Uni-CoT: Towards Unified Chain-of-Thought Reasoning Across Text and Vision
Authors: Luozheng Qin
GONG JIA
Yuqing Sun
Tianjiao Li
Haoyu Pan
Mengping Yang
Xiaomeng Yang
Chao Qu
Zhiyu Tan
Hao Li
Venue: ICLR 2026 Poster (2026)
Keywords: Unified Model;Multi-Modal;Chain-of-Thought; Image Generation; Image Editing;
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Chain-of-Thought (CoT) reasoning has proven effective in enhancing Large Language Models (LLMs) on complex tasks by decomposing problems into step-wise solutions. However, extending CoT to multi-modal settings remains challenging, as it requires modeling transitions of visual states alongside textual reasoning. Existing approaches often underperform due to limited capacity to model visual transitions or fragmented architectures. To overcome this limitation, we introduce Uni-CoT, a Unified Chain-of-Thought framework that captures structured visual transitions and seamlessly aligns them with textual logic, enabling coherent multimodal reasoning.
To mitigate the computational and training challenges inherent to multi-modal reasoning, Uni-CoT introduces a two-level reasoning paradigm: a macro-level CoT for high-level planning and a micro-level CoT for localized subtask execution. This hierarchical design reduces computational overhead while maintaining coherence. Additionally, Uni-CoT incorporates a structured training paradigm with auxiliary tasks to stabilize optimization and improve generalization. Experiments on reasoning-driven image generation and understanding benchmarks demonstrate that Uni-CoT achieves state-of-the-art performance and remarkable generalization, underscoring its potential for complex multi-modal reasoning. Code: https://github.com/Fr0zenCrane/UniCoT.

Published as a conference paper at ICLR 2026
UNI-COT: TOWARDS UNIFIED CHAIN-OF-THOUGHI REASONING ACROSS TEXT AND VISION
Luozheng Qin Jia Gong ! Yuqing Sun Tianjiao Li
Haoyu Pan'
Mengping Yang' Xiaomeng Yang' Chao Qu Zhiyu 2. Hao L
Shanghai Academy of Al for Science FFudan University *Nanyang Technological University (qinluczheng, gongjia, sunyuqing) @sais Esais org.en
ABSTRACT
Chain-of-Thought (CoT) reasoning has proven effective in enhancing Large Lan- guage Models (LLMs) complex tasks by decomposing problems into step-wise solutions. However, extending CoT to multi-modal settings remains challenging, it requires modeling transitions of visual states alongside textual reasoning. Existing approaches often underperform due to limited capacity to model visual transitions fragmented architcctures. To overcome this limitation, we introduce Uni-Co'T, a Unified Chain-of- Thought framework that captures structured visual transitions and seamlessly aligns them with textual logic, enabling coherent mul- timodal reasoning. To mitigate the computational and training challenges inherent multi-modal reasoning, Uni-CoT introduces two-level reasoning paradigm: a macro-level CoT for high-level planning and a micro-level CoT for localized sub- task execution. This hierarchical design reduces computational overhead while maintaining coherence. Additionally, Uni-CoT incorporates a structured train- ing paradigm with auxiliary tasks to stabilize optimization and improve gener- alization. Experiments on reasoning-driven image generation and understanding benchmarks demonstrate that Uni-CoT achieves statc-of-the-ee art performance and remarkable generalization, underscoring its potential for complex multi-modal reasoning. Code: https://iituubcco////eeeCCCCCCC
INTRODUCTION
Chain-of- Thought (CoT) Wei et al. 2022) enables Large Language Models to solve complex prob- lems by generating explicit intermediate steps before producing the final answer. Motivated by its success (Feng et al., 2020 Lu al.), recent works (Zhang et al. Zheng et al. (023) have paid efforts
to extend CoT to multi-modal domain, aiming to endow Multi-modal I.L.Ms (MLLMs) with analo- gous reasoning abilities for challenging vision-language tasks (Xu et al.
2024 Mu al. 2023).
Despite recent progress, modeling coherent multi-modal reasoning remains challenging. Zhang et al. (202aa): Yang et al. (2025b); Huang al. (2025) explored to strengthen the text-based reasoning of MLLMs via reinforcement learning, but performed poorly on visually grounded tasks such as geometry navigation (Wu et al. 2024b; Cheng et al. 2025). Humans, by contrast, can solve this problem easily by integrating visual state transitions into reasoning, e.g., updating a map during nav- igation. This gap calls for models that explicitly embed visual transitions rather than approximating them through text. Programmatic operations such as cropping, drawing, or plotting (Gupta & Kem- bhavi 2023 Surís al., 2023 Hu al. 2024a) approximate local transitions but fail capture global structural changes critical for tasks like jigsaw solving. Other approaches couple MLLMs with visual generators to model large visual change (Guo al. 2025b Tiang al. 2025). but their loose integration often leads to fragmented reasoning and incoherent transitions.
To address these challenges, we propose Uni-CoT, Unified Chain-of-Thoughh framework that in- tegrates structural visual transitions with coherent text reasoning as shown in Figure T Uni-CoT
'Equal contribution
1 "oorespnniing author
1 Projject leader

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Published as a conference paper at ICLR 2026
Pacro editing Sepuential Decorposition
ofeigs Thought
Oizin Subtask?
ortputt
Sabtaski
Finat
cave grinng
dorer rigit Shertigs
Text-Inage Unified Model.
Nicre-CsT for irage pentration: Self SolF-baftection
Planning
Selr-Baflection
Self- Raf-Batttctionn
Self Euflection
Final Resutt
Custas
Text-Inage Unified Model
lacro-Miern image undeestarding: Sequertiat Decompasition with Selr-Baftaction
Inified Date-af-Thnug (Uii-cat)
Finst
Seflection
Reffectinn
Uiitee shent anotas
oar
wrove
right
BG Text-Inage Unified Model
Figure 1: The multi-modee reasoning trajectory of Uni-CoT. Uni-CoT extends Chain-of- Thought to the multi-modal domain, enabling unified model to conduct step-by-step reasoning across text and images, thereby supporting complex tasks in both image generation and understanding.
built on a unified model (Deng et al. 2025 capable of both image understanding and generation, reducing discrepancies between reasoning and visual dynamics and enabling end-to-end training for coherent multi-modal reasoning. Nevertheless, realizing unified reasoning remains difficult due to two factors: (1) Computational burden: Multi-modal reasoning requires generating both textual and visual intermediates, inflating token lengths by orders of magnitude over text-only reasoning and resulting substantial computational overhead (see Section 2). (2) Training instability: Long sequences complicates long-range dependency modeling and optimization, while interleaved im- age-text generation further destabilizes training due mismatched rooss-modal dynamics.
Inspired by human cognition, where complex problems are solved through hierarchical organization of reasoning (Broadbent [977] Newell 1994), propose macro macro-micro hierarchical CoT frame- work for multi-modal reasoning to reduce complexity. At the macro level, the model first sketches global strategy, decomposes the task into manageable subtasks, and then integrates their results into coherent solution while abstracting away execution details. At the micro level, the model focuses on solving each subtask in isolation, filtering out irrelevant context. To further improve efficiency, the micro CoT is formulated as Markov Decision Process (MDP), constraining dependencies to local states. This macro- micro restructuring converts long, entangled reasoning trajectories into modular blocks, substantially reducing redundant token interactions in the attention layers and lowering the complexity of multi-modal reasoning from quadratic to nearly linear.
Moreover, we decompose multi-modal CoT learning into two parts achieve stable and effective training: (1) Macro-Level CoT Modeling: model is refined on interleaved text-image content to acquire global planning and final synthesis; (2) Micro-Level CoT Modeling: subtask execution is cast as an MDP-style process, augmented with four auxiliary tasks (e.g., action generation, reward estimation) to facilitate efficient learning. This decoupled paradigm provides supervision both global and local levels, enabling scalable training for complex multi-modal reasoning tasks.
With these designs, Uni-CoT trains efficiently on Ax00 GPUs and achieves state-of- ffate-of-the-art the-art results on image generation (Niu et al., 2025 Ghosh et al. 2023) and understanding benchmarks (Fu et al. 2023 Wang et al. 2025), showcasing its potential for multi-modal reasoning.
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2 PRELIMINARY: THE UNIFED MODEL- BAGEL
To enable unified CoT across both image and text modalities, framework builds upon BAGEL (Scalable Generative Cognitive Model) (Deng et al. 2025). a unified model that supports joint vision and language generation. We briefly introduce the core elements of BAGEL next.
Architecture. BAGEL adopts a decoder-only transformer architecture with a Mixture-of- Transformer-Expentt design (Liang et al. 2025). It consists of experts: image understanding expert and an image generation expert, which share the same architecture but have different parame- ters. These experts are bridged by a unified self-attention mechanism over shared multi-modal token sequences. This design enables flexible fusion across modalities without task-specific bottlenecks.
Specifically, BAGEL incorporates two visual encoders for image understanding and generation:
>> Vision Transformer (ViT): The ViT serves as an image understanding encoder and is initialized from SigLIP2 ( (Tschannen al. 2025), ensuring the preservation of rich semantic information. converts an input image into 70 x 70 latent grid, yielding 4,900 discrete tokens for understanding.
Variational Autoencoder (VAE): The VAE serves as the backbone for image generation and is initialized from FLUX (Labs et al. 2025), enabling the preservation of fine fine-grained visual details. It maps an input image into a 64 x 64 latent grid, producing 4,096 discrete tokens for generation.
