BideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment
Authors: Dewei Zhou
Mingwei Li
Zongxin Yang
Yu Lu
Yunqiu Xu
Zhizhong Wang
Zeyi Huang
Yi Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models
Multimodal Learning
Text-to-Image Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Conditional image generation augments text-to-image synthesis with structural, spatial, or stylistic priors and is used in many domains. However, current methods struggle to harmonize guidance from both sources when conflicts arise: 1) input-level conflict, where the semantics of the conditioning image contradict the text prompt, and 2) model-bias conflict, where learned generative biases hinder alignment even when the condition and text are compatible. These scenarios demand nuanced, case-by-case trade-offs that standard supervised fine-tuning struggles to deliver. Preference-based optimization techniques, such as Direct Preference Optimization (DPO), offer a promising solution but remain limited: naive DPO suffers from gradient entanglement between text and condition signals and lacks disentangled, conflict-aware training data for multi-constraint tasks. To overcome these issues, we propose a self-driven, bidirectionally decoupled DPO framework (BideDPO). At its core, our method constructs two disentangled preference pairs for each sample—one for the condition and one for the text—to mitigate gradient entanglement. The influence of these pairs is then managed by an Adaptive Loss Balancing strategy for balanced optimization. To generate these pairs, we introduce an automated data pipeline that iteratively samples from the model and uses vision-language model checks to create disentangled, conflict-aware data. Finally, this entire process is embedded within an iterative optimization strategy that progressively refines both the model and the data. We construct a DualAlign benchmark to evaluate a model’s ability to resolve conflicts between text and condition, and experiments on commonly used modalities show that BideDPO delivers substantial gains in both text success rate (e.g., +35\%) and condition adherence. We also validated the robustness of our approach on the widely used COCO dataset. All models, code, and benchmarks will be released to support future work.

Published as a conference paper at ICLR 2026
BIDEDPO: CONDITIONAL IMAGE GENERATION WITH SIMULTANEOUS TEXT AND CONDITION ALIGNMENT
Dewei Zhou', Mingwei Li Zongxin Yang?. Yu Lu
Yunqiu Xu', Zhizhong Wang", Zeyi Huang', Yi Yang
'Zhejiang University Harvard University *Central Media Technology Institute, Huawei 'Zhongguancun Academy, Beijing
Condition
Previons Methods
Ours
Five LEGO dogs.
+
default cond.
low cond.
defarli cond.
(a) Input-Level Conflict: Text requires LEGO structure Condition image requires real-dog structure.
Condition
Previous Methods
Ours
A jade- Like dog.
default cond.
low cond
default cond.
(b) Model-Bias Conflict: Text requires jade-like surface vs. Model-Bias favors real-dog texture.
Condition
Previous Methods
Ours
9
dragon.
defanlt cond.
low cond.
detauli cond.
Condition
Condition
Previous Methods
Ours
tword.
default
cond.
default cond.
(c) More examples achieving dual alignment between text prompts conditioning inputs.
Figure I: Qualitative comparison on cases with conflicting text and condition. We first introduce two conflicts between the text prompt and conditioning input: (a) Input Level Conflict and (b) Model Bias Conflict, which hinder model controllability. We then propose solution that resolves both, generating images that satisfy both the text and the condition. "default cond." means using its default condition constraint scale, while "low cond." means using lower condition constraint scale. (c) Our method also enhances alignment between text and abstract conditions such as style condition, and supports generation with multiple conditions combined with text prompts.
ABSTRACT
Conditional image generation augments text-to-image synthesis with structural, spatial, stylistic priors and is used in many domains. However, current methods struggle to harmonize guidance from both sources when conflicts arise: 1) input- level conflict, where the semantics of the conditioning image contradict the text prompt, and 2) model-bias conflict, where learned generative biases hinder align- even when the condition and text compatible. These scenarios demand nuanced, case-by-case trade-offs that standard supervised fine-tuning struggles
Cooresponding author.

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Published as a conference paper at ICLR 2026
to deliver. Preference-basee optimization techniques, such Direct Preference Optimization (DPO), offer a promising solution but remain limited: naive DPO suffers from gradient entanglement between text and condition signals and lacks disentangled, conflict-aware training data for multi-constraint tasks. To overcome these issues, we propose a self-driven, bidirectionally decoupled DPO framework (BideDPO). At its core, our method constructs two disentangled preference pairs for each sample-one for the condition and one for the text-to mitigate gradient entanglement. The influence of these pairs is then managed by an Adaptive Loss Balancing strategy for balanced optimization. To generate these pairs, we intro- duce automated data pipeline that iteratively samples from the model and uses vision-language model checks to create disentangled, conflict-aware data. Finally, this entire process is embedded within an iterative optimization strategy that pro- gressively refines both the model and the data. We construct a DualAlign bench- mark to evaluate model's ' ability to resolve conflicts between text and condition, and experiments on commonly used modalities show that BideDPO delivers sub- stantial gains in both text success rate (e.g., +35%) and condition adherence. We also validated the robustness of our approach on the widely used COCO dataset. Project Pages: https::/Limuloo www.ooogggittte github. 10/ wtt.thuuub....odde.o.ooo..
