP Zhai et al. 2023), and the recent Intern ViT (Chen et al. 2024c). Early works experimented with elaborate connector designs- for example, Flamingo Alaaraac al. 2022 incorporates gated cross-attention layers to inject image features into the LLM, while BLIP-2 Li et al. 2023b) introduces the Q-Former to better align visual and textual representations. A notable departure from these complex strategies is LLaVA (Liu et al. 2023 2024a), which demonstrates that lightweight Multi-L.ayer Perceptron (MLP) projection can effectively serve as the connector. This simplicity has since become the blueprint for many follow-up systems, such the MMI series (McKinzie et al.] [024] Zhang et al.
2024ah, the Intern VL family (Chen et al., 2024c) Zhu et al. [025] Wang et al. [222aa). and the
Qwen- VL models (team 2024 Bai et al.) 2025), which further improve performance by scaling up both data and backbone models. However, these MLLMs are primarily designed for understanding tasks and lack the capability to generate high-quality images, which limits their applicability in tasks that require bidirectional visual-text reasoning and creation. Despite being limited to understanding tasks, MLLMs still provide valuable strengths- their training recipes and scaling strategies are much more mature than those of current unified multimodal models, which we discuss next.
2.2 UNIFIED MULTIMODAL MODELS
The integration of image understanding and generation within a single, unified multimodal LLM is becoming prominent. GPT-4o (OpenAl 2025) demonstrates embedding image generation capabilities directly into an autoregressive T.I.M, which unlocks emergent abilities, such as stronger instruction following, improved text rendering. multi-turn visual editing, and sophisticated world knowledge reasoning. Existing unified models can be broadly categorized into three architectural paradigms.
First, the unified autoregressive (AR) approach (OpenAl 2025 Chen et al. 2025d b Han et al.
2025b Tian et al., 2024, [hameleoo 2024) Tong et al. 2024b Fan et al.
2025 Ma et al. 2025
Han et al. 2025at Geng et al. 2025 Wu et al. 2025d 2024 Wang al.
(025c) converts images
into sequences of discrete or continuous tokens, enabling LLM to jointly model both image and
text sequences in an autoregressive manner. Second, the decoupled LLM-diffusion approach (Pan
et al,, 2025; Wu et al., 2025a c) employs a largely frozen LLM for semantic understanding and contextual reasoning, while delegating image synthesis to a separate diffusion decoder. In this design, the LLM itself does not possess native image generation capability. Third, the hybrid AR-diffusion
approach (Deng et al. 2025 Zhou et al., 2024| Liang et al. 2024) integrates both paradigms within single transformer, using autoregressive decoding for text and an embedded diffusion process for images. Our model is most closely aligned with the first, autoregressive paradigm. However, instead of employing separate tokenizers (Deng et al. 2025 Chen et al. 2025d Fan et al.] 2025 Wu et al. 2025c) for understanding and generation, we introduce a unified semantic tokenizer to produce both continuous features for understanding tasks and quantized features for generation tasks. This hybrid tokenizer strategy substantially mitigates the task conflict that commonly arises. Moreover, while our I.LM backbone follows the autoregressive design, we augment with diffusion decoder for image synthesis, enabling high-fidelity generation guided by the semantic representations.
2.3 DIFFUSION MODELS FOR IMAGE GENERATION
Diffusion-based generative models (Sogg et al. 2020: Ho al. 2020 Dhariwal & Nichol 2021
have become one of the most prominent approaches for high-fidelity image synthesis. These models gradually refine Gaussian noise into realistic images through a learned denoising process. Latent diffusion methods (Rombach et al. 2022; Podell et al. 2023) enhance computational efficiency by conducting generation in the latent space of a pre-trained variational autoencoder (VAE) (Kingma & Welling 2022p. reducing memory and compute requirements while preserving visual quality. More recently, flow matching approaches (Liu et al. 2022; Ma et al. 2024 Tong et al.) 2023) have been introduced to connect source and target distributions via simplified continuous trajectories, leading to
further gains in synthesis performance (Esser et al. 2024). In parallel, architectural advances such as
Diffusion Transformers (DiT') (Peebles & Xie 2023 Chen et al. 2024a Eisser al. 2024 Labs
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Hybrid Image Tokenizer Training
Inference and Architecture
Understanding task
"<ing. start> <igg andd vibrant apple trea laden with rod apples.