With the above designs, BAGEL employs expert-routing mechanism that dynamically switches between experts and visual encoders for interleaved multi-modal generation:
& For image understanding, the understanding expert activates the VIT encoder to convert images into tokens, which are fused with text to enable grounded next-token prediction for tasks such as visual question answering and scene interpretation.
For image generation, the generation expert takes text and image tokens, applies a Rectified Flow process predict laElatent latent features, and decodes them into high-quality images, supporting both
image generation and image editing.
This dual-pathway design enables fine-grained visual generation and robust semantic grounding within a unified modeling framework, allowing BAGEL to handle diverse tasks under a single model.
High Complexity for Multi-Modal Reasoning. Although BAGEL provides unified inference, encounters severe computational bottlenecks when applied to step-wise multi-modal reasoning. Unlike text-only CoT, where each step typically consumes around 300 tokens for text generation, multi-modal CoT requires both image understanding and image generation within each step. result- ing in substantial overhead. Specifically, generating an image through the VAE introduces approx- imately 4,096 tokens, while encoding an image with the ViT for understanding adds another 4,900 tokens. This amounts to nearly 9,000 visual tokens on top of the 300 text tokens per reasoning step, making both training and inference prohibitively expensive. Moreover, such a token-intensive formulation poses significant challenges for optimization, often hindering convergence and limiting generalization, especially in complex tasks that demand long, compositional reasoning chains.
3 METHOD
3.1 OVERVIEW OF PROBLEM AND OUR SOLUTION
Problem Setup Given multi-moda input x, objective is to generate a multi-moda Chain- Th-Thtt (CoT) trajectory, an ordered sequence of states 7 = h1...,, hrr. to derive a final answer y. Each intermediate state h, can contain both text and images.
Normally, a MLLM म, parameterized by 0, generates this CoT trajectory autoregressively as:
h ( Jr, het,
t= 0=
where hag denotes the history (ho.... h Then the final answer is decoded from the terminal state, y Dec(Irr). This monolithic generation process requires each step attend to the entire
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history, leading to quadratic complexity:
T Craw (T) Σ -(t)= O
(2)
While this is tractable for text-only CoT, where each state ht consumes fewer than 300 tokens, becomes prohibitive in the multi-modal setting. Here, a single state includes both textual and visual tokens, resulting [hr] 10,000 (see Section which magnifies the quadratic cost and makes naive autoregressive modeling intractable.
Our Solution. To address the quadratic complexity, we introduce a hierarchical framework that decomposes the reasoning trajectory into a high-level macro-level process and fine-graince miero- level process. As shown in Figure 2 this framework comprises three main components: a macro planner, set of micro operators, and macro summarizer.
Macro Planner. Firstly, the planner generates a high-level plan Zplan = { that decompose the given to M subgoals. Each subgoal 2, specifies what problem to solve, deferring the how to the operators. The plan is generated autoregressively
Aplan = { where ~ To(- (-1 sp, Zci),
(3)
where my denotes the sequential macro-policy.
& Micro Operator For each subgoal 2;, an operator executes a localized reasoning trajectory т; to produce an intermediate result yı. This local trajectory ти conditioned on the subgoal 2; and optionally the last subgoal's is result y/-1. It is generated autoregressively as:
= [40, 4,, ,. where 14 ~ TT(. Zi, Vi 11
(4)
The subgoal's result is then decoded from the final state of the local trajectory as yı Dec(st,7))
& Macro Summarizer. Finally, the summarizer aggregates the results from all subtasks {yr, .u......ll yMr) to produce the final answer y as
Zoum Tt(- 2, Splan { W)M and y Dec(2sum)
(5)
In summary, this hierarchical decomposition transforms a single, long-history chain-of chain-of-though f (ho, hi...., (1T into a series of modular, localized reasoning blocks:
Thier [=plan @ (T1 ① ..... 1 TM) + Zsum); where т; (A1, 41 ..." A
(6)
By partitioning the full trajectory of length T into M sub-trajectories, each with average length
T~T/M, the quadratic cost is reduced as:
Chier( = O(M - 73) = O(M (T/M)) O(T2/M)
with only minor overhead from high-level planning and summarization. Moreover, by adopting Markovian transition design at the micro level (Section (.3), where each state depends only on the previous state and current instruction, the cost is further reduced O(T), thereby enabling efficient and scalable multi-modal reasoning. We detail all components in the following sections.
3.2 MACRO-LEVEL REASONING: PLANNING AND SUMMARIZATION
Drawing inspiration from human cognition, where complex problems are addressed by first sketch- ing plan of subgoals and then integrating their outcomes into final solution (Epstein et al. 2017]] Constantinescu et al. 2016), our macro-level CoT adopts two-phase structure: a planner for task decomposition and summarizer for evidence integration. We describe how macro-level CoT gov- ems muulti-moda reasoning in the following.
Macro-level Trajectory. Here, we consider mechanisms for macro-level reasoning:
b Sequential Decomposition. Humans often approach difficult problems step by step, solving inter- mediate goals sequence. Analogously, our planner generates macro plan žplan that decomposes task x into subgoals {2, autoregressively, described in Equation
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Macro-Level CoT
Att. Mask
Subtask 1 prompt
Reasoning steps
Result 1
Macro
summary
User Prompt
Planning
Subtask 2 proupt
Reasoning steps
Result 2
summary
Reorganize/ Summarize
Final Results
Racro
sunsarizer
Subtask 3 proept
Reasoning steps
Result 3 summary
Micro-Level COT
MICEO
Reasoning steps conducted by Operator
EIGRNA
Subrask 3 instruction
Reasoning Step
self- Reflection
Reasoning Step
Self-
ton
Subtask 1-1
Anchored content (without loss) Supervised content: Loss)
Figure 2: Overview of the Uni-CoT framework. Uni-CoT integrates two reasoning branches: (1) Macro-Level CoT, which decomposes task into subtasks and synthesizes their outputs, enforced by a macro attention mask that reveals only the prompt, plans, and subtask results; (2) Micro-Level CoT, which executes each subtask Markov Decision Process (MDP), with self- steps conditioned solely on the previous state and current instruction, enforced by micro attention mask that restricts visibility. High-resolution depictions of both masks are shown in Figure SI
Under the sequential decomposition mechanism, each subgoal is executed only after the previous one has been completed. The resulting hierarchical reasoning trajectory is formulated as
Thier
{ Eplam
+
(n
+ ... ① TM))
① Zaam):
(8)
where @ denote concatenate. Then the miero-Cou and last subgoal's 's result y/-1, and is generated sequentially as
{ is initialized from the subgoal 2
hi = कe (z. 1i-1; h = Te(2i,
==0
(9)
t t 1.
Finally, the macro summarization state is defined as the last subtask output as 2sum yas. and the final answer is obtained by decoding as Dec 2.um).
Parallel Decomposition. Alternatively. humans may accelerate problem solving by dividing task into independent components that can be solved in parallel. Inspired by this, we introduce a parallel decomposition strategy where subgoals are executed concurrently. The plan generation follows the same procedure above, but the hierarchical trajectory is now formulated as
Thier = {ppaan EB (+)
① Zaum):
(10)
where 1 denotes parallel execution. The T, initialize its state only on its assigned subgoal as hn Toe (2,) and follow Equation 9 to conduct whole micro-level COT. Then the macro summa- rizer integrates all outcomes to synthesis final answer as:
2sum TT(- x, {2,, 9e
y Dec((zum)
(11)
Macro Attention Mask. In macro-level reasoning, the focus lies on planning the overall task and summarizing the outcomes of all subtasks to synthesize the final result, while abstracting away low- level operations (i.e., the micro-level traces of completed subtasks) to reduce computational burden. To enforce this abstraction, we introduce a macro attention mask (Figure or Figure [a)))), which restricts visibility during planning and summarization to macro-level states only: the input, subgoals, intermediate results, and the final answer.
3.3 MICRO-LEVEL REASONING: MDP-GUIDED SELF-REFLECTION
Once subgoal is assigned by the macro planner, the micro-level CoT handles its execution. As the overall reliability of the system depends on the coherence of outputs across subtasks, the core objective of the micro-level CoT is to produce accurate and reliable results for the assigned subgoal.
To achieve this, we introduce a Self- Reflection mechanism that improves robustness and adaptability during subtask execution. As illustrated in Figure ? after completing initial attempt, the model
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evaluates the quality of its output and determines whether revision is necessary. If logical inconsis- tencies or cross- moosl mismatches are detected, the model revises the output and re-evaluates it in closed-loop feedback cycle. We detail this mechanism below.
Micro-Level Trajectory.
The reasoning trajectory for subgoal e; represented as
Tj = ( - - - -1,",, where hg ~ TT(- 2i, : for t > 0.
(12)
The trajectory initialized as hig Th (2, 11-1) or (=))),, reflecting the model's initial attempt to solve the subtask. Subsequent states refer to the revision steps through self-reflection.
At each self-reflection step, the model performs three operations: (i) generate an evaluation score evali for the current state, (ii) decide whether further refinement is needed, and (iii) apply refine-
ment if necessary, including textual editing prompt generation rev revixt and corresponding image ++1
editing rev_ing, ++ This can be formalized as
Ti {(h] + evall rev_tal rev siee +1 h(+1)
(13)
The process continues until the evaluation score exceeds threshold, yielding the final trajectory тÜ.