INTRODUCTION
Conditional image generation Zhang et al. 2023 Li al. 2024 Liu al. 2024 Zavadski et al. 2024) augments text-to-image synthesis with auxilarr constraints (e.g., structural or spatial priors) and is now widely used in digital art, design, and related workflows. However, real-world use with complex compp-condiiion pairs reveals a fundamental yet underexplored challenge: reconciling guidance from the text prompt with the conditioning input. In this work, we are the first to ex- plicitly identify this problem and propose solution. Specifically, we highlight two recurrent conflicts that undermine model controllability: 1) Input-Level Confliet. When the condition image contains strong semantics that contradict the user prompt, current models often fail to balance these competing sources of guidance. As shown in Fig. a), under the official default setting, models typ- ically prioritize the condition image, resulting in outputs that closely replicate its semantics while neglecting the prompt. Conversely, weakening the influence of the condition allows the model to better follow the text, but often the expense of spatial or structural consistency. 2) Model-Bias Conflict. Modern conditional generation models possess strong generative bias-that is, given a particular condition input, the model tends to produce outputs consistent with its learned biases. As illustrated in Fig. [b), even when the condition and text are theoretically compatible, a mis- match between the model's prior and the prompt can lead to poor adherence to the textual guidance. Addressing the above conflicts requires the model to effectively navigate trade-off's between the con- dition input and the text prompt, for which no universally optimal solution exists. In this context, rather than directly providing the model with fixed "correct" outputs through supervised learning, more flexible and effective alternative is to guide the model using preference data canta-xxample that reflect human judgments over competing outputs. Motivated by this, we adopt the Direct Preference Optimization (DPO) (Ratailov et al. 2023) approach, which has been shown to effectively align model outputs with human preferences in both large language models and standard text-to-image generation, and apply it to train our model to better resolve conflicts between condition inputs and text prompts based on preference data.
However, introducing DPO into the image-conditioned generation task proves to be highly non- trivial, presenting two major challenges. Naive DPO fails to achieve balanced alignment of both constraints. In naive DPO, a single preference pair is used for each example. To jointly im- prove both condition and text alignment for each case, is necessary to set the positive sample to satisfy both constraints and the negative sample to satisfy neither (Fig. aa)). However, we observe that the model often prioritizes the condition input while neglecting the text, especially when these guidance signals conflict (see Fig. This limitation stems from gradient entanglement, as the cou- pled learning signals obscure the optimization direction for the weaker constraint, making it difficult for the model to balance and improve both constraints simultancously. 2) Lack of Disentangled, Conflict-Aware Preference DPO Data: To the best of our knowledge, there are no established DPO datasets tailored for conditional image generation, especially for scenarios where the condition input
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Coupled Preference Pairs
Text-Disnttagled Pairs
Condition- Condition-Dieetangle Pairs
prompt: a vini sovered sow ia R parie
prompt: vine covered 6ow in.
procapt: vine covered 6aw in
coupled
test cand
Grtypwer Pa
50
LuideDPo
S1
Vanilla
(b) Bidirectionully decoupled (Ours)
Comparison between vanilla DPO and our bidirectionally decoupled DPO for condi- tional image generation. Vanilla DPO uses coupled preference pairs, so its gradients can become ambiguous or even vanish when text and condition are not aligned together. b) BideDPO separates the learning signals for text and condition and adaptively balance them. This provides clear, adaptive gradients for each requirement, allowing the model to achieve better multi- multi-constraint aligmment.
Figure 2:
and text prompt provide conflicting or competing guidance. This data gap significantly limits the exploration and benchmarking of preference-basee optimization in multi-constraint settings.
To overcome these challenges, we propose self-driven, bidirectionally decoupled DPO frame- work tailored for conditoondi generation. Our framework consists of three key components: 1) Bidirectionally decoupled DPO algorithm (BideDPO): As shown in Fig. bb), unlike naive DPO, our method constructs two decoupled preference pairs per example-one one for condition fi- delity and one for text adherence susreneeeueedddee simultaneously during optimization.
An adaptive loss balancing strategy further ensures balanced progress on both objectives, preventing the model from collapsing toward single constraint. This decoupling, combined with dynamic loss adjustment, enables the model to better handle conflicts between the two constraints and achieve a more effective trade-off. 2) Automated Construction of Disentangled and Conflict-Aware DPO Preference Data: As shown in Fig. 3 we address the lack of suitable DPO data by introducing an automated pipeline that iteratively samples from the current model and uses vision-language model (VLM) checks to construct high-quality positive and negative samples for both text and condition branches. These datasets include numerous instances where the text and the condition flict. 3) An iterative optimization strategy: Our framework naturally supports iterative refinement because the generator itself produces the preference data used for training. As shown in Fig. 4. we alternate between generating preference pairs with the current model and optimizing it with BideDPO. Each round leverages the improved generator to produce higher-quality data, creating a self-reinforcing loop that progressively enhances both model performance and data quality.
con-
We construct a DualAlign benchmark to evaluate how state-of-ther ofethe--rtr conditional image generation methods handle conflicts between the conditioning input and the text prompt. Experiments are con- ducted across standard conditioning modalities. Results show that our method markedly improves the text success rate (SR) and adherence to the conditioning signal over strong baselines for ex- ample, on -LUt-Depth it boosts text SR by 15% and reduces the conditional MSE by 87.7 points. We also validate robustness on the standard COCO benchmark (Lin et al. 2015 Zhang al. 2025): even with standard prompts, our approach delivers substantial gains over the original model, e.g., a 15% improvement for canny-conditioned generation. Furthermore, our iterative optimization strat- egy proves effective: as the number of iterations increases, the model consistently achieves better trade-off's between conditioning and text, yielding steady performance gains.
We summarize our contributions as follows:
To the best of our knowledge, this work is the first to formally formulate and systematically ana- lyze the text cond-ooniiion adherence conflict in conditional image generation. We propose BideDPO, an effective DPO algorithm that reconciles confficts between condition inputs and text prompts.
We propose an automated pipeline that produces disentangled condition-tex preference pairs. casily extends to other tasks and supports iterative optimization, improving both model and data.
We construct DualAlign Benchmark for evaluating a model's ability to handle conflicts between condition inputs and text prompts, and the results highlight the effectiveness of approach.
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2 RELATED WORK
Conditional Image Generation. With the rapid advancement of image generation
technol-
ogy (Rombach et al. 2022 Podell ct al. 2023 Rombach et al., 2023 Esser al. 2024 Lu ct al.
2023
2024a
2025 2024b
Zhou et al.
2025c
2023 2024a b 2025b a Zhao et al.
