Reconstruction Alignment Improves Unified Multimodal Models
Authors: Ji Xie
Trevor Darrell
Luke Zettlemoyer
XuDong Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Multimodal Models; Image Generation; Image Editing; Visual Understanding
Primary area: generative models
TL;DR: We present Reconstruction Alignment (RecA), a resource-efficient post-training method that improves unified multimodal models by leveraging visual understanding encoder embeddings as dense “text prompts”.

Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.
However, conventional training relies on image–text pairs (or sequences) whose captions are typically sparse and miss fine-grained visual details, even when they use hundreds of words to describe a simple image. We introduce **Reconstruction Alignment (RecA)**, a resource-efficient post-training method that leverages visual understanding encoder embeddings as dense “text prompts,” providing rich supervision without captions. Concretely, RecA conditions a UMM on its own visual understanding embeddings and optimizes it to reconstruct the input image with a self-supervised reconstruction loss, thereby realigning understanding and generation. Despite its simplicity, RecA is broadly applicable: across autoregressive, masked-autoregressive, and diffusion-based UMMs, it consistently improves generation and editing fidelity. With only 27 GPU-hours, post-training with RecA substantially improves image generation performance on GenEval (0.73 → 0.90) and DPGBench (80.93 → 88.15), while also boosting editing benchmarks (ImgEdit 3.38 → 3.75, GEdit 6.94 → 7.27). Notably, RecA surpasses much larger open-source models and applies broadly across diverse UMM architectures, establishing it as an efficient and general post-training alignment strategy for UMMs.

Published as a conference paper at ICLR 2026
RECONSTRUCTION ALIGNMENT IMPROVES UNIFIED MULTIMODAL MODELS
Ji Xie', Trevor Darrell', Luke Zettlemoyer', X Wang UC Berkeley, ?University of Washington, DDuke University Project Page: hmpp://heconssrrucionn alignment, github.io/
ABSTRACT
Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies on image-text pairs (or sequences) whose captions typically sparse and miss fine-gran eraigrai visual details, even when they use hundreds of words to describe a simple im- age. We introduce Reconstruction Alignment (RECA), resource-efficient post-training method that leverages visual understanding encoder embeddings as dense "text prompts," providing rich supervision without captions. Concretely, RECA conditions UMM on its own visual understanding embeddings and opti- mizes reconstruct the input image with a self-supervised reconstruction loss. thereby realigning understanding and generation. Despite its simplicity. RECA is broadly applicable: across autoregressive, maskeddauutoregressive and diffusion- based UMMs, it consistently improves generation and editing fidelity. With only 27 GPU hours, post-training with RECA substantially improves image generation performance GenEval (0.73-0.990 and DPGBench (80.93-885 15)), while also boosting editing benchmarks (ImgEdit 5.38-43.75, GEdit 6.94-7..27). Notably, RECA surpasses much larger open-source models and applies broadly across di- verse UMM architectures, establishing it as an efficient and general post-training alignment strategy for UMMs.
GenEval
DPGBench
0.90
Make block onter Baniiaa
GenEval Overall Scare
Back
0.85
SANK:
88
Jue C
INVES
DPGBench
8
350 Seciua
KANLE
BAGEL
0.70
Nare
DALLO
15
Model Vype
Diffeskez
UMM
20
Frivate
2m 1om 20t Priccte
Model Parameters (Billions)
Model Parameters (Billions)
blue
RecA
GPT-to
Figure 1: Post-training UMMs with reconstruction alignment (i.e., RECA) substantially im- prove image generation and editing. Left: performance comparison on GenEval and DPGBench, where 1.15 1.5B-parameter model post-trained with RECA surpasses much larger models across multi- ple benchmarks (Table GenEval, DPGBench and WISE): Middle: compared with GPT-4o, RECA follows generation instructions more faithfully, especially for color attributes and spatial positions; Right: for editing, RECA better preserves instance identity, overall layout, and object shapes of the original images, such as the girl's 's lips.
INTRODUCTION
Building on the success of large language models (LLMMs) (Brown et al., 2020; Touvron et al., 2023; Yang et al., 2024a), researchers have developed Multimodal Large Language Models (MLLMs) (Liu et al., 2024b; Bai et al., 2023; 2025; Radford et al., 2021; Zhai et al., 2023; Chen et al., 2024b; Zhu al., 2025) with strong visual understanding performance. Recently, unified multimodal models
"corresponding author

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Published as a conference paper at ICLR 2026
a) Standard model training with text-image pairs
b) RecA
Image
Can optimize generation models
10 words
Kitter son puppy nuttlle
using semantic embedding
Visual Und. Encoder
parmly under may yellow blanket
denser "Lext prompt"??
Unified Multimodal Models
Unified Multimodal Models
Original
Text-to-
Embedding-to- Image Loss
Image Loss
Dense supervision from visual embeddings. a) Typical image generation models are trained on image- caption pairs and/or sequences whose text is a sparse representation of visual information. An image is worth far more than a hundred words and contains rich details that text alone cannot capture. As shown in the left three examples, even lengthy captions (500 words) miss key aspects such as lextures, styles, layouts, shapes, and attributes, leading imperfect generations relative to the original image. b) By contrast, embeddings from visual understanding encoders, e.g., CLIP, preserve richer and more faithful semantics. Can these image-embedding pairs provide the dense supervision needed to enhance image generation and editing? Surprisingly, the answer yes: pairs can improve T2I and image editing in a zero-shot manner.
Figure 2:
we find that
(UMMs), or Omni Models, have been proposed to both understand and generate across modali- ros-rcading and writing visual content and text within single architecture (Tong al., 2024a; An et al., 2025; Shi et al., 2024; Zhang et al., 2025a; Ge et al., 2024; Wu et al., 2024b; Pan et al., 2025a; OpenAl, 2024; Li al., 2025; Wu al., 2025a; Niu et al., 2025a). The academic community envisions that a unified framework can inherit the reasoning and world knowledge of LLMs while extending them image generation.
However, UMMs face a fundamental limita- tion: conventional training relies on image-text pairs, where text captions provide supervision. Even captions spanning hundreds of words omit critical visual details such spatial lay- out, geometry, or fine- fine-grained attributes (Fig- ure 2), introducing systematic biases into the learned representations. For instance, because captions rarely describe broccoli's color, mod- cls tend to overfit to the rule broccoli - green, often collapsing
Unified Multimodal Models
A yellow broccoli
What's this?
A yellow broccoli
Unified Multimodal
X
Models
Figure 3: UMMs often correctly recognize an uncommon concept (yellow broccoli) but fail to generate it, revealing misalignment between un- derstanding and generation.
green outputs when given atypical prompts like "a yellow broccoli" (Fig- 3). This misalignment motivates us to ex- plore alternative forms of supervision. Rather than relying on captions, we leverage embed- (Radford et al., 2021; Zhai et al., 2023; Chen
dings from UMM's visual understanding encoders al.. 2024b; Zhu al., 2025), which can map pixels into language-alignet semantic space inter- pretable by themselves. Crucially, embeddings from understanding encoders (e.g.. CL.IP SigL.P) capture semantic structure far more effectively than those from generation encoders (e.g., VAE, VQ-GAN), These semantic embeddings provide dense, semantically grounded supervision without paired captions, raising a central question: Can we improve the generation capabilities of UMMs by training them with semantic embeddings maximally informative "text prompts"?
Building on these insights, we propose RECA, a resource--fficient post-training strategy. The core idea is simple: condition UMMs their own visual understanding encoder embeddings-dde dense "visual prompts" that encode layout, color, and attributes beyond what captions capture- and train them to reconstruct the image. This semantic reconstruction provides richer supervision without
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Self-supervised Reconstruction Loss
Frozen
Trainable
Visual Understanding Encoder / g
Unified Multimodal Models
Vision Decoder
image prompt
random noise
Text Encoder
"Plezse diescribe image in detail"
image
visual response
latents
"null" text prompt
Figure 4: Overview the reconstruction alignment (RECA) pipeline. A visual understanding encoder (e.g., CLIP SigLIP) extracts semantic features from the input image, which fused with template text embeddings and passed the Unified Multimodal Model (UMM) to regenerate the image. The UMM is optimized with sele-supe loss (diffusion cross-entropy) between the original and generated images or image latents. We freeze the understanding encoder except in cases where the UMM employs shared encoder for both understanding and generation (e.g., Har- mon). At inference time, RECA requires no additional inputs, operating as a standard UMM.
additional labels. Despite its simplicity, RECA yields substantial improvements. With only 27 A100 GPU hours, a 1.5B-parameter UMM post-trained with RECA surpasses significantly larger open-source models, achieving state-of-the-art results on GenEval (0.86) and DPGBench (87.21). Importantly, these gains are obtained without any GPT-4o-Imags distillation data reinforce- ment learning (OpenAL, 2024; Chen et al., 2025b;c), in contrast to prior work. Moreover, when post-trained with GPT--o-Image data, RECA further improves to GenEval (0.90) and DPGBench 15).15). RECA also boosts image editing quality, raising ImgEdit from 3.38 to 3.75 and GEdit from 6.94 to 7.27. Moreover, RECA applies broadly across UMM families with different generation mechanisms, including Show-o (Xie et al., 2025b) (Discrete), Harmon (Wu et al., 2025d) (MAR), OpenUni (Wu al., 2025c) (Continuous), and BAGEL (Deng et al., 2025) (Continuous), highlight- ing its generality.
The key contributions can be summarized follows:
Method: We introduce RECA, semantic reconstruction- post-training method that uses semantic visual embeddings as "dense prompts", providing rich supervision without captions. Generality: We show that RECA consistently improves diverse UMM architectures, spanning autoregressive models to hybrid frameworks that integrate autoregressive and diffusion models. Performance: We demonstrate strong empirical gains, with 1.5B-parameter model surpassing GPT-4o and larger open-source models using only 27 A 100 GPU hours, significantly outperform- ing prior state-of- of-the-artt methods without distillation reinforcement learning (RL).
RECONSTRUCTION ALIGNMENT
In this section, we present Reconstruction Alignment (RECA) as self-supervised image recon- struction objective. By training the model to reconstruct images from its visual understanding em- beddings, RECA provides dense supervision that captures fine-grained visual details often omitted by text captions. We show the overall pipeline in Figure 4.
2.1 MOTIVATION AND SETUP
Equipped with a visual understanding encoder, modern multimodal LLMs (MLLMs) effectively treat images rich, dense context and successfully handle not only basic perceptual and question answering tasks, but also challenging multimodal reasoning problems such as symbolic, spatial, and mathematical reasoning (Bai al., 2025; OpenAI, 2024; Huang et al., 2025; Chen al., 2024a).
One may expect that this dense, text-like context can be naturally extended to the image generation task. However, when extract embeddings from the understanding encoder, insert them into
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Published as a conference paper at ICLR 2026
Original Image
Baseline
Post-trained
Original Image
Baseline
Post-trained
Figure 5: Post-training with RECA restores visual details missed by the baseline models. For each query image (left), we feed its visual understanding embeddings back into the UMM with the instruction "Describe the image in detail." " The baseline model (center)'s visual responses, i.e., images, preserve the main subject but distort layout, textures, and colors, while RECA markedly restores visual details like geometry, color, and overall fidelity.
prompt template (e.g.. "Describe the image in detail."), and ask the UMM to generate, the results in Figure reveal gap: the main subject is preserved, but spatial layout and composition are scrambled. This raises the hypothesis that the image itself can be treated dense "text prompt" to train the model, enabling the generation branch of the UMM to better exploit the LLM features and improve its generation capability.
2.2 RECA TRAINING PARADIGM
Training losses. Traditional UMMs are trained with a combination of text-to-image (T2I) and image-to-text (I2T) objectives. Formally:
Ca [(fo(tprompi)) Igt), Ci = L(fo (.L.foconnaa(tquesteee h,)). fanswer)
where C(:--) denotes the training loss (e.g., cross-entropy for autoregressive models (Xie et al., 2025b; Chen et al., 2025e), diffusion loss for diffusion-based models (Zhou et al., 2025; Deng et al., 2025)), (prompp: Iquestion, fanswer are text inputs/outptss, h, are embeddings extracted from the under- standing encoder, and Ig: is the ground-trut truth image (we omit the explicit VAE decoder notation for simplicity). fo( represents the UMM with parameters 0. Preliminaries of different image genera- tion paradigms are listed in Appendix A.