Image
MANZANO
Continuous Adapter
Discrete Adepter
Unified LLM Decoder
Hybrid image Takenizer
random sample
friendly cartoon smilng face itss bearing two red apples. Please generate an Image.
"Whet does the sign say?"
300M LLM Dezoder
Generation task
Text tokan
Continuous image token
Discrata image token
Figure Our hybrid tokenizer workflow. (Left):
The tokenizer produces two distinct but homogeneous feature streams through separate adapters. During training, one adapter output is randomly sampled and passed to small LLM decoder for alignment. (Right): Once the tokenizer trained, the right panel illustrates how these two feature types are applied to understanding and generation tasks.
2024 Chen al. 2025a have demonstrated strong scalability and quality improvements, echoing the success of transformer-based designs in natural language processing.
Building on these developments, our diffusion decoder integrates strengths of the field by employing DiT in the latent domain with conditional flow matching objective. Unlike conventional text-to-image diffusion models (Ramesh et al. 2022 Saharia et al., 2022) conditioned on semantic embeddings from pre-trained text encoders such CLIP (Rafford et al. (02Ta). method leverages visual token embeddings generated by the LLM as conditioning signals.
3 MODEL
Manzano is a multimodal large language model (MLLM) that unifies understanding and generation tasks using the auto-regressive (AR) approach. The architecture comprises three components: (i) hybrid vision tokenizer that produces both continuous and discrete visual representations: (ii) an LLM decoder that accepts text tokens and/or continuous image embeddings and uuto-regressively predicts the next discrete image or text tokens from a joint vocabulary; and (iit) image decoder that renders image pixels from predicted image tokens (see Figure for the framework).
3.1 DESIGN CHOICES
Unified hybrid representation. The hybrid image tokenizer encodes images into continuous tokens for understanding (I2T), and discrete tokens for generation (T2I), while sharing the same
visual encoder: (i) Continuous for 12T' Manzano utilizes continuous embeddings for 12T tasks, a
strategy widely adopted in popular visual understanding models (team 2024 Seed 2025). which has shown superior performance, especially on text-rich tasks that require more visual details (e.g.. Doc ChartQA, and Info info)QA). Our ablation (Table IT also shows discrete tokens underperform on understanding tasks, which reflects the weak understanding results reported for some pure-discrete unified models (Chameleon) 2024 Wang et al., 2024). (ii) Discrete for T2I: Representing images as discrete code indices lets the LLM use the same AR next-token learning strategy as text, simplifying
generation pipeline and scaling behavior. (iii) Shared unified semantic space: Both branches originate from the same encoder backbone; thus, continuous and discrete tokens inhabit common semantic space, which reduces potential task conflict. The LLM decoder focuses on regressing high-level semantics (text and image tokens), while the diffusion decoder is responsible for rendering high-fidelit details pixel space. Many existing unified models rely on separate tokenizers for understanding and generation (Chen et al.) 2025d Deng al.] 2025 for instance, using CLIP tokenizer for understanding tasks and VAE tokenizer for generation. Although this strategy preserves more image spatial details, it exacerbates the task conflict within the subsequent L.M.
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Some studies (Chen al. 2025b find that a dedicated generation tokenizer is not compatible with LLM as the semantic tokenizer. Thus, our hybrid unified image tokenizer employs a single image encoder for both understanding and generation tasks.
Simplicity and scalability. Our design keeps the training losses standard and components cleanly decoupled, which simplifies unification and scaling for the unified MILM in these aspects: (i) Unified AR objective: Our unified LLM decoder uses a single AR objective for text-only, 12T, and T21 tasks without additional auxiliary losses per-task heads. (ii) Decoupled components: The clear split between semantic prediction (LLM decoder) and detail generation (image decoder) supports independent scaling of the base LLM and the image decoder. (iii) Practical scaling: Our approach readily leverages mature, scalable training pipelines from LLM/MLLM and diffusion decoders. By contrast, prior works (e.g.. Transfusion (Zhou et al., 2024 and Bagel (Deng et al.. 2025)) explored incorporating auto-regressive text prediction and diffusion image generation process for image generation in one LLM,, but left large- scale scaling under-explored. Our decoupled design facilitates scaling the LLM to 30B and diffusion decoder to 3B, yielding promising scaling behavior (Sec.