2024a c
2025a
2024b Li et al. 2025 2026), current models are now capable of producing highly real-
istic images. As a result, a key research focus has shifted toward enabling more conditional image
generation (L et al. 2024 Bhat et al. 2024 Lin et al.) 2024 Peng al. 2024 Wang et al. 2024
Ye al. 2023 Zhang et al. 2025 Xu et al.
2025), aiming to make text-to-image technologies ap- plicable in broader range of world scenarios. For example, models of the ControlNet (Zhang
ct al. 2023 Xu et al. 2024 Zhao et al.) 2023) family, such as FLUX. LUX.I-dew Pufo--nnio2Po22 (Shakker
20250. introduce an auxiliary network inject user-provided spatial constraints or reference image information into the generation process. Other variants, including FLUX-Depth and FLUX- Canny (BlackForest, 2024). concatenate the encoded condition image with the generated image features at the input channel, training the entire network end-to-end to achieve conditional image generation.
Labs
Aligning Image Generation with Human Preferences. Reinforcement learning (RL) methods are widely used for post-training large language models (LLMs) to align outputs with human prefer- ences, and has recently been applied image generation for improved controllability and preference alignment. Most approaches rely on explicit reward models, such as IewaeReward (Xu et al.) 2023).
combined with policy rollouts like PPO (Liu et al., 2025 Xue al. 2025) or direct gradient meth- ods (Clark et al. 2024 Prabhudesai et al. 2023). A more efficient alternative is Direct Preference
Optimization (DPO) (Rafailov al.] 2023). adapted to diffusion models by Diffusion-DPO Wal-
lace et al. 2023). and further extended for richer feedback (RankDPO (Karthik et al., 2024 and timestep inconsistencies (SPO [Langg al. 2024), TailorPO (Ren al. (025)). Given efficiency and effectiveness, we build on the DPO framework. However, existing DPO-based methods still struggle with conditional image generation involving multiple, potentially conflicting constraints. To address this, we propose novel Bidirectionally Decoupled DPO algorithm that enables clearer optimization directions and better handles complex conditional scenarios.
METHOD
Our method has three components: (1) a Bidirectionally Decoupled DPO algorithm (BideDPO,
$3.1 that enforces simultaneous text-condition adherence; (2) an automatic pipeline for construct- ing Disentangled and Conffict-Awaree Preference Data ($3.2) for BideDPO training: and (3) an itera-
tive optimization strategy (3.3) that jointly improves model performance and data quality. Prelim-
inary background on Diffusion Models and DPO is provided in Appendix
3.1 BIDIRECTIONALI DECOUPLED DPO
Limitations of Vanilla DPO. In conditional image generation, models are often tasked with satisfy- ing both text prompt p and an extra structural condition s. For notational simplicity, we encapsulate both inputs into a single composite context variable e = (p,s). To analyze the optimization dynam- ies of DPO (Ratailov et al., 2023) in this multi-objective setting, we can conceptualize the model's preference score as being composed of a text alignment component, fiaa(,, c; 0) (which primarily depends on p). and a condition alignment component, foond(1, (I, c; ff) (which primarily depends on s).
For simplicity, let us assume the overall score f(x c; 0) can be modeled as a weighted linear com- bination of these components, while acknowledging that the true relationship far more complex:
far c; 0) Axxtffxaa 0) + Acond (z, c; 0),
(I)
where Avai and Acand are scalar weights. For a preference triplet (a+ _= .c). where both the pre- ferred and dispreferred samples are evaluated under the same shared composite context the vanilla DPO loss is:
Cooupted log (f c; c,
f(z 0)).
(2)
The partial derivative of this loss with respect to the model parameters 0 is given by:
8 deaapled =
aD 8)
--(1-o(A(c0)) -
(3)
00
00
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(c; = Aux (Fea(x ,0,0) - feal :0:007000)) .c: +Acond (Fooma(** c; 0) - f (x , .c,)))
(4)
(ua(c;;)
Avmi(e:))
The update gradient in Eq. is influenced simultaneously by both objectives, but when one ob- jective's gradient is significantly stronger, can dominate the update. Consequently, the weaker objective may be masked or, in cases of conflict, the update may move in direction that opposes the weaker objective, making its optimization more difficult.
Decoupled Preference Pairs. To address this limitation, construct two decoupled preference pairs for each case, targeting condition and text alignment separately (Fig. For text alignment, pair ".p. Co), and for condition alignment, we use (1 FC (1). Here, co (p, 8o) and C₁ = (p, 81), where p is the same target prompt in both cases. In the text alignment pair, 5 and Jy have similar adherence to so, but . follows the prompt p. while Iy does not. In the condition alignment pair, both & and F follow p, but bu matches $1 much better than Jc- Two independent loss terms are then calculated separately for each objective:
Lixt log (frxx( co; 0) fiex1 (00 coi 0))
(ca19)
Goodd - log (Scond C15 - Soond (2) c1;0))
(6)
(c:1()
Adaptive Loss Balancing. To prevent the optimization from being dominated by one objective, we introduce an adaptive Ioss balancing strategy. To ensure stable training, the weights for each loss component are computed based on their current magnitudes but are treated as detached constants during backpropagation. This is achieved by applying stop-gradient operator, denoted as sg(-):
Liest
Whext =gg
L + Loood and
Wcond sg (1 Wixt)
The total loss is thus a dynamically weighted sum:
Laccoupled Wext + Coond-
(8)
Decoupled gradient. The gradient can be formulated as:
dddcoupled
O.axxt
aLoond
Wiext
+ Weund
08
00
08
Asux(co;
ADoond (c1: 0)
= Wicxt (1 o (Aen(co:))) 0))
Woond (1- a (Acona(c1;;)) 0))
00
00
Crucially, this gradient is a fully decoupled sum. Unlike the coupled gradient in Eq. 3 our approach
provides a distinct optimization signal for each objective. This prevents one objective's gradient from being diminished or "swallowed" when the other's loss is significantly larger, ensuring both are consistently optimized.