As described above, self-reflection process can be naturally formulated as Markov Decision Pro- cess (MDP) {(st, at, r ((+1)))) ((+=))==== (1) state st is the reasoning state 74; (2) action ot represents 1 is the evaluation score evali.
the refinement action [rev_tade [+] rev_ing. *1; and (3) reward r
A key difference remains between the raw trajectory and the MDP formulation: in the raw trajectory, each new state attends the entire history, whereas in the MDP formulation, the transition sj s'41 depends only on the current state and the fixed subgoal zq- Analogous to human self-reflection, where one typically focuses on the present state to revise work rather than recalling the full history. we further enforce this constraint by masking the attention layer. Thus, the current state is generated only from the last state and the subgoal: ht ~ TT(- 24, h)
Complexity Discussion. If self-reflection is directly modeled on the raw CoT trajectory, each new state h& attends to the full history h,tt leading to a quadratic complexity O By instead casting the process as MDP, the transition depends only the last state, yielding linear cost:
Cmspp (T:) = OT.))
(14)
Recalling Equation 3 the maaroomicrn structure decomposes a trajectory of length T into M sub- trajectories of length 7 > T/M, reducing the complexity from O(T²) to ((T2/M), With the MDP formulation applied to each sub-trajectory, the overall cost further reduces near-linear complexity:
Cuier-map (T) O(M T/M) = O(T).
(15)
This efficiency gain enables scaling self-reflective reasoning to long multi-modal trajectories.
Micro Attention Mask, To enforce locality, we introduce a micro attention mask that restricts visibility [s|_ + st) and the fixed subgoal 2,, while masking unrelated history as shown in Figure or Figure bb). This design simplifies optimization and improves computational efficiency.
3.4 TRAINING PARADIGM
The Uni-CoT training contains two parts: Macro-Level CoT learning and Micro-Level CoT learning. We briefly introduce both in below and more details are provided in Appendix Section C
For the Macro-Level CoT, we follow (Deng et al. 2025) to use a joint loss that supervises both text and image generation for planning and final synthesis. Specifically, we apply cross-entropy (CE) loss for text generation and mean squared error (MSE) loss for image generation:
Cjoim = ACE
Ler + cimage
(16)
where À is balancing coefficient that controls the relative importance of textual and visual losses.
For the Micro-Level CoT, subtask completion is supervised using interleaved multi-mode data, similarly adopting the joint loss Ljoutt for model learning. Additionally, learn MDP-based self- reflective process, we decompose learning into four auxiliary objectives: text action a image action generation, next-state prediction and reward estimation.
text generation,

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4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Dataset. We construct a multi-modal reasoning dataset containing approximately 30K samples to support both maero- and micro-level Chain-of-Thought (CoT) learning Uni-CoT. Starting from curated text or text-image prompts, we augment each prompt with explicit logical deductions and enriched visual details. The enhanced prompts are then decomposed into 2-3 subtasks using large language models (LLMs). forming macro-level planning traces. Image understanding and genera- tion models then are utilized to complete each subtask and iterative self-reflection, producing fine- grained micro-level trajectories. The final dataset includes 11K interleaved text-image pairs for macro-level planning and 20K for micro-level refinement. We also crop data from GeoPose3K (Brejcha & Cadík) 2017). PIPE (Wasserman et al., 2025). Sharegpt-4o-image (Chen al. 2025b), Echo-4o (Ye et al. 2025) to enhance the basic image understanding and generation of our model. For further detail please refer to Appendix Section
Training Setup. We build Uni-CoT on top of the unified model Bagel (Deng et al. 2025 and fine- tune it on 31k-sample CoT dataset. Training is supervised using combination of cross-entropy loss for textual reasoning and mean-squared error loss for visual reasoning. With the hierarchical design, Uni-CoT is trained efficiently on 8x NVIDIA A100 GPUs within one week. We optimize all parameters with Adam using a constant learning rate of 2e-5 and apply a linear warmup dur- ing the first 200 steps. At each iteration, we mix data sources, understanding, generation, and transition traces, uniformly sample examples, and pack them into sequences of up to 32,768 tokens. Additional training details are provided in Appendix Section
4.2 EXPERIMENTAL SETUP
In the main paper, focus evaluating Uni-CoT on two core tasks: image generation and image
understanding. Ablation study and more experiments are deferred to Appendix Section E
For the image generation task, following Deng et al. (202)) we conduct experiments adopted benchmarks: GenEval (Ghosh et al. 2023) and WISE (Niu et al.) 2025). GenEval (Ghosh) al. (023) serves as a general benchmark for assessing object-focused text-to-image alignment, while WISE (Niu et al. 2025) is a reasoning-driven benchmark designed to evaluate a model's ability to generate faithful outputs from abstract, reasoning- -rasosing-intensive prompts.
widely
For image understanding, we evaluate Uni-CoT on general multi-modal reasoning benchmarks, in- cluding MME (Fu et al.) 2023), MMMU (Yue et al., (024),, Math (Lu al.] [202)). MM- Bench (Liu et 2024bb. We further evaluate Uni-CoT on Jigsaw-R1 (Wang et al. 2025). struc- tured multi-modal reasoning benchmark involving higsaw-puzzle solving across varying difficulty levels, testing model's ability on structured visual reasoning.
4.3 RESULTS FOR IMAGE GENERATION
Quantitative Results. Table I and Table 2 shows results on GenEval (Ghosh et al. 2023) and
WISE (Niu al. 2025), benchmarking basic and reasoning-based image generation, respectively. Uni-CoT surpasses its base model Bagel on GenEval, with improvements largely, mainly relying on our macro decomposition strategy. On WISE, Uni-CoT achieves ofate-of-the-ar performance across all domains, demonstrating substantially stronger reasoning driven generation compared to open-source baselines due to self-reflection mechanism that corrects initial errors.
Qualitative Analysis. As shown in the first row of Figure 3 Uni-CoT generates a coherent plan- ning strategy that transforms a challenging, unnatural GenEval prompt into a sequence of natural intermediate prompts, enabling smoother image generation. In contrast, the second row Fig- ure 3 illlstratess the model's ability to correct semantically inaceurate outputs through multi-round self-refiection. By integrating these two mechanisms, Uni-CoT achieves reliable and significant improvements across benchmarks, Additional visualization results are provided in Appendix E
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Table Quantitative evaluation results on GenEval (Ghosh al. 2023
Uni-CoT w/o
CoT is our initial results without mudti-modal reasoning.
Type Model
Single Obj. Two Obj. Counting Colors Position Color Attri. Overall
PixArt-a Chen et
2024a
0.98
0.50
0.44
0.80
0.07
0.48
DALL-E Ramesh
0.66
0.77
0.19
0.52
Gen. Only
Emu3-Gen Gen Wang
2024b
0.98
0.71
0.34
0.81
0.17
0.21
0.54
SDXI Podell el al. 2024
0.98
0.74
0.39
0.85
0.15
0.23
0.55
DALL-E Betker et al.
2023
0.96
0.87
0.47
0.83
0.43
0.45
0.67
MD3-Medhum Usser
m
2024
0.99
0.94
0.72
0.89
0.33
0.60
0.74
FLUX 1-dev Labs 2024
0.98
0.93
0.75
0.93
0.68
0.65
0.82
LWM 2024a
0.41
01.66
0.79
0.09
0.15
0.47
SEED-X
0.97
0.58
0.26
0.80
0.19
0.14
0.49
Token["low-XL
2024
0.95
0.60
0.41
0.81
0.16
0.24
0.55
ILLUME|Wane Wang a..
2024
0.99
0.86
0.45
0.71
0.39
0.28
0.61
Famu3-Cen Wang el 2024bb
0.99
0.81
0..2
0.80
0.49
0.45
0.66
Unified
Show-o Xie al R024
0.98
0.80
0.66
0.84
0.31
0,50
0.68
Janus-Pro-7BP Chen
2025e
0.99
0.89
0.59
0.90
0.79
0.66
0.80
MetaQuery-XLi ct
2025
BAGRI Deng et al. 2025
0.99
0.92
0.78
0.87
0.53
0.64
0.79
Uni-CoT w/o CoT Uni-CoT
0.95
0.90
0.69
0.99
0.96
0.84
0.92
0.57
0.71
0.83
Table 2: Quantitative evaluation results on WISE (Niu al. w/o CoT is our initial results without multi-modal reasoning.
2025 Uni-CoT
Model
Culture Time Space Biology Physics Chemistry Overall
Jamus Wu al.
2024a)
0.16
0.26
0.35
0.28
0.30
0.14
0.23
MetaQuery Pan et 2025
0.56
0.55
0.49
0.41
0.55
Bagek-Think Deng
1202
0.76
0.69
0.65
0.58
0.70
GPT-4o Hursi el al. 2024
0.81
0.71
0.89
0.83
0.79
0.74
0.80
Uni-CoT w/o CoT Uni-CoT
0.75
0.67
0.79
0.64
0.79
0.65
0.72
0.76
0.70
0.76
0.73
0.81
0.73
0.75
Nacro Sequential Decomposition for Image Generation
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User Pronpt
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cver leit care Have
Figure 3: Visualization of the Uni-CoT multi-modal reasoning process: macro-level decomposi- tion (top), micro-level self-reflection (middle), macro-level parallel decomposition (bottom).
4.4 RESULTS FOR IMAGE UNDERSTANDING
Quantitative Results. We report results on the general multi-modal understanding benchmarks and Jigsaw-RI benchmarks Table 3 On the general benchmarks (including MME, Math and MMBench). Uni-CoT achieves slightly higher scores than its base model Bagel, indicating that it preserves broad world knowledge while also benefiting from our gost-training strategy. In contrast, on the Jigsaw-RI -1 benchmark, Uni-CoT substantially outperforms all compared open-sourced mod-
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Table 3: Image understanding results on MME, MMMU, MMBench, Math Vista, and Jigsaw-RL.
Method
General Benchmarks
JigsauRR
MME-PT
MME-ST
MMMUT
MMBenchf
MathVVstta?