Our key idea is to replace conventional T21 supervision with a semantic-level image reconstruction loss. Instead of using text captions that are inherently sparse in visual information, we condition the UMM on its own visual understanding embeddings. The reconstruction loss is:
CRECA L[[oo(conca(((aampaaae h,)), Ig)
where hemplate is a prompt template triggering image reconstruction (e.g., Describe the image in detail). More details of the template collection and visual embedding integration are provided in Appendix C. The overall training loss is:
Listal = ARECA CRECA + Aiziizi + AmiCas
For UMMs that share parameters between understanding and gen- eration, we set Azz = to preserve 12T capability; otherwise, we set Az = O and freeze the understanding component. We freeze the understanding encoder in most cases, except when the UMM employs the same encoder for both tasks (e.g., Harmon (Wu et al., 2025d)).
We set ÅRECA = A,
Input resolution. Previous works show that higher-resolution visual understanding embeddings retain substantially more pixel-level detail (Allakhverdov al., 2025; Lin et al., 2025). To encour- age the model to focus on semantic-level reconstruction, we resize input images to the minimum resolution accepted by the understanding encoder.
Model inference. At inference time, our post-trained UMM operates identically to a standard one and requires additional visual embeddings. For image generation, only text prompt is needed; for image editing, the inputs remain the text prompt and the original image. This preserves the model's 's original usability while providing enhanced generation capabilities.
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Table I: Results on GenEval and DPGBench. Scores marked with (*) are our reproduced results using 12 random seeds. We use Harmon-1.33 as baseline and post-train it with RECA. The gray- colored rows denote private models, and their results are cited from (Yan et al., 2025b; Geng al., 2025), Arross (1) denote that higher is better. Detailed comparison is listed in Appendix F.3.
GenEval t
DPG
Model
Params
Single Obj.
Two Obj.
Counting Colors Position Color Attri
Overall
Score
Harmon
1.5B
0.99
0.87
0.69
0.86
0.45
0.51
0.73
80.93
SD3-Medium
2B
0,99
0,94
0.72
0.89
0.33
0.60
0.74
84.08
Janus- Pro
7B
0,99
0.89
0.59
0,90
0.79
0.66
0.80
84.33
FLUX-dev
12B
0.99
0.85
0.74
0.79
0.21
0.48
0.68
84.00
BAGEL*
14B
0,99
0.93
0.80
0,86
0.51
0.63
0.79
84.03
Playground-v3
24B
0.99
0.95
0.72
0.82
0.50
0.54
0.76
87.06
GP1-4o-Image
0.99
0.92
0.85
0.89
0.74
0.71
0.84
86, 23
RECA
1,5B
1,00
0.98
0.71
0,93
0,76
0.77
0,86
87.21
Models Trained with GPT-4o Data
Ovis-UI
3.6B
0.98
0.98
0.90
0.92
0.79
0.75
0.89
83,72
OmniGen2
7B
1.00
0.95
0.64
0.88
0.55
0.76
0.80
83.57
BLIP3-o*
8B
0.92
0.63
0.91
0.86
0.67
0.83
80.73
RECA
1.5B
0.97
0.76
0.94
0.91
0.83
88.15
2.3
DIFFERENCE BETWEEN RECA AND PREVIOUS WORKS
Previous works integrate image reconstruction in different ways: (I) diffussio--spperiiee enhance- ment (e.g, ViLex) (Wang et al., 2025d; Ma et al., 2025a; Luo et al., 2024; Wang et al., 2024b) lever- ages pretrained diffusion models regularize vision encoders and improve visual understanding; (II) reconstruction from hidden states (e.g., ROSS) (Wang et al., 2024a; 2025b)) adds lightweight decoders to reconstruct input images from intermediate embeddings, thereby regularizing the model to preserve fine-grained details for visual understanding: (III) representation alignment (e.g., REPA, VA-VAE) (Yu al., 2024; Yao al., 2025) introduces additional alignment modules that map hidden states in DiT (Peebles & Xie, 2022; Ma et al., 2024a) or VAE (Kingma & Welling, 2013) to representations obtained from external, pretrained vision encoders, and (IV) reconstruction as prior (e.g., Lumos) (Ma et al., 2025b) adds additional DINO features (Caron el al., 2021) 1) into the attention blocks of the diffusion model, which is further trained on large-scale scglescale text-to- text-to-image data.
RECA treats images dense prompts and adopts semantic-level reconstruction training objective for UMMs, requiring no auxiliary modules or text-to- sat-to-image data. RECA is fun- damentally different in terms of the methodology, architecture, motivation and task.
native post-
EXPERIMENTS
We validate our RECA method across various unified multimodal models (UMMs), image datasets, and evaluation benchmarks. particular, we investigate the following aspects:
SOTA Results: RECA achieves ffate-of-the-ar performance both image generation and edit- ing benchmarks. (Table 1, Table 3)
Generality: RECA delivers consistent performance gains across diverse UMM frameworks,
demonstrating its generalizability. (Table 2, Figure 6, Figure 7)
Robustness: RECA consistently improves generation capabilities across diverse datasets and benchmarks, indicating that the gains are not from training-date memorization. (Table 5) Training Paradigm: RECA serves as a post-training method applied after UMM pre-training, and is most effective when used at the final stage of model training. (Table 5, Table 6)
3.1 EXPERIMENT SETUP
Model architectures. We evaluate RECA across four open-source UMM architectures:
Show-o (Discrete) (Xie et al., 2025b) employs discrete token generation with MaskGIT paradigm (Chang et al., 2022), using CLIP (Radford et al., 2021) VQGAN (Esser et al., 2021) the understanding / generation encoder, evaluated x56x256 and 512x512 resolutions. Our main results adopt the und.-endestnn variant, while Appendix F.2 explores and analyzes the VOGAA-anderstandinng variant in detail.
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Table 2: RECA consistently improves image generation performance across unified multimodal models. We report results for the largest model/resolution variant of each architecture; results for smaller models and detailed WISE scores are provided in Appendix F.3.
GenEval
Model
RECA
DPG
WISE
Single
Two
Count
Color
Position
Attri
Overall
x 97.2
80.3
61.9
78.2
27.3
52.3
66.2
82.21
0.40
Show-o
/ 98.2
90.6 (+103)
66.8 (+49)
84.1 (+5.))
(+.0. 56.8 (+4.5)
(+..)
84.94
(a.00
x 99.1
71.8
51.9
83,9
23.3
41.6
61.9
79.02
0.43
OpenUni
/ 99.1 (o.))
92.7 (+20.0)
52.3 (40.4)
87.1 (43.2)
43.8
(420.)
70.3 (-28.)
74.1
(412.2)
82.75 (4373)
0.54 (40.11)
x 99.4
87.3
68.7
86.4
44.9
51.1
72.9
80,93
0.41
Harmon / 99.9 (+0.)) 97.7
(+10.4)
71.4
(+2.7)
92.6 (+6.2)
75.7 (+308)
76.6 (+25.5)
85.7
87.21 (+6.28) 0.50
x 99.1
93.0
79.9
86.0
51.3
63.4
78.8
84.03
0.50
BAGEL
99.3 (-0.2) 93.9 (+0.9) 80.3 (+0.4) 87.6 (-1.6) 60.8 (+9.5)
72.6 (+9.2)
82.4 (+3.6)
85.29 (+1.2) 0.52 (+0.02)
Table 3: Results image editing benchmarks. We compare RECA post-trained BAGEL with previous methods. All scores are local reproduced. The gray-colored rows denote private models, and their results are cited from (Deng et al., 2025; Ye et al., 2025).
ImgEdit
GEdit-Bench-EN
Method
Bg.
Style
Adj.
Ext.
Rem.
Rep.
Add Comp.
Act.
Ovr.
SC
PQ
Overall
Gemini 2.0
5.43
6.78
5.36
GPT-4o-Image
4.57
4.93
4.33
2,90
3.66
4.35
4.61
3.96
4.89
4.20
7.85
7.62.
7.53
BAGEL-NHI
3.56
4.43
3.62
1.57
3.17
3.98
4.12
2.88
3.85
3.48
8.04
6.87
7.08
BLUX-Kontexy
3.89
4.62
3.69
1.81
2.97
4.20
3.80
3,00
4.18
3.60
6.95
7.30
6.27
BAGEL
3.38
4.53
3.58
1.49
3.15
3.82
3.71
2.64
4.21
3.38
7.96
6,64
6,94
BAGEL-RECA
3.85
4.73
3.86
1.68
3.75
4.28
4,20
2.94
4.56
3.75
8.24
6.87
7.27
vs. baseline
+0.47
+0.20
+0.28
+0.19
+0.60
+0.46
+0.49
+0.30
+0.35
+0.37
+0.28
+0.23
+0,33
Harmon (MAR) (Wu et al., 2025d) adopts masked autoregressive generation, with MAE (Li et al., 2024) / MAE & VAE (Kingma & Welling, 2013) as the understanding / generation en- coder, evaluated 0.5B and 1.5B parameter scales.
OpenUni (Continuous) (Wu et al., 2025c) generates in continuous latent space via diffusion, with Intern VL3 (Zhu et al., 2025)/ / VAE (Kingma & Welling, 2013) as the understanding gen- eration encoder. It serves as an open- counterpart of MetaQueries (Pan et al., 2025b); we evaluate both 1.6B and 3.6B variants at 512 resolution without HEPT-4o-Image distillation. BAGEL (Continuous) (Deng et al., 2025) also employs continuous diffusion generation, with SigLIP (Zhai et al., 2023) VAE (Kingma & Welling, 2013) as the understanding generation encoder. We report the results of BAGEL at 1024x 1024 resolution.
Together, these models cover the main families of UMM generation mechanisms: discrete token prediction, masked autoregressive (MAR), and continuous diffusion.
Evaluation details. We evaluate texi-to-image generation on GenEval (Ghosh et al., 2023) and DPGBench (Hu et al., 2024), and image editing on ImgEdit (Ye et al., 2025) and -endhh EN (Liu et al., 2025). Our baselines include both generation-only models (e.g., SDXL (Podell et al., 2023), DALL-E 3 (Betker et al., 2023)) and unified multimodal models (e.g.. Show-o (Xie et al., 2025b), Harmon (Wu et al., 2025d), BAGEL (Deng et al., 2025), GPT-4o-Image (OpenAI, 2024)).
Training data. We post-train UMMs with high-quality open-source data, including Midjour- V6 (CortexLM, 2024), LLaVA Mix-665K (Liu et al., 2024b), and 10,000 FLUX FLUX-generated sam- ples (jackyhate, 2024). Further implementation, evaluation and dataset details are provided in Ap- pendix E. To ensure fair comparison, our main experiments exclude CPT-4o-Image distillation data like BLIP3o-60K, which can inflate benchmark scores due to GenEval Template Leakage. See Appendix F. for further discussion.
3.2 RECA ACHIEVES SOTA PERFORMANCE ON IMAGE GENERATION AND EDITING
SOTA performance without GPT-4o-Image distillation. As shown in Table 1, post-training the Hanmon- model with RECA achieves SOTA overall scores on GenEval and DPGBench. Despite using significantly fewer parameters, RECA surpasses recent methods, e.g., Janus-Pro (Chen et al., 2025d) and BAGEL (Deng et al., 2025).

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Published as a conference paper at ICLR 2026
Baseline
RecA
Baseline
RecA
Baseline
RecA
photo brown hot dog and purpla golcian book and steaamnng coffee
backpack below cake.
wooden clesk by the window
In the midst of vibrant garden, a cylindrical green cup stands
...coop red, perfectly round to matocs spil from basket rustic wooden tabletop...daff green leaves of nearby herb kitchen
A polished brown lcather briefcase with visible senching details rests Beside the briefease, a vibrant red fedora hat provides striking contrast
path, reflecting the afterneen sunlight The cup, with smooth fiiihh by blossoming Rlowers and lush greenery, is surrundeed. The shwrlows nearby
tablecloth..
plant... the blureed outline of window las natural light, casting gentle shadow....
the pristine table covwing The rable, placed room light beige walls...
plants dance on the
Figure 6: Image generation results vs. baselines. We use Harmon-1.55 I.5B baseline. The post- trained model better handles multiple objects, complex attributes and spatial layouts, preserving fine details missed by the baseline.