BideDPO Objective for Difffusion Models. Building upon prior work (Wallace et al. 2023
define the reward r(z,, c, f) in diffusion models as the reduction in denoising error for noisy sample 2, under a given context which is computed as the difference between the denoising error ||c - (,r((x,,)) 122 of the frozen reference network and the error ||c co c)ll of the optimized network, where represents the noise:
r(rt, C, f) ||e Gar( e)I ||e en c]1
(10)
The total reward differences for the text pair under context co (Rr) and the context pair under context ei (Rc) are then:
RT ratt Co. c:: 0) r( Co. G7;
(11)
Re r(=tt- C1, (): r(z,c- C1, G)
(12)
The final BideDPO loss adaptively weights the objectives based on these reward differences:
LBddDro(O)) (att (rrta (a )~De Whext log (3TRr) + Weond log a (BTRc))
(13)
By providing distinct gradient for each objective, decoupled approach mitigates the inter- ference inherent in the coupled DPO loss. This leads to more stable and efficient objti-objective optimization. Please Appendix for more details.
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Stepl: Automated Text Prompt Condition Generation
[[Sorcee prompt: "A toolbox on
Condition
objectsr[ dog'. 'cat"... places-('on the grass'... detailed-f detailed-"transarennt" ....
highway", Target prompt: crystalline toolbox on a highway )....
Source
Generator
Prompt
GPT-4.5
Step2: Generate Text-Disentaggle Preference Pairs (yellow data below)
ConditionO
B
ConditionO
"Daes senersted image matcha the test
Prompt
Souree
Generator
Prompt
Target
Generator
Yes
No
T
VLM
1
Step3: Generate Condition- Cadiition-Disentanllee Preference Pairs (blue data below)
Condition
"Dare grourrtted image matches the
Condition
Prompt
Target
Generator
Yes
No
VLM
strictly align
generally align
Figure 3: The Automated Disentangled, Conflict-Aware Preference Data Generation Pipeline.
Low
Data Quality Generator Ability
High
Iteratively enhance both the training data and the generator
Iter!
G
G
G
BideDPO
G
BideDPO
Aglassy pidioo ow shäge
G
A Agglssy pianso shige
Figure 4: Iterative Optimization Strategy. We start with initial generator (G") that produces training data via our automated pipeline (Fig. B Training with BideDPO already improves the model (G*), while repeating the process with the updated generator yields higher-quality data and further gains, forming a self-reinforcing loop where both data and model improve progressively.
3.2 AUTOMATED CONSTRUCTION OF DISENTANGLED AND CONRLICT-AWARE DPO PREFERENCE DATA
As shown in Fig. disentangled preference pairs for both text and condition alignment, including cases where the two objectives are in conflict. It consists of three steps:
design automated data construction pipeline that explicitly generates
1. Prompt and Initial Condition Generation. We first use an LLM to generate a basic Source p. The source prompt is then used produce initial
Prompt and more detailed Target Prompt condition map, "Condition 0" (su). The generated so and target prompt often exhibit input-level or model-bias conflicts in Fig.
2. Text- Dext-Disentangled Pair (xt IT. ( sp)). Both samples adhere to "Condition 0" (so). The pre- ferred sample # is generated from the Target Prompt, with its textual alignment verified by VLM, and serves as our high-quality anchor. The dispreferred sample JT is generated from the Source Prompt and thus lacks textual alignment with Target Prompt.
3. Condition-Disentanglel Pair (It PP. 81). Both samples align with the Target Prompt p. The anchor # serves as the preferred sample a and new, strictly aligned condition map "Condition 1" (s1) extracted from it. The dispreferred sample z then generated to adhere less strictly to "Condition 1" while matching the target prompt's semantics.
This structured process allows to systematically generate preference data that isolates and targets distinct aspects of text and condition alignment. By repeating this process over a large set of prompts and conditions, built comprehensive dataset that enables targeted, disentangled optimization for multi-constraint image generation. Moreover, this approach can easily extended to other tasks, such style-text alignment, illustrated in Fig. g

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Depth
Union-Pro2
+SFT
+DPO
+Ours
foom teleccspe Ln sing at nightt
Depth
FLEXX Depth
SFT
DPO
Ours
Lavik aracieze shove. and Lnvo-ordollzel the Libvrry
Canny
Union-Pro2
SFT
DPO
+Ours
Lava Lavt--ortekzsd chesshoard and fiory canalle Lis subbway.
Canny
FLU IX-Canny amny
SET
D PP
Dars
mussiy vlolán iu Ric.
Soft Edge
Union-Pro2
DPO
OOus
iry condle ow planform.
Figure 5: Visual comparison for conditional image generation the DualAlign Benchmark. We evaluate three common conditioning modalities: depth, Canny, and soft edge. Our method improves adherence to both the text prompt and the spatial conditioning. Please zoom in for details.
3.3 ITERATIVE OPTIMIZATION STRATEGY
For each generator model, BideDPO strengthens adherence to both text and condition, and since our data construction pipeline builds samples directly from the same model, the process naturally supports iterative refinement. As shown in Fig. we alternate between generating preference data with the current model and optimizing with BideDPO, forming self-reinforcing loop that pro- gressively improves both the generator and its training data.
4
EXPERIMENTS
4.1
EXPERIMENTAL SETUP
Baselines. We conduct experiments the state-of-the-art text-to-image model FLUX (BlackForest 2024]. Specifically, we evaluate our approach on the most widely used conditional image genera- tion variants in the community, including FLUX-Depth, FLUX- FLUX-Canny, and Union-Proo (Shakker
Labs 2025). We also compare with LooseControl (limited to depth conditioning) (Bhat et al. 2024 and ControlNett+ (Li al. 2024). We evaluate style-conditioned generation on FL.X IP-
Adapter (Team] 2024). We primarily compare our method with two common basclines: supervised fine-tuning (SFT) and naive DPO (Wallace al. 2023).
