ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning
Authors: Jiawei Gu
Yunzhuo Hao
Huichen Will Wang
Linjie Li
Michael Qizhe Shieh
Yejin Choi
Ranjay Krishna
Yu Cheng
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Reasoning
Interleaved Chain-of-Thought
Unified Model
Primary area: generative models
TL;DR: ThinkMorph interleaves text and image reasoning in a unified model, achieving large gains on vision-centric tasks and revealing emergent multimodal intelligence from just ~24K training samples.

Abstract: Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that text and image thoughts should function as complementary, rather than isomorphic, modalities that mutually advance reasoning. Guided by this principle, we build ThinkMorph, a unified model fine-tuned on $\sim$24K high-quality interleaved reasoning traces spanning tasks with varying visual engagement. ThinkMorph learns to generate progressive text–image reasoning steps that concretely manipulate visual content while maintaining coherent verbal logic. It delivers large gains on vision-centric benchmarks (averaging 34.7% over the base model) and generalizes to out-of-domain tasks, matching or surpassing larger and proprietary VLMs. Beyond performance, ThinkMorph exhibits emergent multimodal intelligence, including unseen visual manipulation skills, adaptive switching between reasoning modes, and better test-time scaling through diversified multimodal thoughts.
These findings suggest promising directions for characterizing the emergent capabilities of unified models for multimodal reasoning.

Published as a conference paper at ICLR 2026
THINKMORPH:
EMERGENT PROPERTIES IN MULTIMODAL INTER- LEAVED -HOOGHH REASONING
Jiawei Gu Yunzhuo Hao" 22 Huichen Will Wang" Linjie Li Michael Qizhe Shich Yejin Choi Ranjay Krishna Yu Cheng
National University of Singapore, ZZhejiang University, University of Washington, Stanford University, "absolute AI, The Chinese University of Hong Kong
Homepage: http::://thhiikooopp.gggggguuu
Code: httt:p///ggtttabb.omm//////n
Models and Datasets; https///t/////gggggggaga.....
ABSTRACT
Multimodal reasoning requires iterative coordination between language and vi- sion, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that text and image thoughts should function as complemen- tary, rather than isomorphic, modalities that mutually advance reasoning. Guided by this principle, we build ThinkMorph, a unified model fine-tuned on ~24K high- quality interleaved reasoning traces spanning tasks with varying visual engage- ment. ThinkMorph learns to generate progressive text-image reasoning steps that concretely manipulate visual content while maintaining coherent verbal logic. It delivers large gains on vision-centria benchmarks (averaging 34.7% over the base model) and generalizes to out-of-domain tasks, matching surpassing larger and proprietary VLMs. Beyond performance, ThinkMorph exhibits emergent multi- modal intelligence, including unseen visual manipulation skills, adaptive switch- ing between reasoning modes, and better test-time scaling through diversified mul- timodal thoughts. These findings suggest promising directions for characterizing the emergent capabilities of unified models for multimodal reasoning.
INTRODUCTION
Multimodal reasoning Lin al. 2025) is not single-past perception task but an iterative process
that interweaves language and vision reasoning. This process remains particularly challenging for
current models in vision-centric domains such spatial reasoning (Li al. 2025c Cai et al. 2025)
where success requires moving beyond describing images toward interrogating and manipulating vi-
sual elements. While textual Shain-of-Thought (hereafter, "text thought") (Wei et al. (022) has ad-
vanced verbal reasoning, it contributes little to multimodal reasoning: models still falter when prob- lems demand more than textual description (Hao al. 2025) Jiang al. 2025). These limitations motivate a shift from language driven reasoming to genuinely cross cross-modal reasoning mirroring the human ability to tackle complex problems through trdin--andskeeeee strategies.
To emulate such think-and-sketad behavior, researchers have explored multimodal interleaved Chain-of-Thouge (hereafter, "interleaved thought"), yet existing approaches remain limited. Tool- augmented designs rely external visual modules such cropping tools (OpenAI) or specialized sketching models (Hu et al.) 2024 Zhou et al.) 2024), making the reasoning process indirect and brittle. Unified models (Team 2204 Chern et al. 2024) offer more integrated alternative but have yet to yield a generalizable recipe for mutual advancement between text and image reasoning. For instance, MVoT (Li et al.) 2025b) introduces interleaved action representations for maze solving,
"qqual contribution.