Origin
Baseline
Origin
Baseline
RecA
Make it snowy
Add farmer hat, scart and pair of sunglasses
ace the yachi the with hot
Transfer the into 8-bit pisel-art video-
balloon floating just ahove the orean surface.
game style.
Figure 7: Image editing results vs. baselines. We use BAGEL as baseline. Our model consistently improves performance object addition, replacement, style transfer, and scene modification.
Image editing results are also substantially improved. As shown in Table RECA consistently outperforms existing baselines across all subtasks and benchmarks. On ImgEdit, RECA achieves an overall score of 3.75, surpassing
L-X-Kottext (3.60) and the baseline (3.38). On GEdit-Bencht EN, RECA reaches 7.27, which closes the gap between open-source models and GPT-4o-Image. In particular, with only 10,000 unlabeled images and 27 GPU hours, RECA beats the concurrent work BAGEL-NHR (Kuprashevich et al., 2025) (3.48), which employs supervised fine-tuning (SFT) on 300,000 high-quality image editing datat.
3.3 RECA EFFECTIVE ACROSS DIFFERENT UMM FRAMEWORKS
Consistent performance gains across UMM architectures. As shown in Table 2, RECA demon- strates consistent and significant improvements across all evaluated UMM frameworks. We report results for the largest model variant of each architecture, with smaller model results provided in Ap- pendix F.3. On GenEval and DPGBench, the most substantial improvement is achieved by Harmon- 1.5B, which attains scores of 85.7 (+12.8) and 87.21 (+6.28), respectively.
Performance on WISE benchmark. WISE (Niu et al., 2025b) evaluates text-to-image reasoning with 1 1,000 knowledge-puzzie prompts (e.g., Einstein's 's favorite instrument). As shown in Table 2,

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Published as a conference paper at ICLR 2026
Dataset
SFT
RECA
GenEval
DPG
90
Midjourney V6
74.76
80.89
SFT
Midjourney V6 (dense)
V
74.05
80.67
MidjournyV66
85.69
87.21
85-
MidjournyVo
81.22
86.54
GenEval
80
BLIP3o-60k
V
84,95
85.19
BLIP3o-60k
85,21
86.50
BLIP3o-60k
86.14
85.96
BLJ13o-60k (w/o GenEval)
80.88
85.24
BLIP3o-60k (w/o GenEval)
84.76
86.37
2000
4000
6000
8000
10000
Training step
Table 5: Comparison of SFT and RECA across post-training datasets. RECA consistently out- performs SFT, especially when template leakage data excluded (yellow row), demonstrating that the improvements are genuine and robust. All results are reported at 5k steps. Right: GenEval overall scores of Harmon-1.5B with SFT vs. RECA across training steps.
RECA improves Harmon and OpenUni, while BAGEL and Show-o show limited gains. We conclude that RECA primarily improves semantic-level alignment, while reasoning ability remains an open problem for future work. Detailed WISE results are provided in Appendix F.3.
3.4 MORE RESULTS
Qualitative results. For image generation, the baseline often fails on multiple objects, complex altributes, and spatial layouts, and in Figure 6, the post-trained model preserves these fine-grained details well (e.g., kitchen window, sunlight). For image editing, Figure shows consistent im- provements across tasks such object addition, replacement, style transfer, and color modification. Additional examples are provided in Appendix and J.
Visual understanding results. We in- vestigate the effect of RECA on UMMs that share parameters between under- standing and generation (e.g., Harmon- RECA 1223
Table 4: Visual understanding performance.
Model MME POPE (Ase) POPE (F)) GQA MMMU SEED
Harmon 1195
83.8
83.9
58.8
34.7
65.2
83.9
83.2
58.4
35.7
65.3
1.5B). In Table 4, results POPE (Li al., 2023b), MME (Fu al., 2023), GQA (Hudson & Manning, 2019), MMMU (Yue et al., 2024), and SEED (Li et al., 2023a) show that RECA can im- prove UMMs' generation fidelity without degrading visual understanding. For architectures with decoupled understanding components (OpenUni, BAGEL), their capability remains unchanged.
3.5 EMPIRICAL STUDIES RECA AS POST-TRAINING METHOD
SFT vs. RECA as post-training methods. We directly compare supervised fine-tuning (SFT) with RECA as alternative post-trat training strategies for UMMs. Unlike SFT, which depends on text-image pairs, RECA is self- supervieee and requires only unlabeled images. Our key questions are: (i) which method is more effective, and (ii) when and how should RECA be applied relative to SFT?
RECA demonstrates genuine effectiveness pared with SFT. As shown Table 5, on Midjour- V6, SFT achieves 74.76 on GenEval. Adding denser captions via Midjourney-LLaVa (brivangl, 2024) does not help: GenEval remains nearly un- changed and DPGBench even drops a little. In contrast, replacing SFT with RECA dramatically boosts performance under the same setup (85.69/87.21). On the complete BLIP3o-60K dataset, RECA still outperforms SFT on both benchmarks. Notably, removing template leakage data leads to performance drop for SFT (-4.07 GenEval), whereas RECA maintains strong performance. The training dynamics the right panel of Table further demonstrate that RECA consistently outperforms SFT. While SFT performance stagnates, RECA rapidly ascends to superior level and maintains a stable advantage throughout the training process; the evolution of GenEval subtasks is provided in Appendix F.5.
Table 6: Training recipe. SFT RECA as sequential post-training stages.
RECA Dataset
Order
GenEval
DPG
B1IIP3--00k BLIP3o-60K
RECA + SFT
85.91
85.67
SFT RECA
89.00
87.50
Midjouney
SFT RECA
90.15
88.15
When to apply RECA? We also study training order to understand when to apply RECA relative to SFT. As shown in Table 5, mixed training (half SFT, half RECA per batch) is unstable: it slightly
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helps BLIP3o-60k but fails on Midjourney V6. If we do SFT on BLIP3o-60k, sequential train- ing is more decisive: as shown in Table 6, applying RECA after SFT (SFA))REAA) consistently delivers the best results (90.15/88.15 on Midjourny V6). Reversing the order (RECA -SFT de- grades performance (-3.09/-1.83) This asymmetry underscores that SFT provides broad text-image alignment, while RECA serves best refinement stage enhancing semantic grounding and visual faithfulness.
Visual understanding vs. visual generation encoder. Most UMMs employ two encoders: one for understanding (semantic features) and one for generation (pixel-level features). BAGEL follows this design, making it natural testbed for RECA. As shown in Table 7, conditioning on the generation encoder (VAE) yields only marginal or degraded gain,, while embeddings from the understanding encoder (ViT) produce consistently better results across GenEval, DPGiench, ImgEdit, and GEdit. This indicates that RECA benefits most from semantic embeddings that capture high-level concep- tual information, rather than raw visual details.
Different resolutions for the under- standing encoder. For Show-o, Har- mon and OpenUni, the input resolu- tions are fixed at 336 x 336, 512 x 512 and 448 x 448, respectively. BAGEL uses NaVIT (Dehghani et al., 2023) and flexibly accommodate arbitrary in- put resolutions. We perform experi- ments on 224 224 and 512 512 input resolutions for RECA, and the results in Table 7 show that the model post-trained on 224 x 224
Table 7: Vision encoder type and input resolution comparison. Embeddings from visual understanding en- coder with the lowest resolution are the best for RECA.
Vision Encoder Resolution GenEval DPG ImgEdit GEdit
Baseline
78.8
84.03
3,38
6.94
VAE (gen.)
256 256
78.5
83.92
3.63
7.08
VIT (und.)
224 224
82.4
85.29
3.75
7.27
VIT (und.)
512 x 512
79.2
84.61
3.68
7.18
consistently outperforms its 512 x 512 counterpart across all benchmarks. Prior work shows that embeddings retain substantially more pixel-level detail (Allakhverdov et al., 2025; Lin et al., 2025), which in turn drives the model to lean on low-level cues rather than the semantic features that RECA is designed to emphasize.
Conclusion. Across data and strategies, RECA consistently proves to be a stronger post-training method than SFT. The best post-fraining recipe is two-stage pipeline: first SFT on high-quality paired data for coarse alignment, followed by RECA for self-supervised fine-grained refinement.
RELATED WORK
4.1 UNIFIED MULTIMODAL MODELS (UMMS)
Unified multimodal model (UMM) is a backbone capable of multimodal understanding and gener- ation within single model or system (Zhang et al., 2025b). Recent studies have explored different types of UMMs: (I) Discrete UMM. Models like Chameleon tokenize images and predict them au- toregressively in next-token prediction way (Team, 2024; Qu al., 2024; Chen al., 2025e; Wu et al., 2024a), while Show-o (Xie et al., 2025b) introduces a discrete-diffasion schedule to improve the token prediction process. (II) Continuous UMM. Models like Transfusion attach a diffusion (or flow-matching) (Ho et al., 2020; Lipman et al., 2023) head to the shared transformer (Zhou et al., 2025; Ma et al., 2024b), while some UMMs keep pretrained MLLMs frozen and route its interme- diate features with learnable queries to an external image generator (Pan et al., 2025b; Lin et al., 2025). (III) MAR UMM. Wasked-autoregreen image generation paradigm, which has been adopted by models like Harmon (Wu et al., 2025d; Fan et al., 2025a; Yang al., 2024b; Wang et al., 2025c).
(MAR) (Li et al., 2024) is a novel autoregressive
4.2 POST-TRAINING STRATEGIES FOR UMMS
Recent efforts explore different post-training techniques for enhancing the generation capability of UMMs. (I) Chain-of-Thought (Wei et al., 2022) or test-time verification do reasoning before the generation or verify generated image step-by-step, but they depend on external models and do not improve the UMM's native generation capability. (Guo et al., 2025; Wang et al., 2025e; Fang et al., 2025; Tian et al., 2025). (II) Reinforcement learning. Methods such as DPO (Rafailov et al., 2023) and GRPO (Shao et al., 2024) optimize generation policies with human or automatic preference signals, but require curated paired data and carefully tuned advantage functions. (Wei

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yo
+0 RECA
STARTS
O
Figure 8: Qualitative T2I results. The large images (1024 x 1024) are generated by the post-trained BAGEL, while the small images (512 x 512) are generated by the post-trained Harmon. Detailed captions are listed in Appendix H.
criess blueberies,
pluch ligura fuzzy fabne
alices the stack
Shed neon light on the scone.
man AaB ing the fromatio
Perriove the heather on the faal.
Tarn on the Rashlight on the smartphane.
Figure 9: Qualitative image editing results. Edited outputs are generated by BAGEL post-trained
with RECA. Left: original images; Right: edited images.
al., 2025; Han al., 2025; Yan et al., 2025a; Mao et al., 2025; Tian al., 2025; Jiang al., 2025). (III) High-quality synthetic data. Recent works (Chen et al., 2025b; Wang et al., 2025f) construct large-scale GPT-4o- Image (OpenAI, 2024) generated image-text pairs for supervised fine-tuning, which boost benchmarks but incur high aata-generation cost and potential distribution shift,
5 CONCLUSION
In this work, we propose RECA, a lightweight post-training method that replaces sparse text-to- image supervision with dense features from the model's visual understanding encoder. RECA requires no extra caption data yet significantly improves image generation and editing across archi- tectures. We discuss the limitations and future directions in Appendix G.
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REPRODUCIBILITY STATEMENT
We ensure reproducibility by providing all training and evaluation hyperparameters, along with full implementation details, in Appendix E. These materials are sufficient to replicate our experiments. Detailed prompt templates are included the Supplementary Material. In line with our commitment to open-source research, we will make our codes and model weights publicly available.
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We provide additional information in the supplementary material, as outlined below:
Sec. A: Brief introduction to different image generation paradigms.
Sec. B: Difference Between RECA and Classifier-Free Guidance (CFG).
Sec. C: Prompt templates for RECA.
Sec. D: Motivation and Mechanism Analysis of RECA.
Sec. E: Further implementation details for RECA implementation and evaluation.
Sec. F: Additional experimental results and analysis.
Sec. Limitations and future work for RECA.
Sec. H: Generated captions for figures in the DEMO.
Sec. I: Additional qualitative results on image editing tasks.
Sec. J: More uncurated qualitative examples for text-to-image generation.