3.2 ARCHITECTURE
Hybrid Image Tokenizer. Our tokenizer comprises three components: (i) a standard vision transformer (ViT) (Dosovitskiy et al. 2020) as the vision backbone; (ii) continuous adapter, which first applies 3 x 3 Pratial-to-Chanee (STC) layer to reduce the number of spatial tokens by a factor of 9 (e.g.. from 42 x 42 x 1024 to 14 x 14 x 9216) and then uses an MLP to project each feature into the LLM feature dimension (e.g., 2048); and (iii) a discrete adapter, which also starts with the STC compression step but further quantizes the features using finite scalar quantization (FSQ) (Mentzer
al.
2023) chosen for its simplicity and scalability to large codebooks (64K in our experiments) before applying an MLP projection into the LLM feature dimension.
We connect our hybrid image tokenizer to a standard text LLM decoder for unified training on mixture of datasets containing text, understanding, and generation data. For the language backbone, we leverage internal pre-trained LLMs.
Unified LLM.
Image Decoder. We train image decoder on top of pre-trained hybrid image tokenizer to reconstruct images in pixel space from discrete image tokens. Given an input image, the hybrid tokenizer first encodes it into latent representation, which serves as the conditioning input for flow-matching pipeline (Lipman et al. 2022) that transports Gaussian noise into realistic images. For the decoder backbone, we adopt the DiT-Air architecture (Chen et al. 2025ab. which employs a layer-wise parameter-sharing strategy that reduces the size of the standard MMDiT model (Esser al. 2024) by approximately 66% while maintaining comparable performance. We provide three decoder configurations with the parameter sizes of 0.9B, 1.75B, and 3.52B, supporting a range of output canvas resolutions from 256 to 2048 pixels.
Inference Pipeline. The inference pipeline for both understanding and generation tasks is shown in Fig. 2 (right). For understanding tasks, Manzano uses the hybrid image tokenizer to extract continuous features. These features, along with text features, then fed into the unified LLM decoder to predict the final answer. For generation tasks, Manzano takes a text input and predicts a sequence of image tokens. The image decoder then renders these tokens into image pixels.
EXPERIMENTS
In this section, we first introduce the evaluation setup for understanding and generation capabilities (Sec. J. We then study the tross-token interplay in our model (Sec. 4.2). After that, we analyze the scaling behavior by varying model sizes (Sec. 4.3). Finally, we compare our model against state-of-the-at models, including both specialist and unified models. The training details, including the data mixture for understanding and generation, can be found in Appendix A.
4.1 EVALUATION
We evaluate our models on image understanding and generation capabilities on popular benchmarks.
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Tokenizer Paradigm
Understanding Tasks
Generation Tasks
General
Knowledge
Text-Rich
GenEval
DPG
WISE
Pure-Discrete 63.3
62.2
62.3
77
80.9
35
Dual-Encoder 63.8
63.6
72.0
66.3
Hybrid Tokenizer
64.9
66.5
73.3
77
79.9
35
Table 1: Tokenizer strategy ablation. Tokenizers are evaluated with a IB unified LLM model. The
full list of evaluation tasks for understanding can be referred to Sec. 4.4. The hybrid tokenizer outperforms the other two tokenizer paradigms.
Understanding, We adopt the three categories of benchmarks for multimodal understanding: (i)
General VQA: SeedBench (Li et al. [023a), RealWorIdQA (Zhang et al.) 2024b), and MMBench (Liu
al. 2024b). (ii) Knowledge & Reasoning: A12D (Kembhavi et al.) 2016), ScienceQA (Lu et al.
2022). , MMMU (uue et al.] 2023), and Math Vista (Lu et al. 2023). (iii) Text-rich Document & Chart
Understanding: CharlQA (Masry al. 2022). TextVQA (Singh et al.) 2019). Doc (Mathew
ct al. 202I), InfoVQA (Mthew ef al. 2022). and OCRBench (Liu et al. 2024c).
Generation. We use both automated and human evaluations: (i) Automated Evaluation: The
automated benchmarks include GenEval Ghosh et al. 2023) and DPGBench (Hu et al. 2024) for prompt following generation, and WISE (Nu al. 2025 for World Browledge-Inforeee generation.
(ii) Human Evaluation: We curate a comprehensive evaluation set comprising 800 challenging prompts, subsampled from established academic benchmarks (Wiles et al. 2025 Yu et al., 2022) and from widely used community evaluation platforms. The generated outputs are assessed by in-house human raters on three dimensions: structural integrity, instruction following, and aesthetic quality, For each dimension, raters assign one of three grades: major issues, minor issues, or no issues, and are quantized to scores afterwards. To mitigate bias, entity information is masked, and the sample order is randomized. Each sample is independently rated by three raters, and the final scores are obtained by averaging across raters to reduce variability.