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Published as a conference paper at ICLR 2026
yet its textual component is confined to simplistic action labels isomorphic (Fu et al. 2024a to its generated images, showing little generalization beyond training domains.
We posit that achieving generalizable multimodal reasoning requires treating text and images as complementary, rather than isomorphic, modalities that jointly advance reasoning. Building on this principle, we introduce ThinkMorph spanning four tasks with varying levels visual engagemens, from chart highlighting to spatial path overlays (Figure 2). Each instance is meticulously designed ensure high-quality multimodal su- pervision, where textual reasoning and visual manipulation progress hand-in-hard toward solutions. ThinkMorph achieves substantial gains on vision- centric tasks, averaging a 34.74% % improvement over its base model, with striking increases of 85.84 % on Spatial Navigation and 38.75% on Jig- saw Assembly. Beyond these quantitative improvements, it provides controlled setting to examine when and how interleaved reasoning helps multimodal problem solving.
unified model fine-tuned ~24K interleaved traces
Compared across reasoning modes, ThinkMorph's interleaved reasoning consistently outperforms text-only and visual-only approaches by 5.33%. Despite its modest data scale, ThinkMorph gen- cralizes robustly to out-of-domain benchmarks, surpassing the larger VL3.5388 on spatial reasoning in SAT by achieving 52.67% compared to 49.33% and matching Gemini 2.5 Flash on MMVP perception at 80.33%. Beyond accuracy. ThinkMorph reveals emergent properties indicative of higher-level multimodal intelligence: PROPERTY D-Unseen Visual Manipulations, where the model generates visual edits unseen during training; PROPERTY @-Autonomous Mode Switch- ing. where it adaptively allocates reasoning effort between modalities; and PROPERTY O-Bettte Test-time Scaling with Diversified Thoughts, where it explores broader multimodal solution spaces, yielding stable accuracy gains such as +8.0% on Jigsaw Assembly, Collectively, these prop- erties indicate that interleaved reasoning is not merely a coordination mechanism but an engine for emergent behaviors, offering a window into how unified models internalize and adapt multimodal problem-solvine strategies.
Overall, our work makes the following contributions:
Systematic analysis of interleaved multimodal reasoning. We present ThinkMorph as unified framework for scalable investigation of interleaved reasoning, providing the first systematic study of when and how multimodal interleaving surpasses text-only and visual- only modes. Through carefully curated, high-quality data and the scalable generation of ~24K mutually reinforcing interleaved traces, ThinkMorph achieves substantial improve- ments across diverse out-of-domain benchmarks while revealing the underlying dynamics that make interleaved reasoning effective and generalizable.
Emergent properties in interleaved reasoning. We identify distinctive emergent behaviors that arise from interleaved reasoning, including unseen visual manipulations, autonomous switching between reasoning modes, and diversified multimodal exploration. These behaviors highlight the model's ability to internalize adaptive strategies that balance symbolic and perceptual reasoning.
New avenues for test-time scaling. We show that the advantages of interleaved reasoning persist and amplify during test-time scaling, enabling broader exploration of multimodal solution spaces and improved robustness across unseen domains.
THINKMORPH: INTERLEAVED CHAIN-OF-THOUGHA GENERALIZATION
Let Pa denote a unified multimodal model with parameters A. We consider multimodal ques- tion Q = (Qiex, QimE) that may contain both textual and visual elements. For reasoning tasks, the model is prompted to generate a sequence of intermediate tokens to reach final answer. Un- Mhain-of-Thhugee
approaches that only produce textual tokens f, ThinkMorph can also generate image tokens é, resulting in interleaved thoughts that combine both modalities. Formally, the thought sequence is defined as T = (mi, my..... m,), where m ~ Po(m; I, mp, mi.... ".i....,,,;1... m,-1) and m, E 0t. In practice, while special tokens are omitted from the notation for simplicity, modality transitions age thoughts are delimited by <image start> and <image end> tokens, enabling seamless switching between textual and visual reasoning within the sequence.