Implementation Details. We generate 5,000 samples in each iteration. For SFT, we use the positive samples in Fig. For DPO, we construct coupled preference pairs by combining the condition image and positive sample from the Aandition-Disentanlle Preference Pairs, together with the negative sample from the Text-Die Disentangled Preference Pairs. We fine-tune all models using Low- Rank Adaptation (LoRA (Hu et al. 2022) method with rank of 256. For the SFT method, train

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Table 1: Results for depth-conditioned image
Table 2: Results for canny-conditioned image generation on DualAlign Benchmark. "Ctrl." indicates support for conditional generation.
generation on Algg Benchmark. "Ctrl."
indicates support for conditional generation.
Method Ctrl. SR t MSE SGMSE
CLIP f
Method Ctrl. SR
t
F1
SGF1
CLIP f
FLUX x 0.79
N/A
N/A
0.2936
FLUX
0.71 N/A
N/A
0.2965
LooseControl v 0.43
791.1
1280.2
0.2852
ControlNet++ V 0.40 0.437
0.174
0.2828
ControlNet++ V 0.49
331.9
480.7
0.2854
Union-Proz Pro2 V 0.34 0.418
0.143
0.2753
Union-Pro2 v 0.49
177.0
272.4
0.2748
+ SFT V 0.58 0.324
0.178
0.2838
SFT v 0.70
262.2
332.5
0.2915
+ DPO V 0.50 0.607
0.284
0.2840
DPO v 0.71
168.3
219.9
0.2860
Ours V 0.68 0.607
0.393
0.2845
+ Ours v 0.84
164.0
195.7
0.2924
FLUX-
0.33 0.397
0.129
0.2703
FLUX-Depth v 0.76
233.6
282.8
0.2899
+ SFT V 0.52 0.357 0.179
0.2842
SFT v 0.79
162.2
195.7
0.2926
+ DPO < 0.55 0.452
0.248
0.2829
+ DPO v 0.89
171.9
195.0
0.2974
+ Ours
0.73 0.454
0.333
0.2927
+ Ours
V 0.91
145.9
164.4
0.2982
for 5,000 steps using the Prodigy (Mishchenko & Defazio 2023) optimizer with a learning rate of 1.0, using all positive samples in Fig. for DPO and BideDPO,, starting from the SFT-tuned model, optimize for an additional 2,000 steps using the AdamW (Kingma & Ba 2017) optimizer with learning rate of 0.00004 and a weight decay of 0.01.
Evaluation Benchmarks. 1) DualAlign benchmark for confticting text-condition constraints. Cur- rently, there is no established benchmark for evaluating conditional image generation in scenarios where the text prompt and condition image provide partially conflicting guidance. Therefore, construct our own test set following a similar pipeline as our training data, generating text-condition pairs that require the model to make meaningful trade-offs between constraints. To better assess the generalization ability of our approach, we ensure that the objects, places, and detailed descriptions in the test set do overlap with those in the training set. Each modality contains 100 cases. 2) COCO benchmark for robustness. To assess robustness on a standard benchmark, we also evaluate various post- post-training methods alongside their baseline models on the COCO dataset (Lin al., 2015 Zhang et al. )025). demonstrating that our approach preserves the base model's original performance. 3) DualAlign-Srea Sryle benchmark for text-style condition constraints (see
E.2 in Appendix).
Evaluation Metrics. We evaluate our models using the following metrics: 1) Success Ratio: We use the Sween.55VVL--225 (Bai et al. 2023) model automatically determine whether the generated image accurately matches the text description, providing a direct measure of text-image consistency. 2) CLIP Score (Radford et al. y: This metric quantifies the semantic alignment between the generated image and the input prompt, indicating how well the model captures the intended content described by the user. 3) MSE/FI Score: These metrics assess the degree to which the generated image conforms to the input condition spatial or structural constraints), thereby measuring con- ditional fidelity. 4) Semantic-Guidedr MSE (SGMSE), Cermnttic-Guddee F1 (SGF1), and Semantic- Guided SSIM (SGSSIM): To jointly evaluate textual and conditional alignment, define SGMSE, SGF1, and SGSSIM. Each extends its standard counterpart by adding a semantic check. If a gen- eration fails the text requirement, we apply penalty: MSE doubled, and the Fl or SSIM score is set to zero (otherwise the metrics reduce to the usual MSE, F1, and SSIM). This design penal- izes outputs that do not satisfy both constraints and provides a more comprehensive assessment of controllable image generation.
4.2 EXPERIMENTAL RESULTS
Qualitative Results. Fig. 5 presents visual comparisons between our BideDPO and other post- training methods. Supervised fine-tuning (SFT) reduces the model's adherence to the input condi- tion. For example, in the fifth row of Fig. the shape at the top of the candle is noticeably altered. Naive DPO, due to coupled gradients, biases optimization toward condition adherence while often neglecting textual alignment, resulting in outputs that frequently fail to match the text description. In contrast, our bidirectionally decoupled and adaptively balanced approach enables the model to resolve conflicts between condition and text better, achieving more effective trade-off and satis- fying both constraints. Notably, BideDPO and vanilla SFT are trained with exactly the same set of positive examples; this controlled comparison underscores the superiority of method.
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Table 4: Quantitative results on COCO Benchmark with depth and canny conditioning.
Depti-condittoned
Canny-conditioned
Method
SR t
MSE |
SGMSE
CLIP
SR
F1
SGF1
CLIP
LooseControl
0.72
1334.0
1706.0
0.2534
N/A
N/A
N/A
N/A
ControlNet++
0.79
548.3
668.9
0.2557
0.71
0.339
0.245
0,2622
Union-Prov
0.83
297.3
363.5
0.2546
0.78
0.416
0.332
0.2539
+ SFT
0.87
561.7
635.6
0.2575
0.81
0,271
0.271
0,2602
+ DPO
0.90
263.4
278.2
0,2586
0.75
0,490
0.373
0.2554
Ours
0.91
236.3
245.3
0.2633
0.83
0.497
0.392
0.2629
Condition
IterO
Iterl
Iter2
Iter3
faamingg
dog.