Overallf
GPT-4V (chiaa et al. 2023
1409
1927
56.8
74.3
47.5
GPT-4, I-mimr QremAA AIPA
67.90
54.50
54.90
20,90
47.M
LnlemVL2..522
Qwen2. Quen2.5-VL--7 -L-L-BB Bar Hl. 2025
2347
48.70
50.60
15,80
41.12
BAGEL Dong
1687
2388
52.8
85.0
73.1
51.25
50.05
52.05
9.60
40.73
Uni-CoT
1687
2392
$2.7
85.6
73.3
51.15
61.46
59.15
18.64
47.60
UN6007
0.30
189
024
Figure 4: Complexity comparison.
Figure 5: MSE loss comparison.
els, demonstrating its strong multi-modal reasoning capabilittis, particularly in perception- -eavv tasks.
Qualitative Analysis. The bottom row of F showcases the parallel decomposition mech- anism for solving Jigsaw-R1 case. By examining candidate options concurrently, Uni-CoT can efficiently process distinct puzzle segments in parallel and subsequently integrate their results into a coherent solution. This strategy accelerates convergence and reduces error propagation across sub- tasks, underscoring the advantages of parallel decomposition in perception-heavy reasoning tasks.
4.5 COMPLEXITY ANALYSIS
we theoretically analyze that our hierarchical design can reduce the complexity from quadratic near-linear. Here, empirically validate the effectiveness of our method by measuring the actual inference cost of two variants on 100 samples: (1) naïve Uni-CoT without the proposed hierarchical framework (Uni-CoT Raw), (2) the complete Uni-CoT (Uni-CoT).
In Section 3.3
As shown in Figure Uni-CoT Raw consumes substantially more tokens than Uni-CoT and incurs an almost quadratic increase in computation the number of reasoning steps grows. In contrast, Uni-CoT requires far fewer tokens and exhibits near-linear scaling. This quadratic-versus-neet -eear- linear gap confirms the significant complexity reduction achieved by our hierarchical, interleaved reasoning framework. Notably, with 2 and multi-modal reasoning steps, Uni-CoT reduces the average token interaction count by 2.24x and 2.95x, respectively, and the reduction grows to 11.26x when the number of reasoning steps reaches 10.
We further examine the training dynamics of Uni-CoT in comparison with Uni-CoT Raw. As illus- trated in Figure 5 Uni-CoT converges substantially faster, reaching comparable loss level within only 6,000 training steps, whereas Uni-CoT Raw requires 12,000 steps. When both models are trained for 12,000 steps small dataset of approximately 10,000 samples, Uni-CoT consistently outperforms Uni-CoT Raw on WISE (0.73 vs. 0.70), indicating not only improved training efficiency but also superior output quality. More details refer Appendix Section
RELATED WORK
Multi-modal CoT with Visual Reasoning. Recent work has extended CoT to MLLMs to better
align reasoning with visual content (Zhang et al.) Mitra et al. 2024 Zheng al.
2023). RL-based
approaches improve textual reasoning (Zhang et al. 2025a Yang et al. 2025b Feng et al. 2025)
but struggle in visually intensive domains (Wu al. 2024b Cheng al. 2025
To approximate

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Published as a conference paper at ICLR 2026
visual state transitions, some methods introduce programmatic operations cropping, drawing, or plotting (Gupta & Kembhavi] 2023; Surís et al. 2023 Hu et al. 2024a|by bj Shen et al. 2025 Gao et al., 2025], which capture local changes but fail to model the global structural transtormations required for navigation or rearrangement tasks. Another direction couples MI..Ms with image or video generators to support larger-scale visual transitions for image understanding (Zhao al.)
2025b) or image generation (Guo et al. 2025a; Xue et al. 2025 Zhang et al., (025c).. Others refine outputs using external reward models (Guo (0.5b) Jiang et al. (025). However, these loosely coupled designs often yield fragmented reasoning flows, limiting overall coherence.
Hierarchical reasoning, where high-level planning guides low-level execution, is well established in cognitive science (Epstein al., 2017: Orru Longo 2018 Inspired by this principle, prior
work has explored hierarchical structures in text-only COT (Yang et al., 2025a Wu et al. (022aa). hierarchical RL \Vezhnevets et al., 2017 Nachum al., 2018), and multi-agent planning (Zhang
et al. 2025b). However, these efforts primarily aim to improve reasoning quality, controllability, or planning efficiency. In contrast, our work leverages hierarchy for a fundamentally different purpose: to reduce the training and inference complexity inherent to multi-modal CoT reasoning while pre- serving the model's reasoning ability. This focus on complexity reduction multi-modal settings distinguishes our approach from prior hierarchical methods.
Efficient Attention is an important and widely studied research direction (Sun et al. 2025 aim- ing to reduce the computational cost of transformers. Prior methods primarily focus on lowering attention complexity through sparsification (Child et al.) (019), low-rank approximations (Wang) iet al. 2020 Tay et al.] 2020), or locality-biased attention (Liu et al.) 2021). nearly without con- sideration for the model's reasoning ability. In contrast, our formulation is driven by the need to reduce the reasoning complexity itself while preserving the integrity of CoT reasoning, making our approach inherent different from prior efficient- attention designs.
CONCLUSION
We present Uni-CoT, a unified Chain-of-Thouue framework that enables coherent and grounded multimodal reasoning across vision and language within single model. By introducing a two-level hicrarchical reasoning architecture, we significantly reduce computational complexity and improve reasoning efficiency. Extensive experiments across reasoning driven image generation and editing benchmarks demonstrate Uni-CoT's superiority in both performance and interpretability. We believe Uni-CoT offers scalable foundation for future multi-modal reasoning systems.
While Uni-CoT achieves strong results on generation and understanding tasks, extending the frame- work to broader real-world applications, particularly those requiring fine-grained visual consistency or perfectly text-image alignment, remains an open challenge. We regard this as a promising di- rection for future work, where improved trajectory modeling and stronger visual-textual alignment could further expand the capability and applicability of Uni-CoT.
7 ACKNOWLEDGMENT
This work was supported by AI for Science Program, Shanghai Municipal Commission of Economy and Informatization (Grant No. 2D2567ZL-RG2N--BBXX00200
We thank Zhengbo Zhang, Li Xu, Qi Lv, Jun Gao, Qian Qiao and Zhiheng Li for their valuable discussions and constructive feedback.
8 REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. To this end, we provide detailed descriptions of the model architecture, training objectives, datasets, preprocessing steps, and eval- uation protocols in the appendix. Most of hyperparameter settings and implementation details documented. Moreover, we have released the codebase together with scripts for inference, to facili- tate independent verification of our framework and future research.
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Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. Mmmu: A massive multi-discipline multi- modal understanding and reasoming benchmark for expert agi. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9556-9567, 2024.
Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. RI-vI: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arXiv:2503. 12937, 2025a.
Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guided image editing. In NeurlPS, 2023.
Wentao Zhang, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, and Bo An. Agentorchestra: A hierarchical multi- agett framework for ceneral-purpont task solving. arXiv e-prints, pp. arxiv- 2506, 2025b.
Yuyao Zhang, Jinghao Li, and Yu-Wint Yu-Wing Tai. Layercraft: Enhancing text-to-image generation with cot reasoning and layered object integration. arXiv preprint arXiv:2504. 00010, 2025c.
Zhuosheng Zhang, Aston Zhang, Mu Li, George Karypis, Alex Smola, et al. Multimodal chain-of- thought reasoning in language models. Transactions Machine Learning Research.
Xiangyu Zhao, Peiyuan Zhang, Kexian Tang, Xiaorong Zhu, Hao Li, Wenhao Chai, Zicheng Zhang, Renqiu Xia, Guangtao Zhai, Junchi Yan, et al. Envisioning beyond the pixels: Benchmarking reasoning-informet visual editing. arXiv preprint arXiv:2504.00 028266 2025a
Zhonghan Zhao, Wenwei Zhang, Haian Huang, Kuikun Liu, Jianfei Gao, Gaoang Wang, and Kai Chen. Rig: Synergizing reasoning and imagination in end-to-end generalist policy. arXiv preprint 2rXiv:2503.24388. 2025b.
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APPENDIX CONTENT
The Use of Large Language Models (LLMs)
17
Details of Atfention Mask
17
Details of Training
18
Training Paradigm
18
C.2 Training Hyper Parameters
19
D Details of Dataset.
20
More Experiments,
22
E.1 Results for Image Editing Benchmark
22
Nano-banana like Generation
22
E.3 Ablation Study
24
More visualization
25
A
THE USE OF LARGE LANGUAGE MODELS (LLMS)
In preparing this paper, we made limited use of a Large Language Model (LLM) as general-purpose writing assistant. Specifically, the LLM employed polish the language, improve clarity, and optimize the organization of certain sections. It did not contribute to the core research process, including problem formulation, experimental design, data analysis, or interpretation results.
B DETAILS OF ATTENTION MASK
(b)
Haga
Fee-la
Sentatk Seseting
Micro-lavel Ressoning.-
m-811 leal
1k-2
Ressoning...
(-lell test
bebrask
Sesetigg
Reasoning..
Leal
1402
Figure S1: Attention mask: (a) Macro attention mask; (b) Micro attention mask.
As shown in Figure a), under the Macro attention mask scheme, the model has access only to the system prompt, high-level planning outputs, and final subtask outcomes, while intermediate reasoning traces, such textual rationales image edits generated during subtask execution,
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completely masked. In contrast, Figure (T] illustrates the Micro attention mask scheme, where each self-reflection step can attend only to the immediately preceding state (e.g., the last image-text pair) and the current subtask instruction.