like conventional
controlled via delimiter tokens. For instance, im-
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Interteaved training data
<ST> Text token
Text token
<ST>
<image_start>
Img token
Img taken
<image_ends
<ST>
Text token
A
Text thought
Img thought
Text thought
Interleaved Thought
special token
reasoning progress
2
question
answer
MSE loss
:CE loss
Figure 1: Design of Interleaved Training Data for Progressive Multimodal Reasoning
Interleaved Thought Collection Advancing multimodal reasoning through interleaved thought presents a foundational challenge: defining what counts as meaningful interleaving is inherently difficult. Unlike textual reasoning, visual thinking is hard externalize, whether through language or sketches. For many visual reasoning tasks (Hao et al. 2025 Li et al. (022cc: Yin et al.) [025 humans often arrows, rough shapes, symbols that show relationships but exact details. This ambiguity makes it hard to set clear criteria and to collect data at scale.
To address this challenge, we construct enriched dataset encompassing four representative tasks that demand different levels of visual engagement and cross-modal interaction, as illustrated in Fig- ure Each task supports concrete, verifiable intermediate visual thoughts grounded in specific visual manipulations. We carefully design task-specific interleaved reasoning sequences where text and images are not treated isomorphic representations but provide complementary cues that pro- gressively guide the reasoning process toward a solution, as shown in Figure [
The following tasks
demonstrate how alternating between textual and visual tokens facilitates cross cross-modee modal reasoning:
Jigsaw Assembly (Wang et al. 2025c) requires determining the correct arrangement of scrambled image patches to reconstruct the original image. To recover the patch ordering o², the initial to- kens provide hise textual descriptions of each puzzle piece's ' local content. The subsequent tokens then visualize the re-arranged pieces according to the current ordering hypothesis a, sup- plying holistic spatial context that text alone cannot capture. The final tokens perform syntactic verification of the reconstructed assembly. & Spatial Navigation (Wu et al. 2024) involves finding safe route from starting point goal path x* E P through maze, the initial tokens establish a coarse global abstraction. The tokens then render the visual trajectory of πT" and the final é tokens articulate and verify the corresponding sequence of moves. b Visual Search (Wu & Xie 2024) involves answering a question about target object in an image Qime, To locate the target object, the initial f tokens hypothesize and describe the area of interest. The è tokens subsequently draw bounding box, offering an explicit visual anchor. object's attributes and confirm the prediction. Chart Refocus (Fu et al. 2025) requires answering question about data visualization. To do so, the initial tokens identify relevant data elements. The i tokens highlight corresponding regions of interest, and the final tokens perform value extraction and computation.
grid map, avoiding obstacles. To determine safe
The final tokens verbalize
Task
Data Source
Count
Visual Manipulation
Curation Steps
Jigsaw
SAT
Raa
al.
2024
6,000
Newly generated ques- tions from a customized pipclinc Overlaying mazes with paths highlighted with red lines and arrows Highlighting Regions with Red Bounding Boxes
Visualizing re-arranged pieces
Assembly
ADE20K (Zhou et a..
2017).
Omni3D (Brazil et al.
2023
Spatial
N/A
6,000
Newly generated ques- tions from a customized pipeline
Navigation
Visual Scarch
Visual
CoT
Shao ct al.]
6,990
Filtering for valid (question, answer) with MLLMs other criteria
2024
GQA [uudson & Man-
ning 2019
VSR (Liu al.
2023
Chart Refocus
ChartQA Masry et al. 2022). Refocus (Fu et al. 2025
Highlighting Regions 6,000
Filtering for valid (question, answer) with MI.MMs other criteria
with Red Bounding
Boxes Overlays
Table 1: Summary of Questions Used for Training ThinkMorph.
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ThinkMorph Multimodal Reasoning
Jigsaw Assembly
Spatial Navigation
Visual Search
Chart Refocus
The image sclowis a 222 jasw puazic.. Your tast determine correct
Your goal 36 g player start the grd while svoldingholes solution sequeece
What the color of the man's cxp?