Figure 6: Visualization of Iterative Optimization.
2 and show that our method substantially improves adherence to both text prompts and conditioning inputs. For example, in depth-conditioned gen- eration, we observe a 43% increase in Success Ratio on Union-Pro2 and a 15% increase FLUX-Depth. In canny- conditioned generation, our approach achieves 34% higher Success Ratio Union-Pro2 and a 40% increase on FLUX-- Canny-even surpassing the original T2I FLUX model in terms of text alignment. Moreover, our method also enhances ad- herence to the input condition across var- ious baselines. For instance, on the MSE loss, our approach reduces the error FLUX- Depth by 148.687, demonstrating improved conditional fidelity. Finally, Tab, shows that our approach does not compromise the base model's robustness: on COCO- dataset not used during training-itt still delivers improvements over the original model. Importantly, all results shown here are obtained without iterative optimization.
Quantitative Results. Quantitative results Tabs.
Table 3: Results for soft edge-conditioned image generation on DualAlign Benchmark. "Ctrl." indi- cates support for conditional generation.
Method Ctrl. SR t SSIM SGSSIM t CLIP
FLUX
x
0.73
N/A
N/A
0.2907
Paion-Pro22 v 0.24
0.610
0.145
0.2768
+ SFT <
0.48
0.510
0.255
0,2855
+ DPO
0.39
0.637
0.250
0.2783
Ours
0.49
0.643
0.297
0.2855
Iterative optimization strategy. Since BideDPO simultaneously strengthens the model's adherence to both text and condi- tion, we can adopt an iterative optimiza- tion algorithm refine the model and data together. Tab. 5S shows that our it- erative optimization (Fig. 4 progressively improves adherence to both condition and text, reaching optimal performance by the third iteration. This trend is further sup- ported by the qualitative comparisons Fig. 3 where generated images increas- ingly align with the text while preserving condition fidelity. Importantly, even a single iteration already yields substantial improvements over the baseline. Thus, iterative refinement should be garded as an optional enhancement that can be adjusted based on available computational resources.
Table 5: Ablation study of our core components. "w/o ALB" ablates our Adaptive Loss Balancing. "Text. Only" and "Cond. Only" are trained using only the text or condition preference pairs, respectively.
Method
SR
MSE 1
SGMSE 1 CLIP
Iter =
0.84
163.968
195.728
0.2924
Iter =
0.85
158,876
195.459
0.2947
Iter 3 (Ours)
0.88
159.559
190.263
0.2957
Iter
0.86
166.274
202.363
0.2939
w/o ALB
0.78
157.729
205.246
0.2862
Text. Only
0.88
258.954
287.749
0.2947
Cond. Only
0.59
153.659
218.683
0.2753
the results for "Iter 1" demonstrate more balanced improvements than those for "w/o ALB", with a 6% increase in success rate and a 9.518 gain in SGMSE, despite only modest decrease in MSE (6.239).
Ablation Study of Adaptive Loss Balancing (ALB). In Tab.
Ablation Study on Preference Pairs. As shown in Tab. 5 using only text- or condition- disentangled preference pairs (Fig. 3 makes the model focus single aspect, yielding marginal

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Published as a conference paper at ICLR 2026
Table User study results. Values are win rates.
Table Success rates from different evalua- tors. Results show that our improvements are not artifacts of specific VLM but hold univer- sally across models and human judge.
Comparison
Text f
Condition
Overall 64.0%
Ours Base
67.9%
66.8%
Table 7: Style-conditioned generation.
Judge
UnionPro2
+SFT
+DPO
+Ours
Method
SR 1
Style
SG Style 1
CLIP1
Qwen2.5
0.49
0.70
0.71
0.84
IPA
30%
6.50
1.31
0.1679
GPT-4o
0.43
0,65
0,70
0.82
Ours
58% %
6.26
2.97
0.2015
Human
0.42
0.62
0.64
0.84
Condition
Cordition
IPA
lamp
compass
Conditso
Qurs
Conditiin
IPA
Ound
telesoope
windwwlit
Figure 7: Visual results for style-conditioned image generation on IP-Adapter (Team 2024
or even harmful effects on the other. Only by jointly leveraging both types of preference pairs (ours) can the model achieve balanced improvements across all constraints.
User Study. We conducted user study compare models, evaluating three aspects: text ad- herence, condition adherence, and overall alignment. For each trial, we randomly sampled 20 cases and asked 30 participants to evaluate them a 1-vs.-l format, yielding a total 600 comparisons. As shown in Tab. 6 participants favored our model roughly twice as often as the baseline.
Universality on style-conditional image generation. Beyond structure- and spatial contal-condiiional generation, further validate the effectiveness of our method more abstract conditions, such as style-conditioned image generation. As shown in Fig. 7 prior methods often suffer from excessive reference copying. due the Model Bias issue discussed in Fig. bb). . In contrast, our proposed Bid- eDPO algorithm substantially mitigates this problem, achieving a 28% higher success rate (Tab. 7
More VLM Selection. To verify robustness beyond single evaluator, additionally assessed our method using multiple VLMSs-nncuuing Qwen2.5-VL-722, GPT-4o-and human raters. As shown in Tab. R all evaluators yield consistent rankings, confirming that improvements artifacts of a specific VLM but hold universally across models and human judgment.
5 CONCLUSION
In this work, we address the challenge achieving simultaneous text and condition alignment in image generation. We identify that existing opproaches-including supervised fine-tuning and naive DPO-struggle to balance multiple constraints, especially when the text prompt and condition input in conflict. To overcome this, we propose bidirectionally decoupled DPO framework that disen- tangles the optimization of textual and conditional adherence. Furthermore, adaptive loss balancing ensures stable and effective learning between objectives. Our approach also features an automated pipeline for constructing high-quality, disentangled preference pairs, well as an iterative opti- mization strategy that continuously enhances both the data and the model. Experiments demonstrate that method outperforms strong baselines substantial improvements Success Ratio and conditional fidelity across a variety of benchmarks. Our framework not only advances the state of controllable image generation, but also provides new insights into preference-bact based learning with multiple, potentially conflicting objectives.
both textual and conditional alignment, yielding
Acknowledgements. This work was supported in part by the National Natural Science Foun- dation of China (62402432, 62293554, U2336212) and Zhongguancun Academy, Beijing, China (20240313).