C DETAILS OF TRAINING
C.I TRAINING PARADIGM
We decompose Uni-CoT training into two reasoning levels: Macro-Level CoT learning, which captures global planning and final result synthesis, and Micro-Level CoT learning, which focuses on subtask execution and self-reflection.
Macro-Level CoT Learning. The macro-level reasoning process consists of two components: a planner for task decomposition and a summarizer for evidence integration. For the planner, understanding problem, where the model takes in multi-modal
the training task is framed inputs and then produces structured textual plan, trained under a cross-entropy loss CCR. For the summarizer, the training task is cast generative problem: the model integrates both the global plan and the intermediate multi-modal outputs to generate the final solution. For understanding tasks, this corresponds producing textual conclusion (supervised by cross-entropy loss C whereas for image generation tasks, it corresponds to synthesizing both textual and visual outputs (supervised joint loss Ljoim that combines cross-entropy (CE) and mean squared error (MSE) losses). The data structures underlying these two stages are summarized in Table SI
Specifically, for image understanding, we define the CE loss Chi as:
CCB
log((;),
(SI)
where denotes the ground-truth token, 2, represents the predicted probability of that token, and C the vocabulary size.
For image generation, we first review the Rectified Flow (Liu et al.) paradigm and then give the MSE loss definition. For denoising- generation under the Rectified Flow (Liu al.) paradigm, given a clean latent Xy and a Gaussian noise sample X1, we construct the noisy latent via linear interpolation:
X t) xo t X1, E [0,
(S2)
The model is then trained predict the velocity field using the MSE loss:
Limage
E [|:
(Xo x x)]1
(S3)
Here, (x; c) denotes the velocity predicted by the model n, conditioned on noisy latent x, and context c.
Then the joint loss Ljoin defined as:
Ljoint X Cixt
MSEE
(S4)
where À is coefficient balance two losses.
The Micro-Level CoT learning. The micro-level reasoning process consists of two complemen- tary components: subtask completion and self-reflection modeling.
For subtask completion. the model is supervised with interleaved multi-modal signals. Specifically, analogous to the final result synthesis, we employ a joint loss Ejoint that integrates cross-entropy loss for textual outputs and mean-squared error loss for image predictions, thereby ensuring alignment across modalities.
For self-reflettion modeling, as detailed in Sec. 3.3 formulate the self-reflective procedure within a Markov Decision Process (MDP) framework. Based on this formulation, we introduce four auxiliary objectives for self-reflection modeling:
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Table S1: Macro-Level CoT training tasks. Content enclosed in
indicates the target be learned.
Objective
Data Structure
Loss
Global Planning
[System Prompt, Multi-Modal Inputs, "Planning Prompt")
Cross-Entropy Loss (CCC)
Result Synthesis
[System Prompt, Multi-Modal Inputs, Generated Plan, Multi-Modal Intermediate Results, "Final Results")
Joint Loss (Cjoin)
Table S2: Micro-Level CoT training tasks. Content enclosed in indicates the target to be learned.
Objective
Data Structure
Loss
Subtask Completion
[System Prompt, Subtask Prompt, "Initial Image Text Results"]
Joint (Cjuin)
Text Action at
[System Prempt, Subtask Prompt, Current Image & Text, "Editing Prompt"]
Cross-Entropy Loss (L
Image Action de
img
[System Prompt, Subtask Prompt, Current Image & Text, Editing Prompt, "Edited Image"]
Mean (C
Square
Error
Loss
Next-State 8++1
[System Prempt, Subtask Prompt, Edited Image, " "Image Analysis")
Cross-Entropy Loss (C
Reward rt
[System Prompt, Subtask Prompt, Edited Image, Image Analysis, "Evaluation"
Cross-Entropy Loss (CT))
Text Action at Generation, where the model evaluates the intermediate results and predicts editing instruction at", supervised by cross-entropy loss Cixt
Image Action img Generation, where the model generates visual modifications img condi- tioned on the textual instruction att, supervised by mean squared error loss Lims
Next-State St+1 Prediction, where the model analyzes and summarizes the status of the modified image, supervised by cross-entropy loss Cixt.
Reward rt Estimation, where the model regresses scalar feedback signal textual description that measures the quality of the intermediate reasoning step relative to the final task objective, supervised by cross-entropy loss Le
The data structure is detailed in Table S2
C.2 TRAINING HYPER PARAMETERS
Owing to our decomposition of interlcaved image-text CoT via MDP modeling and high- data construction, the training of Uni-CoT quite efficient, as all experiments can be accomplished on NVIDIA A100 GPUs. Following common practices, we utilize Fltetonten Fully Sharded Data Parallel (FSDP), and mixed-precision training for better computation efficiency. Throughout the training process, all the parameters in the unified model are optimized using Adam optimizer with a constant learning rate of 2e-5. Additionally, we employ linear warmup learning rate schedule, increasing the learning rate from all data used for understanding and generation expert training, randomly sample from the combined dataset, and pack the samples into sequences with target length of 32, 768 tokens.
the first 200 steps. During each training step, combine
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T21 Pronpts Sourcing
T2I Prompts Sourcing
Prompts Enhancement
Inage (BAGEL-Think)
Subtask Planning/ Decomposition
self- reflective Inage Editing
Subtask Inage Generation/ Edating
Evaluation Ref Refinement
Subtask Evaluation Refinement
Image Editing
Subtask
InterLeaved Recolllevel Reasoning
Interleaved Micro-leve Reasoning Data
(b)
Data curation pipeline for hierarchical reasoning. We collect T2I prompts from various datasets, then for (a)rooco-eevel reasoning data pipeline: prompts first enhanced via GPT-4o or Qwen3, then decomposed into several sequential or parallel subtasks. Then GPT-4o as well as Bagel- Think used for subtask image generation, evaluation and (finementt.b)Mirroolo data pipeline: we use Bagel-Think model to generate preliminary images based on collected T21 prompts. We then perform several rounds self-reflection, using GPT-4o first evaluate on current images and generate refinement instruction, then generate edited images conditioned on refinement instruction.
Figure S2:
reasoning
D DETAILS OF DATASET.
Data curation process. Figure S2 illustrates the data pipeline used for data curation. We col- lect interleaved text-image data for Macro-level and Micro-level reasoning paradigm, respectively. We prepare text-to-image generation prompts from multiple datasets seed prompts for prompt expansion.
For Macro-level reasoning data, then enhance the prompts in the following two aspects: (1) We first check if the given prompts entail domain expertise knowledge or common-sense reasoning. such reasoning logical induction exist in prompts, result of reasoning deduction. For example, if the original prompt describes "a melting ice cream in desert sun", then the deduced rewritten prompt should elaborate the object states under given condition, i.e. "dripping, puddle on hot sand" (2) We then enrich the generation prompts via adding auxiliary visual detail attributes to concretely illustrate any abstract concepts or descrip- tion, especially those regarding art styles, vibes, environment, etc. After prompt enhancement, we employ models such as GPT-4o or Qwen-plus to decompose the enhanced prompts into 2-3 subtasks such that complex image generation goal inferred by the enhanced prompts are broken into simpler and logically coherent sequential or parallel sub-goal. We then utilize models capable of image gen- eration such BAGEL-Think subtask instruction. We also employ VLM models (GPT-4o) to evaluate on the results subtask excecution and generate corresponding subtask refinement instruction. The textual detail of subtask planning, subtask evaluation and refinement, and the intermediate images generated in all subtask steps, all collected as interleaved Macro-level data.
rewrite the prompts explicitly state the
GPT-4o, to perform image generation and editing following the
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Robotics/VLA Dataset
Scientife Video Dataset
fiction Movies
+
Image Extraction
Image Extraction real-world environment
Image Extraction
frames demanstrating
key frames featuring objects & scientific phenomena
incidents
spetia//emppral/ccusss changes
S Text Extraction description/ explanation on scientifii phenomena, foundational natural laws, experimental procedures
Textual commands of rabotic actions; description spatial analysis
Text Extraction
<p
S
Text Extraction
Character dialogues, visual understanding frames
Filtering
Filtering
Filtering
Multimodal Dataset about Ressoning
Muttimodal Scientifo Reasoning
Multimodal Dataset about generalized reasoning open-domainns
(b)
(c)
Figure S3: We leverage existing real-world large-scale datasets that embody rich image and textual information and prepare three structured and scalable data curation pipelines across various domains. (a) Images and textual data are extracted from open-source Robotics/VLA datasets to form multi- modal samples that naturally embeds spatial causal relationship and physical interaction reasoning. (b) For scientific and procedural reasoning, we leverage open scientific video websites to extract key frames from videos that illustrate scientific phenomena, and generate textual explanation about the state transitions between extracted frames. (c) For more generalized and commonsense-leve reasoning featuring daily-life scenarios, prepare images and texts movies. Causally-related con- secutive frame sequences could be extracted from movies, and textual descriptions about causality of character behaviors transitions across the frame sequences could be formulated by collecting character dialogues and performing visual analysis on the frames.