Which attitude represent the smollest
goo between women and men?
aranngemntt.
yellow
bostts anabus the siop traiap Sua 2: dunblyy the and viiiado playa slatt al the 0,30 </thiso-
vihako thered lourdding lron sloud plood around the man's head and capt.. V/llato
two specif buo and libed -/llaaro
<tras..... anabya the visual content of plece and decomine naturd image froe
<thibo-.. Lanalyyee highighteed 3123 visualtaation the don'tt disorimintte against
ethenko rest
Nishighting
are no visible disconiinuttnn Ocitnoaaee>
<ttenko.... There
chhint Laartt lyseetly pioned The rnd prisec enn ot the mosiet oteint> canseee//poost 0XE0N-12AAM slanowrre
Night photd aroo, the mar's cogo is gray so the correct onswer is C. <thies--anseo C-ta- nswwe
cittinoo canseer oi don't
discliinate fear </a c/riosero
Emergent Properties
Unseen Visual Manipulations
Autonomous Mode Switching
the bell pepper the pistare red or ow? (hove swvwny
G image the lowar comer meiingg compore
Interleaved Thought
5.3% Switch
Text thought
7.29%
doose
Superior Test-Time via Diversifed Thoughts
To dotormine whether the bellis or yollow, Ineed 30 closety exomiine the color sepulor shown
<ttenks... To delemine the corned sesserbly
Ureas on the czneinully betooen lar
Video Space
plocos ... recorisiructing the as
strinee
Visit Naatrie
+8.0%
Scating
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prov dedi which foctures a 2060 up-S Shot se tn central locu... sheeed this detalleed that the pepper yelliw... w/taako
<hhint..... cxaminn the result viual natural... compasellin unailerned
55
Zoom-in
Image Inpainting
Number Sempatttti EUNKK
Text
Figure 2: ThinkMorph Overview. ThinkMorph synergistically interleaves language and vision to advance multimodal reasoning across four representative tasks (top). Beyond performance gains on in- and out-of- domain benchmarks, interlcaved reasoning unlocks emergent properties (hottom).
Data Synthesis Table I summarizes the data sources, curation pipeline, and visual manipulations used for each task. In total, we curate 24,990 questions spanning diverse domains. Questions for Jigsaw Assembly and Spatial Navigation are generated using our custom synthesis pipeline, whereas those for Visual Search and Chart Refocus are carefully curated through a human-in-the-loop MILM filtering process. For instance, in the Visual Search task, we observe many questions from existing Visual CoT datasets (c.g., GQA and VSR) are ambiguously phrased, contain incorrect answers, or highlight irrelevant objects in the solution images. To enhance quality and difficulty, we enforce constraint that the target object's 's bounding box must occupy between 1% and 30% of the image area. This selective filtering reduces the dataset from 144K to 6,990 high-quality questions. to the interleaved traces, we derive two unimodal baselines: textual thoughts obtained by prompting GPT-4.1 to solve each task step-by-step, and visual thoughts using only the image outputs from the interleaved reasoning traces. All details are provided in Appendices B.6 and Bnd D
addition
Training and Evaluation We adopt Bagel as our base model and train on its official implemen- tation. As shown in Figure [ we optimize dual objectives: Mean Squared Error (MSE) loss Limg for image tokens and negative log-likelihood loss Lieat for text tokens. Hyperparameters vary across training settings, and detailed configurations are provided in Appendix [B.8] For in-domain eval- uation. we use VSP-main-task (Wu et al.) (2024) as the benchmark for Spatial Navigation, our constructed VisPuzzle for Jigsaw Assembly, and the official Chart Refocus (Fu et al.) (2025) test set (a subset of ChartQA (Masry et al.; 2022)). For -ft-of-domain evaluation, we further test on broad suite of vision-centric multimodal benchmarks, including VStar (Wu & Xie 2024). BLINK (Fu et al. 2024b). MMVP (Tong et al. 2024c), SAT (Ray et al. 2024) and CV-Bench (Tong et al.] 2024a). Specifically, for BLINK, its subset Jigsaw falls under the jigsaw assembly task, which differs substantially from task VisPuzzle. We treat it distinct metric, hereafter de- noted as BLINK-J. All evaluations are conducted using the v vlmevalkit framework (Duan et al.] 2024) for consistency and reproducibility. For most benchmarks, we follow the framework's origi-
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nal evaluation pipeline. For tasks where answer extraction and correctness could not be determined by exact matching, we adopt GPT-5 as an LLM-as---Judge Additional details are provided in Ap- pendix B.7
WHEN DOES INTERLEAVING IMPROVE MULTIMODAL REASONING?