Sec. The Use of Large Language Models (LLMs).
A PRELIMINARIES OF IMAGE GENERATION PARADIGMS
A.1 DISCRETE PARADIGM
Discrete paradigms use image tokenizer (Van Den Oord et al., 2017; Esser et al., 2021; Chang et al., 2022) and treat images as sequence of discrete tokens, predicting each token in turn. The joint likelihood factorizes as:
N II
p(x)
po (2: xex),
CAR E Eezopaata
log (x; xex),
where the model is trained by minimizing the cross-entropy cross-entropy of each next-token prediction. At sam- pling time, tokens drawn sequentially (æ, p=(x1), 22 pe po(x') 21), etc.). Models such as Chamelcon (Team, 2024) and Janus (Chen et al., 2025c) append onto LLM's LM head.
MaskGIT (Chang et al., 2022) extends the discrete paradigm by parallel prediction with random masks. Given a tokenized image sequence y .,N),,,,,), where N is the number of discrete tokens obtained from image tokenizer's codebook of size K, and binary mask vector (mi,... m with m, indicating that position is masked, the model is trained with the masked prediction loss:
N
Fmask = Ey m
m, log Po(ys M)
(5)
where y denotes the input sequence with masked tokens replaced by a special [MASK] symbol. During inference, the model starts from fully masked input and iteratively fills it with predicted image tokens. At step I, the model predicts distributions pt Po (yr M. for all currently masked
positions, where p; (t) is categorical distribution over the K image tokens. The confidence score is defined as ci max, Ply, i.e., the maximum probability across categories. A scheduling function (t/TT), where T' is the total number of refinement steps, controls the number of tokens to remain masked:
n4 = [N (1/TT),
(1+1) v8 () < the ni,th smallest confidence m
(6)
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This process progressively decreases the mask ratio until the full image token sequence is generated. In contrast to autoregressive next-token prediction, MaskGIT decodes multiple tokens in parallel with bidirectional context, achieving faster sampling and higher generation fidelity. UMMs such as Show-o (Xie et al., 2025b) use this paradigm.
A.2 CONTINUOUS PARADIGM
The most representative method in the continuous paradigm is diffusion (Sohl-Dickstein et al., 2015; Ho al., 2020), which learns gradual noising process from Gaussian distribution. The forward (diffusion) process progressively corrupts clean image Xo Páata into pure Gaussian noise Xy:
<(1 x(-1) -N(x v 3:x1-1 B(T),
x(x xo) Nixx; von xo: (1-it)) [T)
where 3e (0, 1) are pre-defined hyperparamters, aa = Br, and ir II Os. The model
learns the reverse denoising distribution:
Po xt- xx) M o.
(8)
and is trained with the noise prediction loss:
Liif Exo,e,t,2 [lle (xxx1,,))11
(9)
Xx = 0. - -re, e N(011.
Here (x,,,,,, is the predicted noise given the noisy input x,, timestep l and condition signal Generation starts from Xy ~ N(0, I) and applies the learned reverse process until Xp is recovered.
Instead of discrete noising steps, flow-matching (Lipman et al., 2023) defines continuous trajec- tory (xJooejo.1 that transports a prior distribution (e.g. Gaussian noise at = 0) into the data distribution P1 (t = 1). In ordinary differential equation (probability flow ODE):
di x(1)
(o(x))t,t,,),
(10)
where uo is a neural velocity (vector) field. The ground-truth target field w"(x,, f, 2) can be derived from an optimal coupling between po and Pt, and training minimizes:
Lnow Ex.t. [elll (x, t, 2) u u'(xx"x t, 2]
(1I)
Rectified flow (Liu et al., 2022) simplifies this by defining linear interpolation:
x(t) (1-t)x(0) tt, c~,(),
(12)
so that the target velocity field is available in closed form:
d u (x(t), 222) dt x())=
(13)
Diffusion adds Gaussian noise at each step with variance 3r, whereas rectified flow directly defines continuous interpolation between data and noise. UMMs such BAGEL (Zhou et al., 2025), Janusflow (Ma et al., 2024b) and Metaqueries (Pan et al., 2025b; Wu et al., 2025c) append these paradigms above onto LLM.
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UMM Input
Supervised Output
<|mm_ starl|=system'n You are a helpful
Image captioning: This image depicts a beautiful town and canal, surrounded colorful flowers..
assistant. <lim_ indl=inslim starr]>userin
Reconstruction Alignment:
Describe this image in detail. <lim end|>in<lim fand>iin(im_starlpaaae star|]>assistant
Figure 10: Visual embedding integration in RECA. The left side shows the UMM input for- mat, where visual understanding embeddings (represented as image patches) are concatenated with text prompts following standard image captioning format. The right side contrasts two supervision paradigms: image captioning uses sparse text descriptions as supervision, while RECA uses the original images themselves as dense reconstruction targets.
A.3 MASKED AUTOREGRESSIVV (MAR)
MAR (Li et al., 2024) is a mask-based generation paradigm similar to MaskGIT, but it generalizes AR without vector quantization. Specifically, MAR employs diffusion decoder generate images with continuous tokens in the transformer.
Inference also differs from MaskGIT: (I) MAR predicts continuous features instead of logits discrete codes, and (II) while MaskGIT fills the most confident logits, MAR stochastically samples denoised patches at each step. UMMs such as Fluid (Fan al., 2025b) and Harmon (Wu et al., 2025d) follow this design.
B DIFFERENCE BETWEEN RECA AND CLASSIFIER-FREE GUIDANCE (CFG)
Classifier-free guidance (CFG (Ho & Salimans, 2022)) typically used improve image gener- ation fidelity. At each generation step, we compute conditional prediction Qoond and an uncon- ditional prediction Ouncond- where denotes either the autoregressive head's logits the diffusion head's predicted noise, as we introduced in Sec. A. The output is given by:
(1 + w) Ocond w Ouncond:
(14)
with the guidance scale. RECA is conceptually orthogonal to CFG. Whereas CFG relies on contrast between a conditional and null-text (or template) prompt, RECA leverages embeddings from the visual understanding encoder as dense prompts for reconstruction-baste alignment. The two techniques are fully compatible and can applied together.
C PROMPT TEMPLATES FOR RECA
C.1 VISUAL EMBEDDING INTEGRATION
The integration of visual understanding embeddings in RECA follows the standard multimodal input paradigm used in modern MILMs for image captioning tasks. As illustrated in Figure 10, the input sequence consists of:
Standard instruction tuning template (e.g.. < lim start >system) are a hulpful essistant. im end|
2. Tokenized image representations from the visual understanding encoder (e.g., CLIP, SigLIP).
template triggering image description or reconstruction.
4. Trigger (e.g., < iimsstarttt >assistants
During RECA training, the model is conditioned on the concatenation the text template and visual embeddings, and trained to reconstruct the original image. This process is identical to how MILMs
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Layer
Layer
laper? 2
Layer
Layer
loper
Layer
Layer?
Layer
Layer
Layer 12
Layer
Layer
Lapor
Layer
Layer
Laper
Layer
Layer
Figure 11: Deep Feature Alignment. PCA (Abdi & Williams, 2010) visualization of text embed- dings (red mark, the word "black") and image embeddings (a photo of black dog) across model layers. In deep transformer layers, text and visual features share the same semantic manifold. This indicates that visual embeddings effectively function dense, language-aligned text prompts.
process images for captioning tasks, except the supervision signal comes from image reconstruction loss rather than text generation loss.
C.2 PROMPT TEMPLATE DIVERSIFICATION
For template diversification RECA post-training, we utilize GPT-o3 to generate 360 distinct prompt templates based the seed template "Describe the image in detail." This diversification prevents the model from overfitting to a specific prompt format and ensures robust performance across various instruction phrasings. Below representative examples from template collec- tion:
Provide a detailed description of the image."
What you observe in this image? Please describe it comprehensively."
"Analyze and describe the visual elements present this image."
"Give a thorough description of what you see the image."
"Examine the image carefully and provide a detailed account."
Describe the contents of this image in detail."
"What does this image show? Please provide comprehensive description."
"Offer detailed visual analysis of the image."
Descriib all the visual elements you can identify in this image."
"Provide in-depth description of what is depicted in the image."
The complete collection of 360 templates varies in linguistic structure, complexity, and prompting style while maintaining the core objective triggering detailed image descriptions.
D MECHANISTIC ANALYSIS OF RECA
In this section, provide deeper analysis of the mechanism behind semantic reconstruction alignment (RECA). We investigate how the model processes visual information as dense prompts and the correlation between reconstruction and generation capabilities.
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The image captures a Shiba Inu dog. known for its fox-llke appearance spinted personality...
The image presents single, vibrant pink shoe against stark white background...
Dense Image Prompt
photo truck and microwave
Input Image
Generated Caption
Reconstructed Image
Sparse Prompt
(a) Reconstruction reflects internal semantic projection.
(b) UMM's Reconstruction correlates with gen- eration capability. Top: Provided with the dense image prompt, the UMM consistently fails re- construct the microwave across multiple seeds. Bottom: Consistent with the reconstruction fail- ure, the text prompt also fails generate the mi- crowave. This suggests strong correlation be- tween two tasks.
Top: The UMM extracts semantic features (c.g, "fox-
like") from the imput Corgi and resamples visually consis-
tent image. Bottom: For an anomalous concept (pink ba-
nana), the UMM maps the visual input to the semantic con-
cept of a "pink shoe" and then faithfully produces a shoe.
This confirms that generation is driven by the model's in- ternal semantic interpretation.
Visual- Textual Feature Isomorphism. A fundamental premise of RECA is that visual embeddings from the understanding encoder can serve as dense supervision for the generator. As shown in Figure 11, in the deeper layers of the transformer (Layers 3 ~ 20), text features (e.g., the word "black") and image features (e.g., a photo of black dog) aligns. This suggests that visual and textual information is effectively isomorphic in the semantic space, allowing us to use images as dense prompts to supervise the generation backbone.
Internal Semantic Projection. As shown in Figure 12a, We observe that the UMM treats input images as semantic prompts rather than raw pixels. For standard inputs (e.g., a dog), it performs semantic resampling. Notably, for rare inputs like pink banana, the UMM projects the visual features onto its nearest learned semantic concept, in this case, a "pink shoe". Crucially, it faithfully produces shoe based on this internal mapping. This confirms that images interpreted as text-like semantic concepts, and the UMM acts upon this interpretation.
Correlation Between Reconstruction and Generation. Furthermore, we find strong empirical correlation between reconstruction and generation. As shown in Figure 12b, we test a challeng- ing composition ("microwave and truck"). (I) When the UMM is conditioned on the dense image prompt, it persistently fails to generate the microwave across multiple random seeds. (II) Corre- spondingly, when conditioned the sparse text prompt (T2I), it also fails to generate.
This suggests that if the model cannot resolve a concept given dense visual cues, it is unlikely to succeed with sparse textual cues. Therefore, enhancing the model's s ability to reconstruct complex details serves as a direct pathway to improving its generation capabilities. RECA works by refining this mapping process.
E EXPERIMENTAL SETUP
Implementation Details. We post-train all models on single NVIDIA A100 (80 GB) GPU. Table 8 presents the detailed byper-parameter configurations for all models, including Show-o (256"/5122), Harmon (0.5B/1.5B), OpenUni (1.6B/366B), and BAGEL (14B). The table includes optimization settings, trainable modules, and loss weights used during RECA post-training. Hyper-parameters not listed in Table 8 follow the original open-source baselines. All Azi is set to 0.
For experiments that report averaged metries (rows marked with in Table 1), we run 12 repetitions using random seeds 1, 11); other experiments default to seed 0 unless otherwise stated. At inference time, we execute Show-o, Harmon, and OpenUni on an NVIDIA RTX 4090 (24 GB), while BAGEL inference remains on the A100.
Evaluation Details. Following previous work, we evaluate eext-to-image generation capabilities using GenEval (Ghosh et al., 2023), DPGBench (Hu et al., 2024) and WISE (Niu et al., 2025b). Our baselines include: (I) Generation-only models: SD3-Medium (Esser et al., 2024), SDXL (Podell al., 2023), SA5 (Xie et al., 2025a), Emu3--Ge (Wang al., 2024c), FL.UX-dev (Black-
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Table 8: Fine-tuning hyperparameter setup.