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14


Reviews:
[{"review_id": "cRL6Y8iaUD", "reviewer": "Reviewer_2Tkg", "summary": "This paper introduces BideDPO, a framework for conditional image generation that is specifically designed to resolve conflicts between a user's text prompt and any other conditioning input (like Canny edge). Existing methods struggle to balance these two constraints, especially when they conflict often leading to poor controllability.", "questions": "1. Could the method scale to more than 2 such conditionings?\n2. How reliable is the VLM based check while constructing the preference data given some of these input conditionings/styles are subtle in the image?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper considers tackling a very practical problem of resolving conflicts between multiple conditionings, which is faced while utilising/controlling majority generative models.\n2. The strongest claim is extending DPO to handle competing objectives by mitigating gradient entanglement.\n3. The qualitative results shown in Figure 5 are of impressive quality, and prove the effectiveness of the 2 objectives working simultaneously.", "weaknesses": "1. The observed degradation at Iteration 4 suggests the model begins to overfit to the biases and narrow distribution of the data it generates itself? Please provide more intuition for why/why not this may be the case.\n2. The main weakness is the unproven quality of the VLM-based preference scoring. If the VLM is not a good proxy for human preference on novel, abstract, or conflicting constraints, then the reported gains may be less strong.", "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": "nYCfWJX9P0", "reviewer": "Reviewer_6jGu", "summary": "This paper proposes to alleviate the common issue of conflicts between conditional images and textual prompts in the task of conditional image generation. The authors propose a novel disentangled preference-based optimization technique to alleviate these conflicts.", "questions": "Please refer to my comments in Weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a disentangled preference-based optimization technique, which helps mitigate the frequent conflicts between conditional images and text prompts in conditional image generation tasks.\n- An automatic Disentangled, Conflict-Aware Preference DPO data pipeline is presented, streamlining the process of handling conflicting conditions.\n- The authors construct a DualAlign Benchmark, enabling robust evaluation of a model’s ability to resolve conflicts between visual and textual conditions.", "weaknesses": "- The proposed method is quite straightforward and lacks significant novelty.\n\n- The comparison with state-of-the-art post-training methods is limited; the paper only benchmarks against DPO and SFT.", "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": "i7x55aBkzF", "reviewer": "Reviewer_VPYe", "summary": "This paper addresses a critical challenge in conditional image generation: reconciling conflicting guidance from text prompts and structural/ spatial conditioning inputs (e.g., depth maps, Canny edges). The authors first identify two key conflicts hindering model controllability: Input-Level Conflict (semantic mismatch between conditioning images and text) and Model-Bias Conflict (learned generative biases overriding text alignment even when text and condition are compatible). To solve these issues, the paper proposes BideDPO, a bidirectionally decoupled Direct Preference Optimization (DPO) framework.", "questions": "- Why were key SOTA conditional generation methods (e.g., ControlNet++, LooseControl, OmniControlNet) excluded from the baseline comparisons? Would BideDPO maintain its performance advantages when competing with these methods, especially in scenarios with strong structural constraints?\n- How does Qwen2.5-VL-70B’s SR judgment correlate with human evaluations? Have you tested other VLMs to confirm that the observed SR improvements are not VLM-specific artifacts?\n- The automated data pipeline uses VLMs to filter \"high-quality\" preference pairs, but what thresholds (e.g., VLM confidence scores) are used to accept/reject samples? How sensitive is BideDPO’s performance to these thresholds?\n- Would BideDPO work for non-structural conditional inputs (e.g., style prompts, color palettes, semantic labels)? The current experiments focus on spatial/ structural conditions—no evidence supports generalization to other condition types.\n-What strategies could address the overfitting observed in the 4th iterative loop? For example, would integrating external datasets (instead of solely self-generated data) or adding regularization to the preference pair loss prevent performance degradation?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Unlike naive DPO (which suffers from gradient entanglement between text and condition signals), BideDPO’s decoupled preference pairs and adaptive loss balancing provide clear, independent optimization signals for each objective. This design effectively addresses the \"trade-off dilemma\" in multi-constraint generation.\n- The automated data pipeline resolves the critical bottleneck of scarce conflict-aware DPO data for conditional generation. By leveraging LLMs for prompt generation and VLMs for quality control, the pipeline enables scalable, self-driven data creation—avoiding labor-intensive manual annotation.\n- The DualAlign benchmark is a valuable contribution, as existing benchmarks (e.g., COCO, ImageNet) do not explicitly test text-condition conflict scenarios. It enables standardized evaluation of multi-constraint alignment, which will benefit future research.\n- Experiments cover multiple conditioning modalities (depth, Canny, soft edge), include ablation studies for core components (adaptive loss balancing, preference pair disentanglement), and validate robustness on COCO—strengthening the credibility of BideDPO’s effectiveness.", "weaknesses": "- The paper only compares BideDPO against naive DPO and supervised fine-tuning (SFT) on FLUX variants. It fails to include critical SOTA methods in conditional image generation, such as ControlNet++ (Li et al., 2024, which improves conditional control via consistency feedback), LooseControl (Bhat et al., 2024, for generalized depth conditioning), and OmniControlNet (Wang et al., 2024, for multi-modal control). Without these comparisons, BideDPO’s competitiveness in the broader conditional generation landscape remains unproven.\n- While the paper mentions DPO variants like RankDPO (Karthik et al., 2024) and SPO (Liang et al., 2024) in related work, it does not compare BideDPO against them. For example, RankDPO handles scalable ranked preferences—relevant to BideDPO’s preference-based optimization—and omitting this comparison obscures BideDPO’s advantages over specialized DPO adaptations for image generation.