ThinkMorph exploits the complementarity between text and images to enable interleaved reasoning, where each modality contributes distinct yet synergistic information toward solving problem. To probe the scope and underlying mechanisms of this advantage, we ask two central questions: when does interleaved reasoning outperform unimodal approaches, and how does this advantage emerge? To answer these, we fine-tune Bagel-7B under three distinct reasoning only, and interleaved and evaluate their performance across all tasks (Table 2)
visual-
Spatial Navigation Visual Search
Jigsaw Assembly
Chart Refocus
VSP
VStar*
VisPuzzle BLINK-J*
ChartQA MMVP*
Bagel-7B
0.83*
55.49
35.00*
67.33
62.05
70.33
Text Reasoning
49.17
56.02
63.50
68.67
81.66
76.33
Visual Reasoning
85.50
58.63
61.25
47.33
73.08
73.00
Interleaved Reasoning
86.67
63.87
73.75
73.33
79.78
82.66
Table 2: Reasoning Mode Comparison. Bagel-7B is tested under think mode (*: no-think mode for tasks where thinking prevents Bagel from generating answers). ChartQA results the average performance on horizontal and vertical bar chart questions. *: out-of-dommin benchmarks. Best second-best
Interleaved reasoning excels on vision-centric tasks. On tasks that demand sustained visual en- gagement, Tornh''rph's interleaved reasoning consistently outperforms all other modes. The effect most pronounced in Spatial Navigation, where the base model nearly fails at 0.83% but interleaved reasoning reaches 86.67%, marking a dramatic 85.84% improvement. Substantial gains also appear in Jigsaw Assembly, with a 38. 75% in-domain improvement and strong out-of of--dmnain generaliza- tion on BLINK-J (+6.00%). For Visual Search, ThinkMorph improves performance on the out-of- domain VStar benchmark by 8.38%. Averaged across these three vision-centric tasks, interleaved reasoning yields 34.74% improvement over the base model and surpasses the next-best mode by 5.33%, establishing as the most effective reasoning strategy for visually grounded problems.
When is interleaved reasoning necessary? Chart Refocus highlights when visual manipula- tion in reasoning traces is essential versus sup- plementary. On the in-domain ChartQA bench- mark, text-only reasoning slightly outperforms in- terleaved reasoning (+ (+1.88%), indicating that vi- sual input adds little beyond text. In contrast, the out-of-domain MMVP benchmark, interleaved reasoning generalizes better, surpassing text-only reasoning by 6.33%. This contrast clarifies both when interleaved reasoning helps and how its ad- vantage arises.
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Across vision-centric tasks, interleaved reason- ing works best when text and images continu- ously inform each other. Visual tokens enable steps that text alone cannot: in Jigsaw Assembly, re-arranged pieces reveal mismatches;
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Spatial Navigation, overlaid arrows validate routes; and in Visual Search, bounding boxes pinpoint object lo- cations. Chart Refocus, however, shows that the need for interleaving depends on task demands (Figure g. In ChartQA, textual reasoning already
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Figure 3: Visual Highlighting: Role varies from supplementary (ChartQA) to essential (MMVP).
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identifies key elements (e.g.. Austria and Norway with their values), making later visual highlighting helpful but unnecessary. In MMVP, visual grounding is essential for spatial cues that text cannot express, such confirming that "the duck's beak points rightward." Overall, text-only reasoning suffices when additional visual information in the reasoning traces redundant, but interleaved reasoning is crucial for generalizing to tasks requiring precise visual grounding manipulation.
4 EMERGENT PROPERTIES IN INTERLEAVED REASONING
Beyond performance improvements, interleaved reasoning also exhibits emergent properties that arise naturally during training and evaluation, showing behaviors characteristic of multimodal intel- ligence (see lower panel of Figure 2
EMERGENT PROPERTY @: Unseen Visual Manipulations The model develops accurate and meaningful visual manipulations unseen in training data when generalizing to out-of-domain multimodal tasks, actively advancing the reasoning process.