4.2 UNDERSTANDING CIEHAANDIIN-GGGEAAIIOOOO INTERPLAY
In this section, we study the task conflict along two axes: (i) tokenizer strategy (pure-discrete vs. dual-encoder vs. our hybrid); (ii) lask mixing (unified vs. single-task). For simplicity, we skip the continued pre-training stage the unified L.LM training for these ablations.
Tokenizer Strategy. We construct two baselines to compare our unified hybrid tokenizer strategy: (i) Pure-discrete. Prior works (Chameleon) 2024 Wang al.] 2024/ Wu et al., 2024) train quantized semantic vision tokenizer using various quantization techniques (entzer et al. 2023 Van Den Oord et al., 2017) and then use an LLM to predict the next text and image tokens. To mimic these methods in setting, replace the understanding inputs for LLM with discrete features from our hybrid tokenizer, so the LLM uses the same discrete tokens for both understanding and generation. To understanding, we the same weights for the vision encoder and the discrete adapter from our hybrid tokenizer. (ii) Dual-encoder: Other popular models (Chen) dual-encoder strategy preserve detailed features by
isolate the effect of quantization
al. 2025d] Deng et al. 2025) semantic encoder for understanding and a VAE-style encoder for generation, effectively mitigating the degradation of understanding. We reproduce this baseline by replacing the discrete tokens from our brid tokenizer with those generated by an internal reproduction of Mag ViT-2 ( YYu et al. 2023). an autoencoder-stye tokenizer. This Mag ViT-2 tokenizer uses FSQ (Mentzer et al. 2023) with 64K codebook and spatial compression ratio of 8. For generation tasks, we resize images to 128x 128 pixels instead of the original 256x256. This reduced the number of tokens per image to 256, which we found improved the model's cestruction-folloiiin capabilities on benchmarks.
Table M shows the results on both image understanding and generation tasks. Our hybrid tokenizer paradigm shows the least task conflict and outperforms both pure-discrete and dual-encoder baselines on all tasks. Specifically, the pure-discrete baseline leads to a significant drop in understanding performance-estee eerformance-especiillly
text-rich benchmarks, due to information loss from quantization. While the dualeencoder baseline mitigates some of this degradation, it still consistently underperforms our hybrid tokenizer all understanding easks-espy knowledge benchmarks, which rely heavily

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Facilitated by the decoupled design of LLM Decoder and Image Decoder, we explore the model scaling behavior along two dimensions: L.M Decoder and Image Decoder. Similar Sec. 4.2 skip the continued pre-train stage in the unified LLM training for the scaling experiments. Published as a conference paper at ICLR 2026
Understanding Task
Generation Task
Understanding Tasks
Generation Task
90
96
-1.8
+2.0
+0.2
-0.3
-0.8
-18
+0.4
-2.3
54
56
-1.2
36
36
17
18
19
Knowledge
Deneral
Knowtedge
Undensaardrg-ony
Unilid
Ueifedmoodd
(a) 300M Model
(b) Model
Figure 3: Unified vs. Single-task study. Our unified model exhibits slight regression compared with the understanding-onl model on understanding tasks; however, this effect becomes negligible the 3B scale, where the gap is less than 1.0. For generation, the unified model shows decline only one benchmark compared with the generation-only model.
on the LLM's reasoning abilities. This suggests that the conflict between heterogeneous visual tokens resides within the L.M.
Unified vs. Single-task. To quantify the task conflict in our hybrid tokenizer paradigm, we compare our unified model with baselines trained exclusively for understanding or generation. For the understanding-only baseline, we remove text-to-image data from both the pre-training and SFT stages. We reduce the training steps to ensure it is exposed to the same number of text and image understanding tokens as our unified model. Similarly, for the generation-only baseline, we remove the understanding data and keep only the text-only and text-to-image data, while also reducing the training steps. We conduct this ablation study with a 300M and a 3B LLM decoder. The results, plotted in Fig. 3a and 3b, show that the unified LLM trained with hybrid tokenizer performs par with the dedicated, single-task models on nearly all tasks, even at compact size like 300M. This demonstrates that our unified hybrid tokenizer paradigm successfully unifies visual perception and generation without a performance trade-off.