\n- The text SR is measured using Qwen2.5-VL-70B, but the paper provides no justification for choosing this VLM over alternatives (e.g., GPT-4V, Gemini Pro Vision) or validation of Qwen2.5-VL-70B’s reliability in judging text-image alignment. There is also no comparison between automated VLM scores and human annotations—leaving uncertainty about the accuracy of the SR metric.\n- All experiments are conducted exclusively on FLUX-family models. The paper does not test BideDPO on other widely used conditional generation frameworks (e.g., Stable Diffusion with ControlNet, MidJourney fine-tuning variants). This limits conclusions about BideDPO’s applicability to different model architectures.\n- The iterative optimization strategy (alternating data generation and fine-tuning) likely incurs significant computational costs. The paper provides no analysis of BideDPO’s training time, GPU memory usage, or inference speed compared to baselines—critical for real-world deployment.\n- The DualAlign benchmark focuses on structural/ spatial conditions (depth, edges) but excludes other common conditional modalities (e.g., semantic masks, human poses, style references). This limits BideDPO’s validation to a subset of conditional generation scenarios.\n- The paper notes performance degradation at the 4th iteration (due to overfitting to self-generated data) but offers no mitigation strategies (e.g., data augmentation, regularization for preference pairs). This limits the practicality of the iterative loop for long-term training.", "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": "40yCqMChg2", "reviewer": "Reviewer_1Gg9", "summary": "This paper addresses a crucial and underexplored problem in conditional text-to-image generation: the conflict between guidance from textual prompts and auxiliary conditions (e.g., depth maps, Canny edges). The authors compellingly categorize these conflicts into \"Input-Level Conflict\" and \"Model-Bias Conflict.\" To resolve these issues, they propose BideDPO, a novel framework based on Direct Preference Optimization (DPO). The core contributions are threefold: 1) A Bidirectionally Decoupled DPO (BideDPO) algorithm that disentangles the optimization signals for text adherence and condition fidelity, combined with an Adaptive Loss Balancing strategy. 2) An automated pipeline to generate conflict-aware, disentangled preference data using a Vision-Language Model (VLM) as a judge. 3) An iterative optimization strategy that progressively refines both the generator and the training data. The authors introduce a new benchmark, DualAlign, to specifically evaluate performance in conflict scenarios. Experiments on state-of-the-art models like FLUX show substantial improvements in both text alignment and condition adherence over baselines like SFT and naive DPO.", "questions": "1. Regarding the VLM oracle: Could you elaborate on the reliability of Qwen2.5-VL-70B as a preference judge? Have you performed any analysis of its failure modes or systematic biases? What is the risk of the model overfitting to the VLM's specific preferences rather than a general human alignment, especially during the iterative optimization? Did you consider using multiple different VLMs for ensembled judging to improve robustness?\n2. Regarding the computational cost: Could you provide an estimate of the computational resources (e.g., GPU type and hours) required to complete one iteration of your framework (i.e., data generation and model optimization)? This would provide valuable context on the practicality of the iterative approach.\n3. Regarding the Adaptive Loss Balancing: The ALB weights are computed on a per-batch basis. Did you experiment with or consider using a moving average of the loss values to calculate the weights for a more stable signal? Do you have any insights on how sensitive this mechanism is to the batch size?\n4. Regarding the generality of the method: Your method is demonstrated on conditions where fidelity can be measured with clear geometric metrics (MSE for depth, F1 for edges). How do you foresee BideDPO extending to more abstract conditions, such as artistic style from a reference image or high-level scene layouts, where defining a \"condition-disentangled pair\" and a corresponding error metric is less straightforward?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel and Technically Sound Method:** The core idea of BideDPO is both novel and elegant. Decoupling the preference pairs for text and condition is a clever solution to the \"gradient entanglement\" problem that plagues naive multi-objective DPO. The mathematical derivation is clear (Sec 3.1, Appendix F), and the Adaptive Loss Balancing (ALB) mechanism using a stop-gradient is a pragmatic and effective way to ensure balanced training without introducing instability.\n2. **Innovative and Valuable Data Generation Pipeline:** The lack of suitable preference data for this specific task is a major bottleneck. The proposed automated pipeline (Fig. 3, Algorithm 1) is a significant contribution in itself. Using a VLM to programmatically generate disentangled positive and negative samples for both text and condition is highly innovative. This data-centric approach, especially the iterative refinement loop, is powerful and could be adapted for other multi-objective alignment tasks.", "weaknesses": "1. **Over-reliance on the VLM Oracle:** The entire framework, from data generation to evaluation (Success Ratio, SGMSE/SGF1), heavily relies on the judgments of a single VLM (Qwen2.5-VL-70B). The paper lacks a critical discussion of the potential limitations and biases of this VLM. For example, if the VLM has its own biases (e.g., favoring certain styles or objects), the generator might overfit to these VLM-specific preferences rather than a more general notion of human preference. This is a potential point of failure for the iterative refinement process, as biases could be amplified over time.\n2. **Limited Novelty in Data Decoupling Strategy:** The method for generating disentangled preference data, while effective, arguably lacks fundamental novelty. The core strategy of isolating one variable (e.g., text alignment) while holding the other (condition fidelity) constant is a standard analytical technique for factor disentanglement, not a new concept. This approach is analogous to creating modular reward functions in multi-objective reinforcement learning. Consequently, this aspect of the work can be viewed more as a clever and practical engineering solution for applying DPO to a multi-objective problem, rather than a novel advance in preference learning itself.\n3. **Stability of Adaptive Loss Balancing (Minor):** The ALB weights are calculated on a per-batch basis from the loss magnitudes. This approach, while simple and effective, could potentially introduce noise into the training process, especially with smaller batch sizes. While the empirical results are strong, a brief discussion on the sensitivity to batch size or potential alternatives (e.g., using an exponential moving average of the losses) could strengthen the paper.", "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)