We identify eight distinct types of unseen visual manipulations, with zoom-in operations being the most common. As shown in Figure [ (lower panel) and Figure 4 these manipulations also in- clude impainting, multi-box generation, motion m forecasting, perspective transformation, and region cropping, among others. These emergent behaviors are not rare: on some benchmarks, unseen ma- nipulations account for up to 10% of all visual operations produced during inference. Importantly, these operations are not arbitrary artifacts but precise and task-effective visual actions that con- tribute directly to problem solving. For example, when asked "Is the bell pepper red or yellow?", the model automatically generates zoomed-in view to better distinguish subtle color differences, closely mirroring human visual inspection without explicit prompting.
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Figure 4: Examples of More Unseen Manipulations
A deeper analysis reveals systematic patterns underlying these behaviors. Statistical evidence shows that specific textual cues reliably trigger corresponding visual manipulations: phrases such as "ex- amine closely" or "focus on" consistently clicit zoom-in operations, while terms like "restore" and "reconstruct" prompt image inpainting. These correlations are both consistent and contextually appropriate, suggesting principled rather than random generation. This capability originates from Bagel's 's large-scale multimodal pretraining, which exposes the model to interleaved visual-text pat- terns encompassing diverse manipulation. ThinkMorph's interleaved reasoning fine-tuning then pro- vides critical alignment by enabling the unified model to activate these manipulation skills within structured reasoning steps for problem solving. In essence, pretraining supplies the raw manipu- lation ability. while interleaved line-tuning directs it toward reasoning-orient visual behaviors. Additional examples and analyses are provided in Appendix C.2
EMERGENT PROPERTY 2 Autonomous Mode Switching The model adaptively switches from interleaved to text-only reasoning based on task complexity, despite being trained exclu- sively on interleaved data.
When trained solely on interleaved Chart Refocus and Jigsaw Assembly data, the model still gen- eralizes strongly the out-of-domain MMVP benchmark, achieving 79.6% and 82, 66% respec-

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Accuracy Gains
Keep Interleaved Reasoning
Can you ace stems of bananas in the image? A: Yes B:
102
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upper portioneal the
vidble loed to closely cuamine the parts of the bazenen that would coropenn so thestems... on partion the lursena ..hinks
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+17.70%
$2.202
+7.29%
66.
Switched to Text Reasoning
We cannot see the window on the school bus? Ar Correct Ii Incorrect
OP
40-
of ochool bas with forany rsuble sttlytttestttoeeeeeee the school hox The image prominentty dieplays the side elouly mamire clear vince of distinetive yellow paint and the SCH1D01 aus oest. There are skoo
vddille bectsoml lines and soy rign stachee to the bas but upen corddul inspection there are windows poorded irouge Conrect +fliinko-aseeee Conecte/ansees
SCHOOL BUS
portos
Sinethe quetias
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winlow,
Restitavee Repporing
where initial text
solution critical elements, the model autonomonsly owitches text-onlly
Charra Jgrin Amsenbly
ressoning, The school bos descriptton suffices for solution, while banana stems require vienal exmination, This sat welsstlpptive behoviin invi demonstratos implici recognition of when tectnal information alone suffirient.
Figure 5: Autonomous Mode Switching Based on Task Complexity.
well above the Bagel-7B baseline of 70.33%. A closer look reveals striking phenomenon. Despite being trained exclusively on interleaved traces, the model autonomously switches to text-only reasoning in 5.3% of inference cases (Figure 55. Notably, these switched in- stances reach 81.25% accuracy overall, a 7.29% improvement over the same samples when solved using interleaved reasoning (73.96%), demonstrating that the model can selectively adapt its reason- ing mode for higher effectiveness.
tively (Table 2p.
Mode switching is task-adaptive, not arbitrary. As shown in Figure 5 the model adapts its rea- soning behavior based on visual complexity. For the question "Can you see stems of bananas in the image?", it maintains interleaved reasoning, generating zoomed-in view of the upper region where the stem would appear. The close-up enables clear stem identification, illustrating that con- tinuous visual engagement remains essential when fine-grained details are critical to the solution. In contrast, for "We cannot see the window on the school bus?", the model switches to pure textual reasoning, describing visible features such the yellow paint and lettering, infer the absence of windows. This contrast reflects a form of front-loaded visual engagement: after processing the im- age and question, the model implicitly decides whether text alone can complete the reasoning. When the initial visual encoding captures information that text can express, it shifts to text-only reasoning for efficiency; when -ne-ggaieed cues remain unresolved, interleaved reasoning continues.