4.3 MODEL SCALING BEHAVIOR
Scaling LLM Decoder. We vary only the LLM Decoder size (300M, 1B, 3B, and 30B) while keeping the image decoder (0.9B), data mixtures, and training hyperparameters fixed Fig. shows monotonic gains across all understanding (General Knowledge / (Text-Rich) and generation (GenEval DPG / WISE) metrics as the LLM decoder scales. Compared 300M, our 3B Manzano model improves significantly by + +14.2 (General), + +18.8 (Knowledge), +10.9 (Text-rich). +11.0 (GenEval), +1.48 (DPG), +12.0 (WISE). Further scaling to 30B yields smaller but consistent gains over 3B. Fig shows the qualitative examples for image generation. We can see that the generation capabilities, including esstuction-followinn text-rendering, and overall image quality, are improved consistently across different LLM scales. The results support the simple yet effective design for Manzano: LLM decoder captures high-level semantics, and scaling it benefits both understanding and generation.
Scaling Image Decoder. We evaluate the performance of image decoders of varying sizes built on top of 3B LLM decoder. Figure 46 shows that, in human evaluations, structural integrity improves substantially (+9.9), while instruction following performance remains unchanged. slight decrease observed in aesthetic quality. For automatic generation benchmarks, performance GenEval and DPGEval remains nearly identical, whereas WISE exhibits a modest improvement (+2.0).
Takeaways. Scaling the unified LLM backbone consistently improves both understanding and generation, with substantial gains text-rich understanding tasks and WISE for generation. Scaling the image decoder also enhances image quality, without negatively affecting understanding.
We pre-train 30B I.L Decoder on roughly half the tokens compared to other model sizes.
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Understanding Task
Generation Task
Human Eval
Generation Task
80
90
70
68
80
83
50
65
58
60
03
90
25
0.95
1,78
355
0.98
1.78
5.38
Knowlodge
Gentiaa 0P3
Stretunl Integrty Intrrcciin Follwwing
Gentral
OPG
wisE
(a) LLM Decoder Scaling
(b) Image Decoder Scaling
Figure 4: Model scaling behavior of Manzano. (a) Scaling the LLM decoder yields monotonic improvements across both understanding and generation benchmarks. (b) Scaling the image decoder enhances structural integrity while maintaining stable quantitative benchmarks. A drop in aesthetic quality is observed, which we leave for more in-depth study future work.
Geseral Benchmakk
Kammledge Benchmacks
Teat-ridh Benshmarks
Model
SEED
RedlWeriagA
MM4Berch (dene))
AED (lod)
SQA (isi))
MMMU
(val)
MathY (teemin))
ChangA (Ioe)
TestQQ (v6))
DocVQA (io1)
lafeVQA (io))
OCRBesch (lce)
Spertallc
BLIP-348
Xac
N041
72.2
60.5
68.3
41.1
39.6
71.3
N01
71.8
10.
770
76.7
M0.8
41.6
64.1
61.4
70.1
x3.3
89.0
65.7
5M115-36
72.4
56.9
72.4
65.7
R5.3
37.1
64.4
78.2
76.5
81.7
59.5
65.7
Im
77.3
51-1
66.9
72.1
Qwen2
SVL.12
SVL-18
76.4
$1.6
$3.1
62.3
$4.0
79.3
93.9
73.1
79.7
SAM Spacialter Mudei
LLAVA-NeXT
al
5-300
47.4
67.3
65.8
1mmmm/12.5500m
51.8
60.7
87.4
79.8
$5.2
Ciigleet Mixtol
Blip-w-48
Chncell
79.6
Ragol-1411
30
75.0
Manoms1166
26.0
K3.4
35.0
96.7
$7.8
73.1
84.4
94.3
81.9
36.5
GPT-4a
Slam 2 Sooo Mo0z)
77.4
75.4
84.6
00.7
69.2
61.3
55.7
02.8
73.6
Genim--e
consieruee 2005
78
86.3
885.
81.7
82.7
833
76.8
84.3
$6.2
Table 2: Comparison on general, knowledge, and text-rich benchmarks. GPT-4o, Gemini-1 1.5-Pro and Sremini-2.5-Pra numbers are from Open reproduced independently in experiments and might differ from those reported in previous studies. Manzano demonstrate competitive understanding capabilities, especially on text-rich benchmarks.