For Micro-level reasoning data, we directly generate preliminary images via BAGEL-Think. Next perform several rounds of self-reflection. We repetitively evaluate generated or edited images from the previous round using VLM models such as GPT-4o, outputting an assessment of image quality and cestruction-ollowiinn order to align with the intention the original prompts better. We then employ image generation models (i.e. GPT-4o) capable of image editing to edit the images in prior rounds according to the refinement instruction. We collect the textual and visual output in each self-reflection loop as the interleaved text-image data for Micro-level reasoning SFT.
well as an instruction on how should the image be refined in
Data information. In total, we initially collect approximately 11K complete long-form multi- modal Chain-of-Thought trajectories, which be decomposed into IK samples for macro-level learning and 20K samples for micro-level learning. Moreover, thanks to the decomposable nature of our framework, the model can also benefit from fragmented data. To further enhance its ba- sic capabilities, we additionally incorporate 1 14K text-to-image generation samples, 68K samples from echo4o (Ye et al. 2025) dataset and 46K samples from sharegpt4o-image dataset (Chen al., 2025b), and 46K image editing samples from sharegpt-o-image. To preserve UniCoT's multimodal understanding capabilities, we incorporate approximately 100K samples from the LLaVA-OV (Li
al. 2024) Vision Stage training data, ensuring balanced and evenly distributed dataset.
Future Works for Data Collection. While our current dataset sufficient to refine the base uni- fied model and elicit strong multimodal reasoning capability, its relatively small scale and predomi- nantly synthetic composition still limit the model's ability to generalize to complex real-world see- narios.
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To address this limitation, propose hybrid data-scaling strategy that integrates high-qualige real-world data with carefully structured synthetic data. This approach enables us to expand data di- versity, regulate reasoning complexity, and enhance alignment between visual transitions and textual CoT steps in a cost-effective manner (see Figure S3
For spatial reasoning and Object manipulation, plan to leverage large, scalable datasets from the robotics and VLA domains (e.g., Open X-Embodiment), where sequences of temporally consecutive frames naturally encode causal structure and physical interaction, and textual commands of action- able tasks assigned to the robots could be validly reformed as textual guidance between image state transitions.
For scientific or procedural reasoning, rich multimodal datasets depicting scientific phenomena, controlled experiments, and procedural workflows can be sourced from open scientific video repos- itories (e.g., Jo),,, offering clear visual evidence of state transitions between video frames, along with off-the-shee textual explanation of entailed natural laws or experimental procedure.
For more generalized and commonsense-leved reasoning featuring daily-life scenarios, particularly those involving spatial, temporal, or causal dependencies, we design a scalable pipeline to extract causally meaningful frame sequences from publicly released movies and other long-form videos without restriction on specialized topic areas. Textual guidance or explanation about the character behaviors transitions across frame sequences could be formulated by collecting character dia- logues and performing visual analysis on the frames. This provides abundant, diverse, and realistic visual transitions support multimodal CoT learning.
Within these data pipelines, images could be extracted directly from videos or valid from source dataset and would only require lightweight processiin regarding quality and sizes. The GPU usage or API budgets would then mainly be contributed to the generation and the formulation of structured and meaningful textual guidance between image state transitions. As result, these data pipelines could be feasibly scaled to the level of 100k-500k considering that images are mostly natural and would not require synthetic image generation. Together, these directions form a practical roadmap towards large-scale, high-quality, and generalizable multimodal CoT data collection.
E
MORE EXPERIMENTS.
E. RESULTS FOR IMAGE EDITING BENCHMARK
Experiment Setup. For the image editing task, we benchmark Uni-CoT on GEdit-Bench (Liu et al. 2025b), RISE (Zhao et al. 2025a), and KRIS (Wu et al. (0025)). GEdit-Bench (Liu et al. 2025b) general benchmark that provides diverse set of real-world editing tasks. In contrast, RISE (Zhao et al. 2025a) focuses on reasoning-informe
editing across temporal, causal, spatial, and logical dimensions, while KRIS (Wu et al.) (025b) serves as a diagnostic benchmark categoriz- ing editing tasks into factual, conceptual, and procedural knowledge types.
Quantitative Results. First, we evaluate the basic editing capability of Uni-CoT GEdit- Bench (Liu et al. 2025b). As shown in Table S3 our model achieves competitive performance compared with other approaches.
Furthermore, we report KRIS and RISE benchmark results in Table S4 and Table S3 Surprisingly, on KRIS benchmark, Uni-CoT outperforms all open-source baselines across perception, concep- tual, and procedural categories. Remarkably. also surpasses the closed-source Gemini 2.0 in overall score, highlighting its robust and interpretable editing capabilities under complex reasoning instructions. On RISE benchmark, Uni-CoT demonstrate comparable performance with Gemini 2.0 with respect both overall performance on the four reasoning categories and the sub-dimension evaluation metries of instruction reasoning, appearance consistency and visual plausibility.
E.2 NANO-BANANA LIKE GENERATION
Recently, Google introduced Nano-Banana (Nao Banana 2025 unified image generator that extends the limits multimodal generation. Impressively, it succeeds in tasks once considered unattainable, such generating photorealistic landscapes from highly abstract geospatial inputs,
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Table S3: Quantitative comparisons on GEdit-Bench (Liu et 2025b).
Higher better (1).
[GEdit-Bncch-NNT]
Type
Model
G_SCC G_PQ G.O
G_SC G_PQ
G_O
Gemini 2.0 (Kamf & Brichtova, 2025
6.73
6.61
6.32
5.43
6.78
5.36
Private
GPT-4o (uustt al. 20244)
7.85
7.62
7.53
7.67
7.56
7.30
Instruct-Pix220 (Brooks et al.
2023
3,58
5.49
3.68
MagicBrush (Zhang et al. 2023
4.68
5.66
4.52
AnyEdit Yu et al. 2024
3.18
5.82
3.21
Open-source
OmniGen (Xiao et al. 2024)
5.96
5.89
5.06
(Liu St-plX-Edit
2025a
7.09
6.76
6.70
7.20
6.87
6.86
BAGEL (Deng et al. 2025
7.36
6.83
6.52
7.34
6.85
6.50
Uni-CoT
7.91
6.24
6.74
8.01
6.30
6.87
Table S4: Quantitative comparisons KRIS (Wu al.
2025b).
Model
Attribute Perception
Spatial Perception
Perception
Temsoraa Perception
Social Natural Sclence
Conceptual Reasoning
Logical
Proceduaal Knowledge
Instruction Decompose
Overall
Score
Reasoning
Score
GPT-4o (OpmAh [Hus ctal. 2026
83
79.08
68825
80.06
81.37
71.56
78.32
Gemini-2.0 Kampl & Wrsiiiii 0T7
66.33
63.31
63.92
65.26
19
$6.94
59.65
54.13
71
62.
62.41
Suep 30 1istin (in blayinn 2023
69.57
61.08
63.25
66.70
66.58
60.88
62.32
49.05
51.92
51.99
61.43
Douban Beteeaae 2024
70.92
59,17
40.58
63.30
65.50
61.19
62.23
47.75
60.58
$4.17
60.70
RAGEL DDag
64.27
$2.54
Think Doig
68.33
61.40
Uni-CoT
72.76
72.87
67.10
71.85
70.81
66.00
67.16
53.43
73.93
63.68
68.00
including satellite imagery and isohypse line maps (top of Figure S4). These outcomes shows that Nano-Banana exhibits high-level implicit reasoning mechanism to bridge modalities with substan- tial domain gaps, rather than merely memorizing visual correspondences.
We target reproducing this success by introducing reasoning-driven generation process that trans- forms implicit reasoning mappings into explicit and interpretable sub-tasks. Concretely, we for- mulate the transformation from an isohypse map to a natural landscape image as three sequential steps:
2d-to-3d, interpolating the 2D contour lines into continuous 3D terrain representation;
3d-crop, projecting plausible camera view from the global 3D terrain;
3d-to-real, rendering the localized 3D view into a photorealistic landscape.
This decomposition mirrors how human experts, such geographers or cartographers, would men- tally reconstruct terrain from contour lines before imagining realistic viewpoint.
To validate this hypothesis, we curate a dataset of 3K multi-modal Chain-of- Chain-of-Thought (CoT) geogra- phy samples based on GeoPose3K [Brejcha & Čadík 2017) explicitly aligned with this decomposi- tion. We then refine our model this dataset with around 12,000 steps. As shown in the middle row of Figure S4 our model present a strong generation ability on this specialized test set By contrast, when the model fine-tuned directly on raw Sathyyse-landscapp pairs without reasoning decom- position, training becomes unstable and the generations exhibit incoherent or distorted structures (bottom row of Figure S5)..
Furthermore, we analyze the training dynamics by visualizing the mean squared error (MSE) loss curves of Uni-CoT (with explicit reasoning) versus Uni-CoT w/o Think (direct fine-tuning). As shown in Figure S5a direct training starts with a substantially higher initial loss, reflecting the large domain gap between symbolic contours and naturalistic landscapes. More critically, while multi- modal CoT converges smoothly within ~2k iterations, direct fine-tuning shows stable conver- gence trend even after 12k iterations. This divergence underscores the intrinsic difficulty directly mapping from 2D contour modalities realistic 3D landscape imagery. By contrast, introducing
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Table S5: Quantitative comparisons on RISE Zhao al. 2025a).
Model
Temporal Causal Spatial Logical Overall
GPT-4o (Hurst ct al. 2024)
34.1
32.2
37.0
10.6
28.9
Gemini-Z.0 Kampt & Brichtova, 2025
8.2
15.5
23.0
4.7
13.3
BAGEL-Think (Deng et al. 2025
5.9
17.8
21.0
1.2
11.9
BAGEL (Deng ct al. 2025
2.4
5.6
14.0
1.2
6.1
Uni-CoT
8.2
18.9
20.0
1.2
12.5
structured reasoning decomposition not only accelerates convergence but also improves robustness, highlighting the effectiveness of reasoning-driven generation in bridging heterogeneous modalities.