Show-o
Harmon
OpenUni
BAGEL
256
512
0.5B
1.5B
1.6B
3.6B
14B
Optimization
Optimizer
AdamW
AdamW
AdamW
AdamW le-5
AdamW
AdamW lc-5
AdamW
Learning rate
5e-7
5c-7
le-5
le-5
4e-5
B
(0.9, 0.999)
(0.9, 0.999)
(0.9, 0.95)
(0.9, 0.95)
(0.9, 0.95)
(0.9, 0.95)
(0.9, 0.95)
weight decay
0.01
0.01
0.02
0.02
0,05
0.05
0
warmup steps
1000
1000
500
500
50
50
1000
Training steps
5K
5K
3K
5K
5K
5K
IK
Grad. accumulation
Per-GPU batch size
2
96
48
42
20
8 (6 GPUs)
Training time (hours)
4
9
7
12.5
2
4.5
Trainable modules
Frozen parts
CLIP
MLLM
Und. Expert
Loss weights
Azi
1.0
1.0
0.0
0.0
ARECA
1.0
1.0
1.0
1.0
Forest, 2024), Playground-v3 (Liu et al., 2024a), and DALL-E 3 (Betker et al., 2023). (II) Unified maltimodal models: Show-o (Xie al., 2025b), Harmon (Wu et al., 2025d), Janus-pro (Chen al., 2025d), OmniGen2 (Wu et al., 2025b), BLIP3-o (Chen et al., 2025b). BAGEL (Deng et al., 2025), Show-o2 (Xie et al., 2025c). UniWorld-VI (Lin et al., 2025). Ovis-U1 (Wang al., 2025a), and GPT--4-Imaae (OpenAI, 2024). For enhanced statistical reliability, we reproduced some models using 12 random seeds (tripling the standard seeds).
For image editing evaluation, we employ ImgEdit (Ye et al., 2025) and 2025) benchmarks. We exclude GPT-4o-Image and its distillation-rrained models due to poor iden- tity preservation. We select BAGEL (Deng al., 2025), and FLUX-Kontex, (Labs et al., 2025) as our primary baselines. Notably, we compare with the concurrent work BAGEL-NHR (Kupra- shevich et al., 2025), which employs supervised fine- fine-tuning (SFT) using 300,000 high-quality im- age editing data. Due to changes in the GPT API version and benchmark maintenance issues, the leaderboard scores exhibit significant variance and difficult to reproduce. Therefore, follow BAGEL (Deng et al., 2025) and report our own local evaluations for consistency. Following the original benchmark, we use 1pt-4.1-2025-04-11 for ImgEdit and GEdit-Bench-EN evaluation.
(Liu et al.,
Training Data. Due to limited availability of UMM training data, we use the high-quality open- source Midjourney dataset (CortexLM, 2024) (MIT License) for Show-o, Harmon, and OpenUni. For BAGEL, initial experiments using Midjourney data led to significantly higher initial losses and degraded image quality, which we attribute to distribution mismatch because Midjourney V6 images are outside BAGEL's original training distribution. Therefore, we use 10,000 1024 x 1024 FLUX-generated (BleckForest, 2024) images from Text-to-Image-2M (jackyhate, 2024) (MIT Li- cense), as BAGEL was originally trained on FLUX FLUX-generated images (Deng et al., 2025), ensuring better distribution alignment and training stability.
As we discussed in Sec. 2.2, to preserve visual understanding capabilities of UMMs with shared pa- rameters for both understanding and generation (Show-o and Harmon), we also incorporate LLaVA Mix-665K (Liu et al., 2024b) (CC BY 4.0); for BAGEL and OpenUni, set Aga 0.
GPT-4o-Image distillation data (e.g., BLIP3o-60K) can boost GenEval and DPGBench scores by 5~7% on average (Chen et al., 2025b). However, these datasets contain GenEval prompt templates, creating evaluation bias which we call GenEval Template Leakage. In our primary experiments, deliberately avoid BLIP3o-60k to ensure fair comparison. We provide detailed analysis of this leakage issue in Sec F.I.
F MORE EXPERIMENTAL RESULTS AND ANALYSIS
We present additional qualitative sat-to-image generation and image editing results in Figure 8 and Figure 9. The detailed captions of T21 results listed in Appendix H.
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a photo of giraffe and baseball glove
photo of banana
photo of green toaster photo of a blue apple
photo of four trains
GenEval template leakage in BLIP30-60k. Examples from BLIP3o-60K that mirror GenEval templates, creating evaluation bias for models trained on this dataset.
Figure 13:
F.1 GENEVAL TEMPLATE LEAKAGE
GenEval (Ghosh et al., 2023) is a text-to-image evaluation benchmark which includes six sub-tasks: Single Object, Two Objects, Color, Color Attributes, Counting, and Position. The templates for these six sub-tasks are as follows:
Single Object: a photo of {object].
Two Objects: a photo of a {objectl] and {object2].
Color: a photo of a (color) fobject].
Color Attri.: photo of {color} [object1] and a {color) (object2).
Counting: photo of {count} {object).
Position: a photo of {object} (spatial relationship) [object].
However, we find that the BLIP3o-60K dataset (Chen et al., 2025h), distilled from GPT-4o-Image, contains 7,000 text-image pairs that nearly duplicate these templates. shown in Figure 13, many training examples align closely with GenEval prompts, raising serious risk of evaluation con- tamination. Models trained on BLIP30-60K may gain unfair advantages by having effectively seen test-like data during training.
To ensure fair comparison, we evaluate both the full BLIP3o-60k dataset and cleaned version with these 7,000 pairs removed. With the full set. SFT attains inflated GenEval score of 84.95, but after removal drops to 80.88 (-4.07). In contrast, DPGBench remains unchanged (+0.05), confirming that these pairs only boost Geneyya--ppccffcc performance rather than general generation capability. Thus, BLIP30-60K reflects sanchmark-targeted instruction tuning rather than a universal solution.
RECA achieves comparable performance and superior robustness. Using the full BLIP3o-60k dataset, RECA reaches 85.21 on GenEval, matching the benefit of SFT while requiring only unla- beled data and self-supervision. Crucially, on DPGBench RECA surpasses SFT (86.50 vs. 85.19), despite BLIP3o-60N containing dense- dense-prompt data favorable to SFT. When 7,000 data are removed, RECA declines by only 0.45 and retains its DPGBench score. This robustness underscores that RECA provides generalizable alignment signal, avoiding benchmark leakage and overfitting.
F.2 RECA ON SHOW-O WITH IMAGE TOKENIZATION
Naive 512 x 512 reconstruction collapses. When we do RECA on the VQGAN variant of Show-o 512 512 resolution, which uses VQGAN (Esser al., 2021) image tokenizer as the visual understanding encoder, the cross-entropy loss of RECA rapidly drops to nearly zero and the model's performance degrades, as shown in Figure 14. We observe that the model simply copies tokens from the input sequence instead of learning meaningful conditioning. To mitigate this token-copying behavior, we explore three strategies:
Resizing input images to 256 x 256, which aligns with the configuration Sec. 2.2 and feeds the visual encoder its minimum supported resolution.
Resizing input images 128 128.
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10
Setting
GenEval
DPG f
WISE
8
Baseline (no RECA)
68.70
83.63
0.40
6
512x512
66.59
83.55
0.30
4
256x256
70,61
84.77
0,40
2
128x 128
67.47
83.22
0.39
Step
Blur
72.20
85.12
0.38
1k
2k
3k
4k
Figure 14: Token-copying collapse and mitigation for Show-o's VOGAAN-understandinn vari- ant. Left: Cross-entropy loss plunges after 3,000 steps when training at 512 x 512. Right: Quanti- tative results of Show-o under different reconstruction inputs.
Blurring input images by downsampling it by 8x and then upsampling back.
The blurred reconstruction method yields the highest GenEval and DPGBench scores but slightly deteriorates WISE reasoning accuracy. Resizing inputs to 256 256 offers the best balance across all metries. In contrast, 128 128 inputs fall outside Show-o's pretraining distribution and significantly reduce all of these scores.
Why the gain of VQGAN variant is smaller than that of CLIP variant? Notably, the tokenizer variant starts from higher performance (68.7/83.6
66.2/82.21), yet after RECA the 256x reconstruction reaches only 70.61/84.77 compared with 72.3/84.94 .4 for the Show-o CLIP variant. This gap is consistent with the weaker semantic representations provided by the VQGAN tokenizer and the lower visual understanding capacity of the Show-o tokenizer variant, leaving RECA with less high-level signal leverage.
F.3 COMPREHENSIVE QUANTITATIVE RESULTS
We provide comprehensive quantitative analysis RECA across all evaluated unified multimodal models in this section. Table shows the full comparison between RECA post-trained Harmon-1.35 1.5B and previous models. Table 10 shows RECA'S consistent performance improvements across all eval- uated architectures, with particularly notable gains in positional understanding and color attribution tasks. The detailed WISE benchmark results in Table 1l reveal that RECA primarily enhances se- mantic alignment capabilities while showing modest improvements in reasoning-intenen tasks.
F.4 ADDITIONAL COMPOSITIONAL EVALUATION ON T2I-COMPBENCI
To further substantiate the robustness of our evaluation framework and address concerns regarding the complexity of the compositional tests, conducted additional rigorous analysis utilizing the T21-CompBench (Huang et al., 2023). T21-CompBench
specialized dataset comprising 2,670 carefully curated test samples designed systematically evaluate the compositional under- standing capabilities of text-to-image models across 10 distinet compositional dimensions. While GenEval primarily focuses on four fundamental attributes (object presence, count, color, and 2D po- sition), T21- r21-CompBenee systematically assesses comprehensive range of high-level compositional attributes and relationships. Specifically, T21-CompBench includes critical capability dimensions absent or underspecified in simpler benchmarks, such as:
Shape & Texture: Dedicated tests for understanding diverse geometric forms (c.g., "a pentag- onal stop sign"") and material properties (c.g.. "a plastic toy").
Numeracy: Precise control over object quantities (e.g., "six airplanes").
3D Spatial: Crucially, the benchmark distinguishes between 2D spatial and 3D spatial reason- ing, with the latter focusing specifically on depth perception and occlusion relationships (e.g., "a chair hidden by a turtle") > key capability for evaluating complex scene understanding.
Complex Scenes: High-level tasks like complex scenes and complex action/spatial scenarios evaluate models' generalization capability in situations involving multiple intertwined attributes,
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Table Results on GenEval and DPGBench. Scores marked with (*) are our reproduced results using 12 random seeds. We use Harmon-1.5B as our base model and post-train it with RECA. The gray-colored rows denote private models, and their results are cited from (Yan al., 2025b; Geng et al., 2025), Arrows (1) denote that higher is better.
GenEval t
DPGT
Model Params Single Obj.
Two Obj.
Counting Colors Position Color Attri. Overall
Score
Generation Only Models
SD3-Medium
2B
0.99
0.94
0.72
0.89
0.33
0.60
0.74
84.08
SDXL
2.6B
0.98
0.74
0.39
0.85
0.15
0.23
0.55
74.65
SANA-1.5
4.8B
0.99
0.93
0.86
0.84
0.59
0.65
0.81
84.70
Emu3-Gen
8B
0.98
0.71
0.34
0.81
0.17
0.21
0.54
80.60
FLUX-dev
12B
0.99
0.85
0.74
0.79
0.21
0.48
0.68
84.00
Playground-v3
24B
0.99
0.95
0.72
0.82
0,50
0.54
0.76
87.06
DALL-E3
0.96
0.87
0.47
0.83
0.43
0.45
0.67
83,50
Unified Multimodal Models
GPT-4o-Imas
0.99
0.92
0.85
0.89
0.74
0.71
0.84
86.23
Show-o*
1.3B
0.98
0.84
0.67
0.82
0.30
0.52
0.69
83.63
Harmon
1,5B
0,99
0.87
0.69
0.86
0.45
0.51
0.73
80.93
Show-o2
7B
1.00
0.87
0.58
0.92
0.52
0.62
0.76
86.14
Janus-Pro
7B
0.99
0.59
0.90
0.79
0.66
84.33
BAGEL*
14B
0.99
0.93
0.80
0.86
0.51
0.63
0.79
84.03
RECA
1.5B
1.00
0.98
0.71
0,93
0.76
0.77
0.86
87.21
Unified Multimodal Models Trained with GPT-4o Data
Ovis-U1
3.6B
0.98
0.98
0.90
0.92
0.79
0.75
0.89
83.72
OmniGen2
7B
1,00
0.95
0.64
0.88
0.55
0.76
0.80
83.57
BLIP3-o*
8B
1,00
0.92
0.63
0.91
0.86
0.67
0.83
80.73
UniWorld-Vi
V1
20B
0.99
0.93
0.79
0.89
0.49
0.70
0.80
RECA
1.5B
1.00
0.97
0.76
0.94
0.91
0.83
0.90
88.15
Table 10: RECA brings consistent performance gains to various UMM frameworks. We show the performance gains after applying RECA to different unified multimodal models. All models are evaluated on GenEval, DPGBench, and WISE benchmarks.