VLM Leaderboard, represents that the results were
We observed that performance on the GenEval and DPG benchmarks becomes saturated when the model becomes larger. This saturation motivates re-examination of how emergent capabilities of unified models could be assessed, as existing benchmarks may capture only a limited portion of overall capability and can be boosted through targeted data tuning (L.uu et al. 2025). Meanwhile, we observe substantial improvements on knowlede generation tasks, and we hope these findings pave the way for new directions in future community rescarch.
4.4 COMPARISONS WITH UNIFIED AND SPECIALIST MODELS
To comprehensively assess Manzano model's capabilities, compare its performance against SOTA unified and specialist models (i.e., understanding-on and standalone generation models).
4.4.1 IMAGE UNDERSTANDING
As mentioned in Sec. we evaluate our model's understanding capabilities from three perspectives: Knowledge Reasoning, General Visual Question Answering, and Text-rich Document & Chart Understanding. The results, shown in Table compare model against other understanding- only models of a similar size. Despite being a unified model, our model achieves satte-of-the-ae performance many understanding benchmarks: (i) Knowledge & Reasoning: At the 3B scale, our model outperforms all unified models within the 7B scale and achieves performance on par with better than the best specialist models the 3B size. At the 30B scale, our model ranks
8

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Published as a conference paper at ICLR 2026
Gesiaal Bendmark
WISH Benchmark
Model
Single
Two
Couring
Colors
Postton
Color Ant.
Oveaal
Cutural
Time
Space
Bloggg
Preses
Chenisny
Overal
Divilcated Mudvi
SDXL.355 Inselle K02
0.34
0.65
0.15
0.43
0.44
0.45
BALL-
0.56
0187
0.47
1181
11:81
0.45
SD3 1D1-Medivirra
0.99
0.94
0.72
0.89
0.33
0.60
0.74
0.43
0.50
0.52
0.41
0.53
0.33
0.45
0.45
0.50
0.48
0.49
0.56
0.54
0-47
FLUX.I dev 12B
0.98
0.93
0.75
0.83
0.68
0.65
0.52
0.48
0.58
0.62
0.42
0.51
0.35
0.50
Eiglasion Comporatoon
0.56
0.49
Oom62en-778 We
0.95
11:54
11:55
Qwc--Image-7
0.99
0.92
0.89
0.88
0.76
0.77
0.87
0.67
0.67
0.80
0.62
0.79
0.41
0.67
Uniited Moliinswdil LLM
Imm--hh-118 Charaal af pmp
0.89
01.79
0.10
0:31
X-Ouni-18
0.837
Mancans
0191
0.71
00.74
0.42
0.45
0.51
Man
1.50
0.91
0.83
0.67
0.84
0.65
0.85
0.58
0.50
0.65
0.50
0.55
0.32
0.54
GPT-4 Slmm Morphh 2004
0:99
0.92
0.55
0.9
0.75
0.6
0.34
0.1
0.71
0.89
0.83
0.79
0.24
0.80
Table 3: Comparison GenEval and WISE benchmarks. i represents the evaluation that involves
LLM rewriting. Manzano achieves competitive performance compared with other unified models.
Math first on the ScienceQA, MMMU, and Math Vista benchmarks and third the A12D benchmark, outperforming all other unified and specialist models in these categories, Notably, our model surpasses the proprietary models listed in the final three rows on ScienceQA and is competitive with the current state-of-the-ar art model on the A12D benchmark. (ii) General Visual Question Answering: For general visual question answering, our model generally outperforms other unified models, despite its smaller size. It also achieves competitive results with state-of-the-art specialist models at both scales. (iii) Text-rich Document and Chart Understanding: On text-rich and chart understanding tasks, our model achieves the best performance on four out of five benchmarks (ChariQA, TexiVQA, DocVQA, and OCRBench) when compared to all other unified, specialist, and proprictary models. For the Info VQA task, our model significantly outperforms its unified counterparts and achieves the best results among specialist models.
4.4.2 IMAGE GENERATION
We present the quantitative results for our model's image generation capabilities, evaluating them on two benchmarks: GenEval (Ghosh et al. 2023) and WISE (Niu et al. 2025). While both benchmarks assess how well models follow text instructions, WISE additionally evaluates semantic grounding through wonddddoowlldge-nformeee
attributes. As shown in Table our model achieves SOTA results among unified MLLMs on both GenEval and WISE. The 3B model can already perform competitively with or better than much larger unified models, and scaling to 30B further improves generation quality most notably yielding large gain WISE, while maintaining strong GenEval performance. This confirms that our unified architecture and training recipe support strong certucctio-followiin generation. Furthermore, we show the editing capabilities of our model in Sec.