E.3 ABLATION STUDY
Ablation of Reasoning Component. We isolate the contribution of each reasoning component via three baselines: (1) w/o CoT: only with simple text reasoning; (2) w/o macro-CoT: only micro- level self-reflection; (3) w/o micro-CoT: only macro-level decomposition without iterative refine- ment. As shown in Table [6feeft on GenEval (Ghosh al., 2023), macro-CoT is substantially more critical than micro-CoT. This is likely due to the nature of GenEval prompts: they are program- matically generated and often deviate from natural human phrasing. Macro-level decomposition helps bridge this gap by progressively transferring these synthetic prompts into more natural forms, thereby facilitating smoother generation. In contrast, as shown in Table 56 right, on WISE (Niu et al.) 2, micro-CoT contributes to performance substantially more than macro-CoT. We hy- pothesize that this arises from the nature of WISE prompts: they are abstract yet relatively simple, and therefore benefit more from fine-grained reasoning (micro-CoT) rather than explicit high-level decomposition (macro-CoT).
Ablation on Training Strategy. We further examine whether the proposed hierarchical training paradigm can simplify and stabilize multimodal optimization. To this end, we compare Uni-CoT with a naïve long-chain baseline (Uni-CoT Raw) that is directly fine-tuned on full interleaved tra- jectories. As shown in Figure S5] the loss of our method converges more than twice as fast (6k vs. 12k steps), and Table S7 indicates that it consistently outperforms Uni-CoT Raw across all cate- gories. We note that these experiments were conducted under limited computational resources, and therefore the Uni-CoT results reported in Table S7 do not reflect the final performance of our full model.
Table S6: Ablation of CoT GenEval and WISE.
GenEval (Ghosh et al. 2023
WISE
(Nuu et al.
2025
Model
Single
Two Cnt.
Color Pos.
Attr.
Overall
Cult.
Time
Space
Bio.
Phys.
Chem.
Overall
w/o CoT
0.99
0.95
0.82
0.90
0.55
0.69
0.81
0.75
0.67
0.79
0.64
0.79
0.65
0.72
wlo micro-CoT
0.99
0.96
0.83
0.89
0,58
0.72
0.83
0.73
0.70
0.76
0.69
0.77
0.65
0.71
w/o macro-CoT
0.99
0.95
0.81
0.87
0.57
0.71
0.81
0.75
0.68
0.74
0.66
0.79
0.71
0.73
Uni-CoT
0.99
0.96
0.84
0.92
0.57
0.71
0.83
0.76
0.70
0.76
0.73
0.81
0.73
0.75
Table S7:
Ablation of Training on WISE.
Model
Cult. Time Space Bio. Phys. Chem. Overallf
Uni-CoT Raw 0.73 0.67 0.75 0.60 0.75
0.65
0.70
Uni-CoT
0.75 0.66
0.78
0.70
0.78 0.71
0.73
Ablation on Training Strategy. We examine the training dynamics of our approach (Uni-CoT) against a naïve baseline (Uni-CoT Raw) that directly fine-tunes on long-chain interleaved data. As shown in Figure SSb) our method converges substantially faster, reaching comparable loss level within only 6,000 steps, whereas the baseline requires 12,000 steps. When both models are trained dataset of ~00,00 samples, we further evaluate them WISE (Table SS). Across all metrics, our method consistently surpasses the long-chain baseline, demonstrating higher training efficiency and superior output quality.
for 12,000 steps
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Ablation study for base model To further assess the generality of our Uni-CoT framework, replace its original base model, Bagel, with BLIP3o (Chen et al. 2025a). BLIP3o is a unified mul- timodal model built the autoregressive backbone Qwen2.5-VL, supporting both image under- standing and image generation. For understanding, it encodes visual inputs using a CLIP encoder and predicts text tokens via standard cross-entropy training. For generation, the model first produces intermediate visual features in a flow-style manner, which then condition a diffusion transformer to synthesize images. Through this unified design, BLIP3o aligns visual perception and visual genera- tion within a single coherent architecture.
We evaluate this setup on the WISE benchmark and investigate the effectiveness of key Uni- CoT reasoning component, the micro-Col self-reflection mechanism. Specifically, we fine-tune the BLIP3o (the BL.P3o-NEXT-eiit-V VAE (BLIP30 Team 2025) version for its competitive image editing capability) on our training dataset using 8xA100 GPUs for 100,000 steps, which took ap- proximately hours. We then compare three baselines to analyze the contribution of relf-reflection: (1) BLIP3o: the original pretrained model without any fine-tuning; (2) BLIP3o-sft w/o Uni-CoT: the
model fine-tuned on our dataset #without self-reflection signals; (3) BLIP3o-sft w/ Uni-CoT: the model fine-tumed
dataset *with * micro-CoT self-reflection.
As shown in Table S8} directly fine-tuning BLIP3o on our dataset already yields a substantial per- formance improvement. This is likely because the selected BLIP3o version -EXX-e--E VAE) exhibits relatively weaker reasoning alignment in its original form. Furthermore, with our reasoning architecture incorporated, BLIP3o-SFT w/ Uni-CoT achieves additional improvement of approximately 0.8 points across all metrics compared to BLIP3o-SFT w/o Uni-CoT. This clearly demonstrates the effectiveness of our proposed multimodal reasoning strategy. The visualization of reasoning trajectory of BLIP3o is shown in Figure S9
Table S8: Ablation study for base model
Model
Culture Time Space Biology Physics Chemistry Overall
BLIP3o
0.30
0.33
0.48
0.37
0.36
0.22
0.33
BLIP3o-sft w/o Uni-CoT
0.54
0.54
0.59
0.42
0.61
0.57
0.55
BLIP3o-sft w/ Uni-CoT
0.65
0.62
0.67
0.48
0.70
0.69
0.64
F MORE VISUALIZATION
Additional image editing-based geography reasoning cases are provided in Figure S7 while further examples of macro-CoT, micro- CoT, and hybrid macro-mice CoT are illustrated in Figure S8
We present the image editing visualization results in Figure S6
25

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Published as a conference paper at ICLR 2026
Nano-Banana (Google)
Input
Output
Input
Output
Uni-CoT (Ours)
Input
2dto3d: CoT step 1
3d-crop: CoT step
3dtoreal: Output
Uni-CoT Think
Input
Out
Input
put
Quen-Inage
Input
Output
Output
Bagel
Input
Output
Output
GPT-5
Input
Output
Output
Figure $4 Qualitative Results for Nano-Banana like Generation.
26

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Published as a conference paper at ICLR 2026
0320
1728
0.38
038
024
2000
6005
6202
2000
20020
10000
2000
4000
6C06
8002
1000s
22033
Traiiing step
Tratting
(a) MSE loss comparison for geometry task.
(b) MSF Loss comparison for training strategy.
Figure S5: MSE loss comparisons.
Belistle image editing on KRS prompt (Bewwoning-hseed Edtting Benchmark)
ColnpPonet
Oneal
UK CETICO
plaze
BOSTER SECRY.
serouss vilee badly
lagg,,
Figure S6: Qualitative Results for Reliable Image Editing, Uni-CoT demonstrates considerable image editing abilities, further supporting the effectiveness its micro-level CoT reasoning. It can generate textual editing instructions and modify the current visual state accordingly.
27

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Published as a conference paper at ICLR 2026
User Preept.
Planning
Subtasik1
Subtask2
Subtaskk
Final Result
duscest raodit sareer
tener oertee veggsiie
Cos es IETra
Peoept
Planning
Subtasii2
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how refepe 1iee sed' lomel lime witer
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User Proept
Planning
Subtask1
Subtask2
Subtasks
Final Result
Thix
realiet Lentrce the
Indepe
crupe rig
User Proept
Planning
Subtesk1
Subtask2
Subtask3
Final Result
cettur meo Ten emeriacte
tocke mitoon la deaere
Deviil regeiie vieer
A at reoper regar
Figure S7: More visualization results for Geography Reasoning.
28

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Published as a conference paper at ICLR 2026
User Prompt
Planning
Subtsek
Subteek
Final Resutt
falimdyy se la
and pacte your
Trui Finkl:
senen mmage
Planning
Final
cutipdo Famur de ceratt mery
pien camn JUDcA VI31 wecer san Dearde
nadic paccu all ditiiiggh deskkipeppiine
Thesisl millo posionn plattes
User Prompt
Planning
Subtask
Subtask
Final Result
not cin decon
cacrato Areorriooo sios daven
Manoa saaa paro
per viarr parr
The Treasdee Tressdepcaion sec12.n
Prompt
Result
Abort white coonng with bod
Booer arenn cearridee Reerader'
lng
DaN
Analte momeon oae
Prompt
Planning
Resull
voti iive air cano vaid arastin
cad nall sig sihoo conlic silutie?
lodde wik slep twss cotesaeo con
cloetig aeterlnde cater the on
segictio gaann engen ceronse
th
bweking ancilbcte
HaR--in BInCk ONITI MEUU
gloning Mroe vrida
Prompt
Final
Accdeng benherre
vnta coster dilids coltectto
stanc coliditty cvilgge calti
cner addir criiss
senuall cesrva e wele
redi bakkt Choress
mak cats
detiled tattaaoo
aidh wate Vi
User Promet
Planning
Subtask
Subtask
Finel Result
coraus unioed de
secoeed Fhe
Bixc Reporer
sal sie be ste
fow lop
Lraks vhattie
Fiael Poovl
User Promet
Planning
Bublask
Bubbask 2
Finaal Reesutt
undiralieh shote arteertie
SiCcoi vnfiguaar
Figure S8: More Visualization of our Uni-CoT thinking process.