This autonomous adaptation shows that interleaved training not only improves multimodal coor- dination but also enables models to dynamically allocate reasoning effort based on task demands, implicitly recognizing when each modality is essential versus supplementary. Notably, switching also yields efficiency gains: when forced to continue with interleaved reasoning. switched samples produce the same answers but consume approximately 75% more tokens (e.g., 156 vs. 89 tokens), confirming that the model avoids redundant visual computation when initial perception suffices. The result is enhanced efficiency, robustness, and fiexibility across diverse task types. Further examples and analysis are provided in Appendix
EMERGENT PROPERTY 3: Better Test-Time Scaling via Diversified Thoughts Interleaved reasoning enables superior test-time scaling by generating diversified thoughts that explore broader multimodal solution spaces, delivering stable accuracy gains that consistently outper- form unimodal approaches.
Having established the effectiveness of interleaved reasoning, we next examine a more nuanced question: how do different reasoning modes scale at test time? We compare interleaved and uni- modal reasoning under Best-of-N sampling across four benchmarks representing a continuum of distribution shifts (Table 4. Figure 6). VSP serves as the in-domain reference. VStar shares the same task setup as VCoT but stress tests on a smaller scale of target objects. MMVP represents moderate shift toward general perception, containing open-ended question types similar those in VCoT data. Finally, BLINK-J presents the most substantial deviation, with a task setup distinct from Jigsaw Assembly that demands stronger compositional and multimodal adaptation.
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VSP (In-Domain)
VStar (Oub-af-Domenll
BLINK]] (Out Nuut-af-Domalnn
MMVP Domaln)
11.0
20.00
Accuracy
37.5
10.7
12.5
25.0
525
70.2
V-3
Number of Samples (N)
Number of Samples (N)
Number of Sumples (N)
Number of Samples (N)
Ressoring Mode
Teat Reaeoring
Vaust Reassring
Thinkoorsh (ymereaued)
Figure 6: Test-Time Scaling Across Reasoning Modes, Interleaved reasoning demonstrates robust scaling advantages, particularly challenging benchmarks where unimodal approaches plateau decline.
Interleaved reasoning scales more effectively, with gains amplifying under distribution shifts. Across all benchmarks, interleaved reasoning maintains consistent improvements: +5.2% VSP, +1.0% on VStar, +0.7% on MMVP, and a substantial +8. -8.0% on BLINK-J. This peak occurs under the most demanding generalization conditions: to 73.33%, while visual reasoning drops by 2.0% and text reasoning rises only 2.67%. The 10-point gap between interleaved and visual modes highlights that multimodal exploration becomes most critical when single modalities cannot generalize effectively.
BLINK-J, ThinkMorph improves from 65.33%
The scaling advantage arises from richer trajectory diversity in multimodal solution spaces. As illustrated in Floure][lower panel), unimodal reasoning chains are confined to single represen- tational spaces, whereas interleaved reasoning explores both modalities simultaneously, spanning broader search space. This multimodal exploration produces diverse reasoning trajectories that suc- ceed on complementary subsets of problems. Under Best-of-N sampling, such diversity becomes crucial: as N increases, independently sampled chains cover more regions the solution space, greatly improving the likelihood that at least one trajectory reaches the correct answer. These results indicate that interleaved reasoning's s benefit extends beyond single-inference performance to test- time scaling, where trajectory diversity plays central role in discovering higher-quality solutions.
5 GENERALIZATION OF INTERLEAVED REASONING
To extend interleaved reasoning gains broader vision-centric tasks, fine-tune ThinkMorph 24K high-quality interleaved thought samples drawn from all four training tasks and evaluate across diverse benchmarks.