4.4.3 COMPARISON WITH UNIFIED MODELS
In addition to specialist models, we also compare against recent unified models such as Janus- Pro (Chen et al. 2025d), X-Omni (Geng et al. 2025), and Bagel (Deng et al. 2025). which aim to handle both understanding and generation within a single framework. Our Manzano model substantially outperforms these unified baselines across almost all understanding benchmarks. At similar scale, our 3B model exceeds X-Omni and BAGEL on DoAA OCRBench, and SEEDBench while maintaining competitive performance on Math Vista and ChartQA. Our 30B model further extends this lead, consistently surpassing all existing unified models across knowledge, general VQA, and text-rich domains. This demonstrates that unification does not have to come at the cost of understanding capability. With careful architectural and training design, our model matches or surpasses the best specialist models while providing strong generative capability. We provide more qualitative comparison to the tate-of-the-an unified models in Fig.

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Published as a conference paper at ICLR 2026
5 CAPABILITY EXTENSION TO IMAGE EDITING
Image editing represents both a crucial application and a natural extension of text-to-image generation. Despite Manzano demonstrating strong multimodal modeling capabilities, particularly text-rich understanding benchmarks, achieving pixel-level precision in fine-grained image editing remains challenging. Similarly, recent work within the decoupled LLM-diffusion paradigm (Wu al. 2025c reports difficulties when relying solely on the LLM for precise editing, since the LLM lacks native mechanisms for direct pixel-level control.
We adopt an approach similar to (Wu et al. 2025c) by providing the reference image simultancously both the L.MM and the diffusion decoder. In this formulation, the LLM is responsible for diverse instruction following and maintaining semantic coherence, while the diffusion decoder enforces precise pixel-level control. By jointly conditioning on the reference image, Manzano enables accurate semantic instruction following while preserving fine-grained visual consistency. In Fig. 5 Manzano demonstrates versatile editing capabilities, including catruction-guided editing, style transfer, inpainting, outpainting, and depth estimation.
a) Instruction-guided Editing
crazy
woW
Change the word "crazy" to "wow?
Add a pink ball in front of the kitten.
Turn the blanket light purple.
b) Style Transfer
Ink cripping drawing
MMss-staneddeee glass
Game retro
c) ImpsintinjOOutpaantnn/Dppph Estimation
BURGER
Figure 5: Editing capabilities of Manzano. (a) instruction -iriied editing, (b) style transfer across diverse visual domains, and (c) extended editing tasks including inpainting, outpainting, and depth- estimation. Manzano achieves pixel-level controls across these five editing tasks.
CONCLUSION
We introduced Manzano, an MLLM that combines visual understanding and image generation through a hybrid image tokenizer and unified autoregressive backbone. The LLM predicts high- level semantics in the form of text and image tokens, while lightweight diffusion- -based image decoder renders final pixels from the generated image tokens. Coupled with streamlined three-stage training recipe, this architecture delivers: (i) state-of-the-arr on understanding tasks, (fi) substantial gains on generation among unified models, and (iii) minimal task interference as validated by interplay and scaling ablations. Beyond generation, Manzano naturally supports image editing by conditioning both the LLM and image decoder on a reference image, enabling instruction followwig with pixel-level control.
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Published as a conference paper at ICLR 2026
Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408. 2024.