29

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Published as a conference paper at ICLR 2026
User Prompt
BLIF30
BLIPS-- UNIOTT
UniCoT
chaaaleon pertectsy camoutlaged
Mgatral grean
skin and cattor cloodly
camscrlaee agenic given
sun nonnog hoal
Prompt
Relf-Ruflecion
BLIP3o- UniCoT md
Adl mexot in the She Nose at AM Tolayo
gra arrens wald tnoeeet,
Nine ng tise night with people
exvesports en to
rahare booee
User Prompt
BUP30
BLIP3o- UniCoT
Self- Soff-Reflection
BLIS UnlCoT t-t
TTa inage sucoasstiily
An as tomolll horvect celebration in North
food
center rtage drinng
ronated turkey surrounced other
Assoc ared with harvest
ELIPd
UniCoT
Belf-Raflectin
BUP20- UniCoT m=
coliesa oculpaure thacl, with anstintttie
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below: model should ganarate image featuring the Ohr the
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Figure S9: More Visualization of our Uni-CoT with BLIP3o model's thinking process.
30


Reviews:
[{"review_id": "KTJ4iM0OBS", "reviewer": "Reviewer_wVH1", "summary": "The paper proposes a unified multimodal Chain-of-Thought framework combining text and vision reasoning in one model. By introducing a macro–micro hierarchical structure and self-reflective MDP reasoning, it achieves coherent and efficient multimodal reasoning with reduced complexity. Results on multiple benchmarks show strong performance, though challenges remain in fine-grained visual consistency and generalization to dynamic scenes.", "questions": "None", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper proposes a unified framework that integrates textual and visual Chain-of-Thought reasoning in one model. It introduces a hierarchical macro–micro design that improves reasoning structure, coherence, and interpretability.\n2. The method employs an MDP-based self-reflective process that reduces complexity from quadratic to near-linear while enhancing robustness.\n3. The method achieves strong results on multiple benchmarks for both image generation and understanding.", "weaknesses": "1. Despite reduced complexity, multimodal reasoning remains computationally expensive due to the large number of visual tokens per step.\n2. The dataset used for training is relatively small and partially synthetic, which may limit generalization to real-world multimodal scenarios.\n3. The paper could be strengthened by providing a quantitative comparison of token usage during inference against baseline models.", "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": "rmUAAF0pYc", "reviewer": "Reviewer_MA39", "summary": "This paper introduces Uni-CoT, a unified framework for multi-modal Chain-of-Thought (CoT) reasoning that integrates both textual and visual reasoning steps.  The proposed method employs a hierarchical macro-micro reasoning structure: the macro-level handles task decomposition and summarization, while the micro-level executes subtasks via a Markov Decision Process (MDP) with self-reflection.  The authors address the computational challenges of multi-modal CoT by reducing the complexity from quadratic to near-linear through localized reasoning and attention masking.  Experiments on image generation and understanding benchmarks (e.g., GenEval, WISE, MME, Jigsaw-R1) demonstrate state-of-the-art or competitive performance.", "questions": "Please refer to the weaknesses part. More results on understanding tasks will support the conclusions of this paper.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The technical contribution under the unified model such as BAGEL is effective. \n- The proposed method significantly reduces computational complexity, making long-horizon multi-modal reasoning more tractable.\n- The paper includes both quantitative and qualitative results across multiple challenging benchmarks, including reasoning-driven generation and understanding tasks.\n- Ablation on CoT mechanisms and training strategies provides strong evidence for the contribution of each component.\n- The authors provide code, training details, and dataset construction pipelines, enhancing reproducibility.", "weaknesses": "- The evaluation results on understanding tasks are not sufficient. The authors only provide results on the MME benchmark, which is not representative of supporting the claim of unified tasks. Lack of more general and widely-used benchmarks such as MMMU, MMBench, OCRBench, MathVista, MathVision, etc. \n- Experiments are conducted only on the BAGEL backbone.  It is unclear how the framework would perform with other MLLMs or in a more model-agnostic setting.", "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": "VxHclm1cf0", "reviewer": "Reviewer_xg6H", "summary": "This paper proposes a hierarchical framework to extend chain-of-thought (CoT) reasoning to multi-modal settings. The authors introduce Uni-CoT, which unifies text and visual reasoning within a single model by integrating structural visual transitions and coherent textual logic. To manage the high computational cost of multi-modal reasoning, they design a macro–micro hierarchical CoT, where the macro level plans subgoals and the micro level executes them via Markov Decision Process–based self-reflection. Experiments on reasoning-driven image generation and understanding benchmarks show state-of-the-art results and improved interpretability, demonstrating Uni-CoT’s potential for scalable and coherent multimodal reasoning .", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Originality: This paper proposes a unified framework for multi-modal chain-of-thought (CoT) reasoning, bridging visual and textual reasoning within a single coherent process. The Markov Decision Process formulation in this paper reduces the computation overhead.\n\n2. Quality: The proposed method is well-motivated, combining goal decomposition with self-reflective decision-making. The experiments are comprehensive, covering both understanding and generation tasks, and the results consistently support the framework’s effectiveness. \n\n3. Clarity: The paper is generally well-organized and easy to understand.\n\n4. Significance: The work contributes meaningfully to the emerging area of unified multimodal reasoning. It also formally analyzes the complexity of efficient MDP attention.", "weaknesses": "1. The main novelty is kind of limited; there are some similar works using CoT for image generation refinement, like LayerCraft [1]. Efficient attention is also a well-studied area. However, combining them might be the first try.\n2. The training dataset for CoT image generation purely depends on synthetic data. This limits generalization to open-domain or real-world visual reasoning tasks.\n3. Figure S3 in the appendix seems not accurately reflect a realistic ground-level scene, which raises questions about the accuracy of synthetic training data.\n\n[1] LayerCraft: Enhancing Text-to-Image Generation with CoT Reasoning and Layered Object Integration, Yuyao Zhang, Jinghao Li, Yu-Wing Tai, in NIPS 2025", "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": "54D6XkaCxy", "reviewer": "Reviewer_GGF1", "summary": "This paper proposes Uni-CoT, a unified hierarchical Chain-of-Thought (CoT) framework for multimodal reasoning. The approach aims to bridge textual and visual reasoning by introducing two levels of CoT:\n\n1. Macro-CoT, which handles high-level planning and decomposition of a multimodal task into subgoals.\n\n2. Micro-CoT, which focuses on localized subtask execution, reasoning over both text and image modalities with shorter context windows.", "questions": "1. What is the base multimodal model used?\n\n2. Was SFT with plain CoT data attempted as a control baseline?\n\n3. How much does each level (macro/micro) contribute individually?\n\n4. Can the authors provide concrete statistics or examples showing context length reduction and training efficiency gains?\n\n5. How are the auxiliary tasks (e.g., reward estimation) implemented? are they learned jointly or pre-trained separately?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Strengths\n\n1. The idea of hierarchical reasoning decomposition (macro for planning, micro for execution) is conceptually sound and aligns with cognitive and hierarchical RL principles.\n\n2. The insight that only the latest local state is needed for Micro-CoT, rather than the entire reasoning chain, is practical and can indeed reduce context length and compute cost.\n\n3. The paper is well-motivated by the challenges of multimodal reasoning, and the integration of CoT-style reasoning into a unified visual–text model is timely", "weaknesses": "1. Hierarchical decomposition (macro/micro planning) has been explored in multiple contexts, including text-only CoT, hierarchical RL, and multi-agent reasoning. The paper does not sufficiently articulate what is new in Uni-CoT beyond combining these known techniques.\n\n2. The paper omits critical information about the base model, data scale, loss functions, and training setup, and most are deferred to the appendix. This makes it difficult to assess reproducibility and fairness of comparisons.\n\n3. In Tables 1 and 2, Uni-CoT shows only modest gains over plain CoT baselines (especially on WISE). The discussion around why and when Uni-CoT helps is minimal.\n\n4. The ablation studies do not isolate the effects of the macro vs. micro components, nor do they show what happens when hierarchical supervision is removed.\n\n5. Complexity reduction claim is unconvincing. The claimed reduction from quadratic to near-linear attention complexity seems theoretical. Given that the SFT data only involves 2–3 subtasks, it’s unclear that Uni-CoT’s decomposition meaningfully reduces overall computation compared to a single CoT pass.\n\n6. Qualitative results lack rigor. For instance, in Fig. 3 (line 440), the generated image contradicts the textual description (“sealed container” appears open), suggesting incomplete alignment between reasoning and 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": "0QgRZSuzpy", "reviewer": "Reviewer_XhV7", "summary": "This paper proposes Uni-CoT, a Unified Chain-of-Thought framework that captures structured visual transitions and seamlessly aligns them with textual logic. They use a macro-level CoT for high-level planning and a micro-level CoT for localized subtask execution.  Overall, Uni-CoT performs better than the baselines.", "questions": "I am not very familiar with the literature on unified models, but I would like to ask why there are missing data entries in Tables 1 and 3. Is this due to limited computational resources that made it difficult to obtain the results, or are there other reasons?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. There are still relatively few reasoning frameworks for unified models, and this study effectively fills that gap.\n\n2. The paper is easy to follow, the figures are very clear, and the code is open-sourced.\n\n3. The experiments are highly detailed, and the provided examples are intuitive and illustrative.\n\n4. The selected datasets are convincing (e.g., WISE), and the experimental results are strong.", "weaknesses": "Although the paper has several notable strengths, its weaknesses are also apparent. The proposed method does not appear sufficiently novel since it builds a reasoning framework using prompting-based scaffolding, an approach that has already been extensively and systematically explored in the LLM and VLM literature. Therefore, the method itself is not particularly innovative. Nevertheless, this paper is likely among the first to adapt such approaches to unified models, which remains a meaningful contribution.", "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)