GenEval
Model
RECA
DPG
Single
Two
Count
Color
Position
Attri
Overall
X
97.4
63.3
52.1
82.3
14.2
30.3
56.6
70.65
Show-o-256
/
97.4 (0,0)
73.6
(+10.3) (6.0 (+39)
83.8 (+1.5) 20.3 (-61)
40.2 (-99)
61.9 (+5.))
75.70 (+505)
97.2
80.3
61.9
78.2
27.3
52.3
66.2
82.21
Show-o-512
98.2
(+1.))
90.6 (.10.3)
66.8 (14.)
84.1 (+5.9)
37.4
(-10.1
56.8 (+4.5)
72.3 (.6.1)
84.94 (+2.73)
X
96.8
63.3
46.4
80.1
18.5
30.8
56.0
76.29
OpenUni-1.66 1.6B
/
97.1
(40.3)
84.3
(.21.0)
57.4 (+11.0)
83.5
(43.4)
44.3
(.25.)
56.0
70.4
80.45 (44.16)
X
99.1
71.8
51.9
83.9
23.3
41.6
61.9
79.02
OpenUni-3.6B
99.1 (0.8)
92.7
(+20.9)
52.3 (40.4)
87.1
(43.2)
43.8
(-20.5)
70.3
(-28.7)
74.1 (-12.2)
82.75 (+3.33)
x
99.7
80.5
55.8
86.7
32.2
49.7
67.6
80.12
Harmon-0.5B
99.9
(+0.2)
92.3 (+11.8)
59.4 (+3.6)
91.7
(+5.0) (+26.3) (-26.3)70.7
70.7
(=21.0)
78.7 (+11.1)
84.67 (+4.5)
X
99.4
87.3
68.7
86.4
44.9
51.1
72.9
80.93
Harmon-1..
/
99.9
(+0.5) 97.7 (+10.4)
71.4 (+2.7)
92.6 (+6.2)
75.7
(+308) 76.6 (+25.5)
85.7 (+12.8) 87.21 (+6.28)
x
99.1
93.0
79.9
86.0
$1.3
63.4
78.8
84.03
BAGEL
99.3 (40.2)
93.9 (10.9)
80.3 (10.4) 87.6
(+1.6) 60.8
(-9.5) 72.6 (-9.2)
82.4 (43.6)
85.29 (+1.26)
objects, and abstract relationships (e.g., "The red hat was top of the brown coat rack" simul- tancously testing color binding, spatial relationships, and object recognition).
Following SRUM (Jin al., 2025), we employ Ow.-2VL.2255 (Bai al., 2025) as the designated multimodal evaluator. The results presented in Table 12 validate the effectiveness of RECA across all tested UMM architectures, consistently delivering substantial performance gains. Crucially, the re- sults T2l-CompBench serve powerful complement to GenEval findings. While GenEval confirms alignment fundamental attributes (Color) and 2D positioning. 121-CompBennh demon- strates that method's improvements generalize much broader spectrum of visual semanties. (I) Beyond Color: The substantial gains in Texture and Shape (c.g., +10.38 on shape for Ope- nnni) prove that RECA refines the model's sensitivity all fine-grained visual attributes, not just.
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WISE benchmark results. Performance on reasoning- baased text-to-image generation. Harmon and OpenUni exhibit zero-shot improvement but Show-o and BAGEL do not.
Table 11:
Model
RECA
Cultural
Time
Space
Biology
Physics
Chemistry
Overall
X
0.27
0.38
0.45
0.26
0.37
0.24
0.33
Show-o-256
V
0.28 (J0.1)
0.40 (.0,2)
0.46 (-0.1)
0.27 (.0.0)
0.38 (-0.1)
0.24 (0.00)
0.34 (40.01)
Show-o-512
x
0,31
0.42
0.55
0.31
0.48
0.32
0.40
0.31 10.00)
0.42 (0.00)
0.56 (+0.11)
0.32 (-0.01)
0.50 (-0.22)
0.30 (-0.2)
0.40 (0.00)
x
0.40
0.41
0.54
0.35
0.52
0.36
0.43
OpenUni-1.66
0,40 (0.00)
0,46 (+0.05)
0.56 (-0,02)
0.42 (+0,07)
0.54 (+0.02)
0.34 (-0.22)
0,45 (+0.02)
X
0.36
0.38
0.59
0.43
0,50
0.34
0.43
OpenUni-3.6B
0.52 (+0.16)
0.51 (-0.13)
0.69 (-0.10)
0.50 (-0,07)
0.64 (-0.14)
0.40 (-0.06)
0.54 (+0.11)
X
0.28
0.39
0,41
0.31
0.38
0.22
0.33
Harmon-0.51
0.33 (+0.05)
0.43 (+0.04)
0.54 (=0.13)
0.38 (+0.07)
0,48 (+0.10)
0.24 (+0.82)
0.40 (+0.07)
X
0,38
0,48
0.52
0.37
0.44
0.29
0,41
Harmon-1.5B
/
0.44 (+0.06)
0.58 (-0.10)
0.57 (+0.55)
0.48 (-0.11)
0.58 (+0.14)
0.32 (+0.83)
0.50 (+0.09)
x
0.42
0.53
0.64
0.42
0.57
0.43
0.50
BAGEL
/
0.43 (+0.01)
0.51 (-0.02)
0.67 (+0,3)
0.46 (+0.04)
0.59
(-0.02)
0.46 (-0.03)
0.52 (+0.02)
T2I-CompBench Performance Analysis. categories before and after applying RECA.
Table 12:
Comparison of performance across sub-
OnenUni-3.6B
Harmon-1.35
Show-o-512
BAGEL
Category
Base
RECA
Base
RECA
Base
RECA
Base
RECA
Color
87.47
92.50 (45.03)
89.55
93.72 (44.17)
88.60
90.88 (+2.28)
93.05
93.62 (40.57)
Shape
75.22
85.60 (+10.38)
79.09
84.32 (+5.23)
77.68
82.22
(+4.54)
84.25
85.72 (+1.4))
Texture
82.87
89.65
(+6.78)
86.28
91.37 (+5.09)
85.23
88.37
(+314)
88.95
90.58
(+163)
Spatial
78.29
86.30
(18.01)
84.98
92.37 (47.39)
85.75
87.28
(+1.53)
88.42
88.57
(+0.15)
Non-Spatial
83.10
87.18
(14.08)
82.77
87.12
(44.35)
85.38
85.13 (4.25)
88.27
88.80
(40.53)
Numeracy
64.44
75.97
(+11.53)
69.19
79.78 (+10.59)
71.39
74.42 (+3003)
79.43
81.88
(+2.45)
3D Spatial
68.60
84.35
(+15.75)
75.58
86.08 (-10.50)
75.38
81.42 (+6.04)
81,47
83.50
(+2,03)
Complex
82.82
87.79 (14.7)
82.05
88.51 (46.46)
82.73
85.07 (+2.34)
87.85
88.74
(40.89)
Overall
78.84
86.49 (+7.65)
81,36
88.03 (46.67)
82.12
84.47 (-2.35)
86.74
87.89
(+1.15)
color binding. (II) Beyond 2D Layout: The remarkable improvements in 3D Spatial reasoning (e.g., +15.75 for OpenUni, +10.50 for Harmon) indicate that RECA effectively teaches the model to comprehend depth, perspective, and occlusion, far exceeding simple up/doww/left/rriht positional alignment. (III) In Complex Scenario, all models showed consistent gains (e.g.. Harmon-1.50 increased by +6.46 points). The only minor fluctuation was observed in the Non- Non-Spatial category for Show-o-512 (-0.25), which does not diminish the overwhelming positive trend across high- difficulty tasks.
F.5 GENEVAL SUBTASK DYNAMICS
How do individual GenEval subtasks evolve? Figure 15 shows that RECA primarily boosts per- formance within the first 3k 5k steps before convergence. Single Object and Two Objects success rates are already saturated and quickly flatten once they touch the ceiling. Color continues to climb through the entire run, indicating sustained improvements in attribute grounding. Position rises sharply before 5k steps and then stabilizes at its ceiling. Color Attributes accuracy peaks around 5k steps and then gently drops as we optimize for other objectives. Counting exhibits only minor fluctuations without a clear upward trend, matching the limitation we discuss in Appendix G. Un- derstanding how to balance different subtasks for finer finer-grained control is an important direction for future work.
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Single object
Two objects
Counting
100
100
80
70
95
60
8000
2000
6000
10000
Colors
Position
Attributes
100
80
80
70
70
90
60
60
50
80
40
50
0
2000
4000
6000
8000
10000
2000
4000
6000
8000
10000
0
2000
4000
6000
8000
10000
Figure 15:
GenEval subtask performance dynamic during RECA training. We plot the Harmon- 1.5B scores for each GenEval component at different training steps.
F.6 FURTHER DISCUSSION ON EXPERIMENT RESULTS
Why the improvements on Show-o is modest? (I) The CLIP used in Show-o exhibits insufficient high-level semantic information (Tong et al., 2024b), which limits the upperbound of RECA. (II) Show-o's codebook has only 4096 tokens. fundamentally limits its representational capacity and generation performance.
Why the text-to-image generation improvements on BAGEL modest? (I) The data and com- putational resources prevent the full exploitation of the potential benefits of RECA. (II) BAGEL's pre-existing image editing training. which conditions on both SigLIP (Zhai al., 2023) and VAE (Kingma & Welling, 2013) features, has already endowed the model with good capabilities for semantic-level reconstruction. It constrains the improvement space in generation tasks, while correspondingly enabling substantial enhancements in image editing capabilities.
F.7 MORE VISCALIZATION OF RECONSTRUCTION RESULTS
To provide deeper insights into how RECA improves model capabilities, we visualize the reconstruc- tion results. Figure 16 demonstrates how Harmon's reconstruction quality evolves across different training timesteps, while Figure 17 compares reconstruction quality before and after RECA post- training across all evaluated UMM architectures.
As shown in Figure 17, REC.A post-training yields substantial improvements in reconstruction qual- ity across all evaluated architectures:
Show-o: Reconstruction transitions from almost noisy outputs to clearer images with better
preservation of semantic content and object structures.
OpenUni, Harmon: Reconstruction quality improves significantly, with enhanced detail preser- vation and more accurate rendering of geometry, color and texture.
BAGEL: Despite already having some reconstruction capability from pre-training, RECA fur- ther enhances reconstruction quality, particularly in preserving fine- ine-grained semantic details and spatial relationships.
Training progression analysis. Figure shows how Harmon's reconstruction ability improves over the course of RECA training. Early outputs blurry and lack clear structure, but as training progresses, the model learns to recover object shapes, colors, and layouts more accurately. By the end, the reconstructions sharp and semantically faithful.
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Original Image
2500
5000
10000
Figure 16: Harmon reconstruction progression during RECA training. (Left Right) Early timesteps show blurry or incomplete reconstructions, while later stages achieve high-fidelity recon- struction with accurate semantic details, object structures, and color rendering.
F.8 MORE ABLATION STUDIES
To evaluate the robustness of RECA, we conduct comprehensive ablation studies on the scale and source of the training data.
Data Scale Scalability. We first evaluate Harmon-1.51 with varying amounts of Midjour- VeyV6 (C,rtexLM, 2024) data, ranging from 10,000 240,000. As shown in Table 13, RECA demonstrates consistent performance gains as the data scale increases, highlighting its potential for further improvement with larger datasets.