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17


Reviews:
[{"review_id": "47cPGSioRP", "reviewer": "Reviewer_B3Pz", "summary": "This paper introduces Manzano, a unified and scalable multimodal large language model designed to jointly handle visual understanding and generation. The framework employs a hybrid image tokenizer and a shared vision encoder with dual lightweight adapters that produce both continuous and discrete visual representations within a common semantic space. A single autoregressive LLM models text and image tokens jointly, while an optional diffusion decoder reconstructs pixels from image tokens. Through a unified training recipe, Manzano effectively balances understanding and generation without significant task interference, achieving state-of-the-art performance among unified models and strong competitiveness with task-specific systems.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper presents a very clear and well-motivated narrative, with the logical progression from problem statement to solution design articulated in a highly readable manner.\n\n2. The experimental evaluation is thorough, convincingly demonstrating the effectiveness of the hybrid tokenizer and the unified autoregressive architecture across both visual understanding and generation.\n\n3. The unified framework tackles a challenging trade-off (between understanding and generation) and delivers empirical evidence that minimal task conflict occurs—this strengthens the claim of scalability and generality.", "weaknesses": "1. The scaling analysis of LLM size is incomplete — the paper jumps directly from 3B to 30B without intermediate settings such as 7B or 14B, leaving a significant gap in understanding how performance scales with model capacity.\n\n2. While the experiments and insights are clearly presented, the overall methodological novelty is rather limited, as the work mainly integrates existing design elements in a well-engineered manner.\n\n3. The claim “We first pre-train the hybrid tokenizer with a small LLM decoder to pre-align the image features with the LLM feature space” would benefit from additional references to similar prior works, such as X-omni[1] and ETT[2], to better contextualize its contribution.\n\n[1] Geng Z, Wang Y, Ma Y, et al. X-omni: Reinforcement learning makes discrete autoregressive image generative models great again[J]. arXiv preprint arXiv:2507.22058, 2025.\n[2] Wang W, Zhang F, Cui Y, et al. End-to-end vision tokenizer tuning[J]. arXiv preprint arXiv:2505.10562, 2025.\n\n4. There is a minor typo on the first page — “Manzanoemploys” — which should be corrected for polish.", "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": "iMYkThmYaF", "reviewer": "Reviewer_SqUp", "summary": "This paper proposes a hybrid vision tokenizer and a unified MLLM for both image understanding and generation. The vision tokenizer produce both continuous and discrete latent for different tasks: continuous for multimodal understanding and discrete for autoregressive image generation. Experiments on benchmarks, model scaling and tokenizer comparisons validate its effectiveness.", "questions": "- Why not to use open-sourced LLMs?\n- Tab.1. Why Dual Encoder performs much worse on generation tasks?\n- Sec A.2.1. Why the CLIP model is trainable during vision tokenizer training? In ILLUME, the CLIP model is frozen and benefits from faster convergence.\n- How about the performance of image editing on benchmarks? Which representation is used for encoding the input image, discrete or continuous?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear motivation. One tokenzier to handle both understanding and generation is an improtant step to unified models. Compared to many works with two seperate vision tokenizers, this paper proposes a simple and scalable vision tokenizer, leading to higher degree of unification.\n- Detailed illustration of model architecture, training recipe and evaluation.\n- Sufficient experiments on model scaling and model comparisons", "weaknesses": "- Lack of comparisons with related works like ILLUME [1], which also leverages continuous features for image understanding and discrete feature for image generation. Although ILLUME and MANZANO implement the vision tokenizer in a different way, they share similar motivation and architecture in tokenizer and MLLM. This makes the paper with limited techinical novelty.\n- Hybrid tokenizer training. Why to use LLM decoder for tokenizer training instead of reconstructing a pretrained vision tokenizer (e.g. CLIP/SigLIP)? I believe it do good to the image understanding task but may not optimal for the image generation task.\n- Although the two types of visual representation are similar, but the embedding for image generation cannot be reused for image generation like discrete tokenizers. It makes the model hard to handle interleaved image-text tasks.\n\n\n[1] ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance. arXiv 2412.06673", "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": "FCEP1psgXP", "reviewer": "Reviewer_xZkP", "summary": "The paper presents Manzano, a method for unified understanding and generation of images, via fusion of respectively continuous and discrete tokens. This leads to improved or competitive performance on various tasks.", "questions": "Can you discuss how the learned representations, where the unified framework benefits from the unification and where it hurts, including qualitative examples and/or empirical findings that go deeper than the numbers?\n\nBagel is very performant too. Are there particular advantages of the proposed method compared to Bagel?\n\nHow are the generated images better than those of other methods, what should the reader take from the illustrations?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The fusion of continuous and discrete tokens, as each of them is better suited for generation and understanding, is interesting. \n\nThe results are good, structurally favorable or similar to other methods.", "weaknesses": "A discussion of why discrete tokens are better than the proposed method for generation - DPG is lacking. \n\nThe results are mostly about discussing that the quantitative performance is good. I would be interested in a discussion about the learned representations and a discussion on where the unified framework benefits from the unification and where it hurts, including qualitative examples and/or empirical findings that go deeper than the numbers. \n\nIt is not clear to me that the generated images are better than other methods.", "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)