Data Source Robustness. Crucially, we investigate whether RECA relies on high-quality syn- thetic data. We select three distinct datasets representing different domains and quality levels: (I) COC02014 (Lin et al., 2014). representing real-world data with relatively lower visual quality; (II) JourneyDB (Sun et al., 2023), representing synthetic data with medium quality; (III) BLIP30- 60k (Chen et al., 2025a) and (IV) Midjourney V6 (CortexLM, 2024), representing high-quality syn- thetic data. As shown in Table 14, RECA maintains stable and robust performance across all sources. Notably, even when trained lower-quality real-world images (COCO), the method yields compet- itive improvements (GenEval 85.3), indicating that RECA A is applicable to broad data distributions.
Table 13: Data scale analysis. Performance with varying amounts of Midjourney V6 data.
Table 14: Data source robustness. Perfor-
mance across different data qualities.
Data
Scale
GenEval
DPG
Data
Quality
GenEval
DPG
MidJourneyv6
10,000
86.82
COC02014
Low
85.3
86.82
MidJourneyv6
50,000
85.3
87.06
JourneyDB
Medium
85.2
86.71
MidJourneyvó
240,000
85.7
87.21
BLIP3o
High
85.2
86,50
MidJoumnyyv
High
85.7
87.21
G LIMITATIONS AND FUTURE WORK
Despite the effectiveness of RECA across various unified multimodal models (UMMs), approach has several limitations that warrant discussion:
The improvements on the counting task are minor. As shown in Table 10, the model's gains counting are not substantial. We attribute this to object number being a mid-level visual feature that current MLLMs are not adept extracting. BLINK (Fu et al., 2024) points out that the information required by high-level semantic tasks (c.g.. distinguishing left from right, identifying color) can often be captured and leveraged in linguistic form, whereas counting resists language-only mediation in
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Original Image
OpenUni
Figure 17: Reconstruction comparison across UMM architectures. For each model (Show-o, Harmon, OpenUni, BAGEL.,, show reconstruction results before (left) and after (right) RECA post-training. RECA consistently improves reconstruction fidelity, especially in semantic details and color accuracy, which further benefits text-to-image generation.
MLLMs. Consequently, RECA primarily enhances the model's generative ability tasks directly tied to high-level semantics, such color attributes and positional relations. Improving counting may require the mixture training objective of high-quality counting datasets the incorporation of reinforcement learning techniques.
Architecture-speeffic constraints. RECA still exhibits limited effectiveness on BLIP-3o. As illus- trated in Figure 18, BLIP-3o possesses inherent image reconstruction capabilities. Application of RECA fails to improve reconstruction or text-to-image generation quality, likely because BLIP-3o's pre-training already incorporates strong reconstruction objectives, rendering additional reconstruc- tion training counterproductive.
Original Image
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2000
3000
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Figure 18: BLIP-30 reconstruction results. We observe that BLIP-30 possesses inherent image reconstruction capabilities. Following RECA application, reconstruction quality exhibits degrada- tion rather than improvement, suggesting that BI.IP-3 incorporates reconstruction objectives during pre-training.
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H GENERATED CAPTIONS
H.1 CAPTIONS FOR FIGURE 2
10-word Caption: Kitten and puppy cuddle warmly under a cozy yellow blanket.
100-word Caption: A heartwarming scene of pure companionship unfolds on soft white bed where a tiny tabby kitten and a tricolor Corgi puppy nestle side by side. Wrapped gently under fluffy mustard-yellow blanket, they gaze curiously at the viewer with wide, innocent eyes. The kitten's 's delicate stripes and the puppy's 's soft fur blend harmoniously under the golden tones of the blanket, creating tender atmosphere filled with comfort and warmth. Their small heads rest close, touching lightly, as if they've 've found solace in each other's s presence. The peaceful bedroom backdrop adds a serene touch to this moment of affection.
500-word Caption:
In warmly lit bedroom, the tranquil quiet of the morning is broken only by the soft breaths of two unlikely yet inseparable companions. Upon a pristine white bed, layered with thick cotton sheets and plush pillows, lies tender tableau of comfort and innocence: tabby kitten and a tricolor Corgi puppy, curled gently together beneath a soft, fleece mustard-yellow blanket. Their eyes are wide and bright, reflecting mixture of curiosity and calm, as if they've ve just woken up from a shared dream. The kitten is small, delicate, with a soft grayish-brown tabby pattern tucked beneath its chest, body pressed
running across its fur like brushstrokes. Its tiny paws closely to the puppy's 's for warmth and security. Its ears point upward slightly, twitching at the soft sounds of the morning breeze filtering through the room. Its eyes, a bright glimmering hazel, look out with an expression that is both alert and serene. The puppy, young Pembroke Welsh Corgi. has a face full of character expressive brown eyes, upright ears edged with tan, and a snout speckled with white. Its fur is a beautiful tricolor blend of rich black, creamy tan, and snowy white, each hue carefully painted by nature to highlight its playful features. Though still small, its body is slightly larger than the kitten's, ss and its front paw is placed protectively near its feline friend. There is no tension between ohem-only the shared comfort of closeness, the unspoken language of trust. The blanket they share adds golden warmth to the scene, its texture soft and inviting. It wraps around them like cocoon, hinting at a chill in the air beyond the bed, but ensuring they remain snug in their haven. The folds of the blanket frame them like the petals of a flower, drawing the eye inward to their peaceful gaze and gentle postures. In the background, the bedroom is softly lit by morning light diffused through a window. The white and cream tones of the bedding and walls contribute to the serenity of the scene, allowing the vivid colors of the animals and their blanket to take center stage. Pillows are propped up neatly behind them, hinting that someone may have just stepped away, leaving behind these two nestled in perfect moment of quiet affection. This image tells a silent story-not not just of two animals coexisting, but of genuine bond. It evokes themes of comfort, friendship. and the uncomplicated joy of presence. It reminds us of the purity in companionship, of how safety and peace can be found not just in familiar places, but the closeness of those we trust. The kitten and the puppy, young and full of life, are captured here in fleeting, gentle stillness that speaks to the heart.
H.2 CAPTIONS FOR FIGURE 8
512> x 512 images (left to right, top to bottom):
Two spiraling strands of rich, crimson-colored pasta rest elegantly on the surface of a polished dark wooden table, the grain of the wood accentuating their vibrant hue. This rustic Italian kitchen is bathed in the warm, golden light of the late afternoon sun, which highlights the intricate texture of the pasta. The table, set amidst traditional décor and terracotta pots filled with fresh herbs, offers a tranquil setting for this simple yet captivating culinary display.
golden squirrel and a bright magenta elephant in bright sunlight.
During the warm glow of a dwindling summer evening, particular fussy feline with distinctive calico markings is perched atop a garden table. The cat, seemingly indifferent to its surroundings, sports a pair of large, reflective aviator sunglasses that sit comically upon its small, furry face. Around the cat, there are scattered pots of blooming flowers, contributing to the charm of the scene, and in the background, hints of orange and pink skies are visible through the foliage. a highly intricate and vibrant cityscape that reflects a fusion of Moebius's 's imaginative design and Makoto Shinkai's 's detailed animation style. The streets are aglow with neon signs in kaleidoscope
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Origin
Replace the dragon made pipe deannes the image with bright yellow rubber duck.
Origin
Change the forested camping area snowy landscape with wooden cabin.
Origin
Change green follage background in the picture coastal beach setting.
Origin
Add a bicycle positioned near the left side of
dock. Remove colorful inflatable
Origin
Remove the bird from the image and replace the background with natural, unallered scene of trees and follage.
Origin
Remove the colorful infiatable bounce house with slides in the foreground.
Figure 19: Additional ImgEdit benchmark results. Qualitative results on image editing tasks. In each pair, the left image generated by the baseline model and the right by the RECA post-trained model
of colors, casting reflections on the glossy, rain-slicked pavements. Towering skyscrapers with glowing windows rise towards starless night sky, the artwork garners significant attention and praise on ArtStation.
A deep red rose with plush petals sits elegantly coiled atop ivory, intricately patterned lace napkin. The napkin rests on rustic wooden table that contributes to the charming garden setting. As the late evening casts warm golden hue over the area, the shadows of surrounding foliage dance gently around the rose, enhancing the romantic ambiance. Nearby, the green leaves of the garden plants provide fresh and verdant backdrop the scene.
A close-up image capturing the intricate details of maple leaf, which is composed entirely of clear, sparkling water droplets. The leaf is set against a smooth, dark background that accentuates its delicate water structure. The droplets glisten as they cling to the invisible veins of the leaf, creating a natural yet surreal piece of art.
A detailed photograph captures the image of statue with the likeness of an ancient pharaoh, unexpectedly accessorized with a pair bronze steampunk goggles resting atop its head. The statue is dressed in an anachronistic fashion, featuring a crisp white t-shirt and fitted black leather jacket that contrasts with its traditional headdress. The background accentuates the statue's unconventional attire and the intricate details of the steampunk eyewear. On the soft, warm sand of the beach, a fluffy white rabbit with rounded ears is caught in curious moment, gently placing its paw the ribbed surface of pink scallop shell. The scallop, slightly open, reveals its smooth interior contrasting with its coarse outer texture, while hues of pink and orange from the setting sun reflect off its surface. There's tranquil ocean backdrop with the gentle ebbing of the tide, and the fading daylight casts golden glow over the scene, highlighting the rabbit's s soft fur and the shell's subtle color.
simple, solid color that
1024 1024 images (left right, top bottom):
A transparent glass cube on endless desert, with burning candle inside casting shadows
the sand.
A drop of water containing miniature forest with colorful tiny flower inside.
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Origin
Change the time to prehistoric era.
Origin
Upgrade the necklace's material to 999 pure gold.
Origin
Add chair in the background.
Origin
Remove the stuffed animals.
Origin
Change the person's expression one of distress.
Additional GEdit-Bench-EN results. Qualitative results image editing tasks. In each pair, the left image is generated by the baseline BAGEL model and the right by the RECA post-trained model.
Figure 20:
A vibrant and traditional depiction of Tleokguk, Korean rice cake soup, served during the Chuseok festival. The image features steaming bowl of clear broth filled with soft, chewy rice cakes, slices of zucchini, carrots, and green onions, garnished with sprinkle of sesame seeds. Surrounding the bowl are traditional Korean elements, such woven basket with red dates, small wooden spoon, and a wooden table with a warm, earthy tone. The atmosphere is cozy and festive, with soft, natural lighting and a slightly blurred background to emphasize the dish. The scene captures the essence of Korean culture and the warmth of the Chuseok celebration, with focus authenticity and detail.
Photorealistic closeup image of two pirate ships battling each other as they sail inside a cup of coffee.
A cute handmade felt doll of a little girl standing on a grassy patch. She wears an orange knitted hooded dress with blue buttons and matching boots. The girl is holding smiling sun-shaped balloon made of felt. Fluffy white clouds with button details float in the sky, and soft green felt trees surround the scene. The style is whimsical, cozy, and playful, with pastel colors and dreamy, handerafted aesthetic.
The word 'RECA' is written on street surface, with the word 'STARTS' written just below it, surrounded by colorful chalk drawings and playful doodles.
Van Gogh style painting of cyberpunk city at sunrise.
An anime-style portrait of girl with big sparkling eyes, detailed hair highlights, soft gradient background, vibrant colors.
A dreamy composition of young woman with butterflies emerging from her skin, wings glowing soft golden hues, surreal and enchanting. She is wearing a wedding dress and has white angel wings, waving her hand.
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A surreal split-face portrait: left side realistic woman with soft skin and vivid blue eye, right side robotic cyborg with exposed steel plates, fluorescent blue circuits, tiny gears, and blood-red mechanical eye, cinematic lighting, futuristic and striking.
I MORE QUALITATIVE RESULTS ON IMAGE EDITING
We provide additional qualitative comparisons in this section. Figure 21 presents comparison be- tween the RECA post-trained BAGEL and previous SOTA models, ICEdit (Zhang et al., 2025c), FLUX-Kontext (Labs et al., 2025), and GPT-4o-Image. Compared with ICEdit and FLUX-Kontext, our method demonstrates stronger instruction-followe method exhibits superior identity preservation and background fidelity. Figure 19 and 20 show- case consistent improvements in semantic consistency. instruction following, and visual quality preservation across various editing tasks including background modification, style transfer, and ob- ject manipulation.
capability. In contrast to GPT-4o-Image,
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Origin
Ours
ICEdit
FLUX- Xuut-Kontext
GPT-4o
Turn the rainbow into a glowing glass dome arching over the valley, refracting colorful light
The dog looks left.
Add more stars in the upper right corner.
Convert it into an ink-in-water style, her hair and clothes dissolved into black ink and watercolor clouds.
Add dramatic side lighting, strong shadows emphasizing facial features.
Figure 21: Qualitative comparison of editing results across different methods. Our method achieves more faithful instruction following (e.g., rainbow dome, star addition), better identity preservation (c.g., pet and human faces), and stronger background consistency.
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J MORE QUALITATIVE ReSULTS ON TEXT-TO-IMAGE GENERATION
Figure 22: Uncurated generation results from Harmon-1.5B (top) and post-trained model (bot-
tom). Prompt: A white banana and a black banana.
Figure 23: Uncurated generation results from Harmon- 115B (top) and post-trained model (bot-
tom). Prompt: A photo of yellow broccoli.
Figure 24: Uncurated generation results from Harmon-1.5B (top) and post-trained model (bot-
tom). Prompt: A photo of an orange snowboard and green cat.
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Figure 25: Uncurated generation results from Harmon- (top) and post-trained model (bot-
tom). Prompt: A photo of a skis right of a zebra.
Figure 26: Uncurated generation results from Harmon-1.5B (top) and post-trained model (bot-
tom). Prompt: A photo of a microwave and a truck.
Figure 27: Uncurated generation results from Harmon-1.50 (top) and post-trained model (bot-
tom). Prompt: A diamond on the right, an emerald in the middle, a ruby on the left.
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Figure 28: Uncurated generation results from Harmon-1.5B (top) and post-trained model (bot-
tom). Prompt: Cheerful and bright, vibrant lighting. A shell and a bright orange bear in a bright setting.
Figure 29: Uncurated generation results from Harmon-1.5B (top) and post-trained model (bot- tom). Prompt: During the warm glow of dwindling summer evening, particular fussy feline with distinctive calico markings is perched atop a garden table. The cat, seemingly indifferent its sur- roundings, sports a pair of large, reflective aviator sunglasses that sit comically upon its small, furry face. Around the cat, there are scattered pots of blooming flowers, contributing to the charm of the scene, and in the background, hints of orange and pink skies are visible through the foliage.
Figure 30: Uncurated generation results from Harmon-1.5P8 (top) and post-trained model (bot- tom). Prompt: A vivid scene unfolds where several deep red, perfectly round tomatoes spill from woven brown basket onto a rustic wooden tabletop. The basket lies on its side as the plump toma- toes scatter across the surface, some touching the dark green leaves of a nearby herb plant. In the background, the blurred outline of an open kitchen window lets in soft, natural light, casting gentle shadows around the fallen produce.
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Figure 31: Uncurated generation results from Harmon-1.55B (top) and post-trained model (bot- tom). Prompt: A polished brown leather briefcase with visible stitching details rests on a white tablecloth, displaying sense of organization amidst the surrounding environment. Beside the brief- case, a vibrant red fedora hat provides a striking contrast against the pristine table covering. The table, placed in room with light beige walls, gives impression of professional setting with touch of personal style.
Figure 32: Uncurated generation results from Harmon-1.50 1.5B (top) and post-trained model (bot- tom). Prompt: a festive array of red and yellow balloons tied with curling ribbons, gently bobbing from the breeze of a spinning ceiling fan. The fan has wooden blades and brass finish, which contrasts with the bright colors of the balloons. The balloons are clustered in a joyful bunch, casting soft shadows the ceiling above.
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Figure 33: Uncurated generation results from Harmon- 1.5B (top) and post-trained model (bot-
tom). Prompt: photo of fire hydrant.
Figure 34: Uncurated generation results from BAGEL (top) and post-trained model (bottom).
Prompt: photo of surfboard.
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Figure 35: Uncurated generation results from OpenUni (top) and post-trained model (bottom) Prompt: photo of suitcase.
Figure 36: Uncurated generation results from Show-o-512x512 (top) and post-trained model (bottom). Prompt: photo of a cup.
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K THE USE OF LARGE LANGUAGE MODELS (LLMS)
During the preparation this paper, we used large language model (LLM) only as a writing assistant check grammar, spelling, and polish the clarity of expression. The LLM was not involved in the research design, experiments, or analysis, and the authors take full responsibility for all content of this work.
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Reviews:
[{"review_id": "0mxMkRZybz", "reviewer": "Reviewer_ccV6", "summary": "This paper identified an important misalignment and information gap during the training of image generation (text to image): text input provides only sparse visual information while the model learns to fine-grained visual details. The sparsity holds even when the prompt is completed with hundreds of words. In the context of a unified multimodal model (UMM), the mapping from visual features to language space gives rise to a new source of dense supervision for image generation, i.e., the semantic embeddings of the target image itself. Built on this insight, the authors proposed a post-training technique named Reconstruction Alignment (RecA), where images are abstracted by a visual encoder and mapped to the input space of LLM, then the generation part of the UMM reconstructs the input image. This enables the learning of structural and low-level details of images that may be rare in image captions. RecA was experimented on various types of UMM and brought consistent gains for both image generation and editing.", "questions": "1. The improvement on Harmon is exceptionally larger than on other models. Can the authors provide more insight? Would it be due to the shared visual encoder or because Harmon mainly uses short captions for training, thus leaving a large space for improvement with additional dense supervision?\n\n2. The performance gains on GenEval are crystal clear. However, GenEval prompts are all short, and the evaluation mainly focuses on high-level image understanding like object detection and counting. Why would the dense supervision be so effective?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The core idea of the paper, i.e, dense conditioning for image generation, is intuitive and well-motivated. It has been a long-standing issue in multimodal learning that generating visual details is much easier than perceiving them. The authors' perspective on the information density of condition signals is insightful.\n\n2. And the solution, a simple reconstructive supervision, is widely applicable to various UMM paradigms and is effective across different benchmarks. Particularly, the results on GenEval are exceptional, without relying on gpt-distilled data samples.\n\n3. The paper is well written and easy to follow.", "weaknesses": "1. The proposed method seems limited to the post-training stage. It is unclear whether ReCA will also benefit the pretraining of image generation models or unified models.\n\n2. More reconstruction results are expected for all the models studied. I believe the reviewers and readers would be interested in how well the images can be reconstructed.\n\n3. It is unclear how the performance would be affected by the training images, e.g., the scale of the data, visual quality, real data v.s., synthetic data.\n\n4. A factual error: Show-o is not an AR model for image generation. Instead, it is a mix of AR for understanding and discrete diffusion for generation.", "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": "tqo7Aqj0Gq", "reviewer": "Reviewer_xRHK", "summary": "This paper presents Reconstruction Alignment (RecA), a simple yet remarkably effective post-training technique for unified multimodal models (UMMs). RecA addresses the limitation of sparse text-caption supervision by leveraging dense supervision from visual encoder embeddings, training the model to reconstruct the original image. This process realigns the model's understanding and generation capabilities without requiring any additional labeled data. Extensive experiments across diverse UMM architectures (autoregressive, hybrid, diffusion-based) demonstrate consistent performance gains on both generation (GenEval, DPGBench) and editing (ImgEdit, GEdit) benchmarks. Notably, the method is computationally lightweight.", "questions": "1. Could the authors provide an analysis, perhaps using feature attribution methods, to identify where RecA contributes most significantly, such as spatial layout, geometry, or fine-grained attributes?\n2. Could applying RecA iteratively—through multiple cycles of reconstruction—lead to further improvements in the output?\n3. How does RecA perform when given open-ended or abstract prompts for which no concrete visual reference exists?\n4. Is there a quantifiable trade-off between the fidelity of the reconstructions and the diversity of the generated samples?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method is characterized by its simplicity and broad applicability, ensuring easy implementation with low computational overhead. It demonstrates strong empirical performance, evinced by consistent improvements across four distinct model families. The study maintains rigorous experimental standards, including fair comparisons and thorough ablation studies (e.g., SFT vs. RecA, encoder choice, resolution). The approach has high practical relevance as a plug-and-play component for future UMM pipelines, and its benefits are clearly illustrated through intuitive figures and examples", "weaknesses": "1. The heavy reliance on simple benchmarks (GenEval and DPGBench) raises questions about true generalization. The absence of tests for real-world prompt fidelity and compositional reasoning", "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": "FR6TWv179M", "reviewer": "Reviewer_U3Cq", "summary": "This paper introduces Reconstruction Alignment (RecA), a simple yet effective post-training method for Unified Multimodal Models (UMMs) that unifies understanding and generation. The key idea is to condition a UMM on its own visual understanding encoder embeddings and optimize it to reconstruct the input image using a self-supervised reconstruction loss.", "questions": "1. Could RecA be extended to cross-modal reconstruction (e.g., text→image→text) for deeper alignment?\n\n2. Could combining RecA with RL-based alignment (e.g., DPO/GRPO) further enhance reasoning alignment?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Simple yet effective. The key idea is very simple, but surprisingly works well. It yields consistent improvements in image generation and editing performance while being computationally efficient (27 GPU hours). \n\n2. Broad generality: RecA is architecture-agnostic and demonstrated across various UMM families (autoregressive, masked-autoregressive, and diffusion-based). Works across AR, MAR, and AR+Diff UMMs with consistent gains.\n\n3. Good experimental results. Experiments show that a 1.5B-parameter UMM post-trained with RecA outperforms much larger models, achieving GenEval 0.90 and DPGBench 88.15, and improves editing benchmarks such as ImgEdit (3.38→3.75) and GEdit (6.94→7.27).\n\n4. Clarity and reproducibility: Strong visualizations, clear methodology, and detailed appendices.", "weaknesses": "1. Limited theoretical grounding: The paper would benefit from more analysis on why reconstruction alignment so effectively bridges understanding–generation gaps (e.g., mutual information or feature alignment metrics).\n\n2. What are the failure cases—does RecA ever degrade diversity or overfit to reconstruction-style artifacts?", "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": "m11GHagrRH", "reviewer": "Reviewer_uKdX", "summary": "This paper introduces Reconstruction Alignment (RecA), a lightweight post-training framework designed to improve semantic consistency between the visual understanding and visual generation modules of unified multimodal models (UMMs). The key idea is to use the embeddings from the vision encoder as dense semantic prompts for self-supervised image reconstruction. By enforcing a reconstruction loss, the model explicitly learns to map semantic embeddings to visual outputs, thereby refining the alignment between understanding and generation. RecA is model-agnostic and can be applied to multiple architectures. Extensive experiments on GenEval, DPGBench, and multiple image editing benchmarks demonstrate consistent improvements with minimal computational cost.", "questions": "Please see weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The proposed reconstruction alignment is conceptually simple yet effective. By reusing visual embeddings as dense prompts, it provides a self-supervised approach to enhance multimodal coherence without relying on additional labeled or distilled data. Across multiple UMM architectures (e.g., Harmon and Show-o), RecA consistently improves the fidelity of text-to-image generation and editing. Furthermore, RecA demonstrates practical efficiency, requiring only 27 A100 GPU hours for deployment.", "weaknesses": "- Unified multimodal models (UMMs) aim to integrate both visual understanding and generation within a single architecture. However, RecA is based on a reconstruction task, where the training (I2I) and testing (T2I or I2T) processes differ. It is expected to further clarify how RecA benefits T2I generation tasks as well as visual understanding tasks, especially considering that visual understanding requires both detailed and high-level comprehension.\n\n- Although the work claims to enhance unified multimodal alignment, the quantitative results primarily focus on generation tasks (T2I and editing). Direct evaluation on understanding tasks (e.g., VQA) is needed to substantiate the claim of improving the \"unified\" aspect of UMMs, as the current results do not fully address this dimension.\n\n- The mechanism by which semantic embeddings improve alignment is primarily intuitive. A deeper representation analysis (e.g., embedding similarity before/after RecA) would strengthen the causal understanding of why RecA works.\n\n- RecA primarily operates as a post-training technique, which inherently limits its scalability.\n\n- Some important technical details are missing. For instance, the paper does not clearly specify where and how the visual embedding is integrated into the model. Additionally, it remains unclear whether hyperparameter tuning is required for each model, which limits the reproducibility of the results.", "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)