5.1 RESULTS
Baselines We evaluate ten leading models to establish a strong baseline, including seven Vision- Language Models (VLMs) and three unified multimodal models (UMMs). The VLMs tested in- clude open-source models VL3e5 (8B and 38B) (Wang et al.] (002b) and Qwen2.5VL. (7B and 72B) Bai et al. 2025), well as proprictary models GPT-4o, GPT-5, and Gemini 2.5 Flash.
Analysis As shown in Table 3 two advantages stand out.
(1) ThinkMorph delivers large and consistent gains over unified baselines. Compared to its base model. Bagel-7B, ThinkMorph achieves significant improvements across all benchmarks, with an average gain of 20.74% over nine diverse tasks. For instance, on BLINK, ThinkMorph improves by 12.42%, demonstrating robust interleaved reasoning that generalizes to unfamiliar task configu- rations. Other unified basclines, such lnnus-Pro-7B and Chamelcon-7B c notably worse (e.g.. 38.22% and 28.27% on VStar, and near-zero on SAT), whereas ThinkMorph surpasses them by margins ranging from 28.8% to 42.7%. These results indicate that interleaved training not only strengthens multimodal coordination but also enables generation and understanding to reinforce each other, yielding far more capable and generalizable unified models.
(2) ThinkMorph rivals or exceeds large-scale VLMs, particularly on reasoning-intensivee tasks. Despite being fine-tuned only 24K samples, MorphMorph achieves performance comparable to,
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Published as a conference paper at ICLR 2026
Size
VSP
VisPuzzle ChartQa VStar" BLINK-J" MMYP SAT* BLINK" * CV-Bench
Visual Understanding-only VLM
GPT-4o
33.50
43.75
76.34
61.78
72.67
84.67
28.00
60.28
75.61
GPT-5
57.33
78.00
80.85
71.73
77.33
86.33
73.30
69.86
85.46
Gemini 2.5 Flash
59.33
47.00
83.79
70.68
66.00
80.33
56.00
67.49
85,07
8B
8.17
34.75
76.26
68.59
71.33
76.33
45.33
59.60
81.99
Intern VL3.5
38B
20.16
36.50
80.44
76.96
80.67
80.33
49.33
62.65
85,96
7B
2.16
34.75
78.12
76.44
59.33
77.33
51.33
55.92
75.20
Qwen2.5-VL
41.83
40.00
82.03
85.86
61.33
82.00
64.67
61.91
82.54
Unified Models
Janus-pro
00.00
33.50
43.08
38.22
50.67
63.33
22.00
38.51
67.83
Chameleon
7B
00.83
30.50
5.74
28.27
00.67
47.67
10.67
16.52
36.52
Bagel
00.83*
35.00*
61,82
55.49
67.33
70.33
44.67
47.66
76.03*
ThinkkMrrph
7B
75.83
79.00
78.10
67.02
72.00
80.33
52.67
60.07
80.82
(vs Bagel)
+75.00
+44.00
+16.28
+11.53
+4.67
+10.00
+8.00
+12.41
+4.79
Table 3: Comparison of ThinkMorph with Other Models. Bagel-7B B tested under think mode ( no-think mode for tasks where thinking prevents Bagel from generating answers). *: out-of-domain benchmarks.
and several cases exceeding, models an order of magnitude larger. outperforms Qwen2.5- VL-72B by 34% on VSP and 10.67% on BLINK-J, and surpasses Intern VL3.5-388 on SAT while maintaining similar 3D spatial reasoning on CV-Bench. Against proprictary systems, ThinkMorph remains highly competitive, excelling especially on reasoning-heavy evaluations: it outperforms GPT-4o by 24.67% on SAT (52.67% vs. 28.00%) and matches Gemini 2.5 Flash on general percep- tion in MMVP (80.33%) Further qualitative examples provided in Appendix C.1
5.2 ADDITIONAL ANALYSIS ON EMERGENT PROPERTIES
Beyond the main results, we further examine how the emergent properties identified Section 4 gen- eralize to out-of-domail benchmarks. The first two properties remain robust: unseen visual ma- nipulations continue to emerge, and autonomous
Benchmark
CVBench VStar
Relative Improvement over N=1 (%)
BLINK]
mode switching adapts to task complexity (see Ap-
pendix B.2 for detailed analysis of mode switching under test-time scaling).
12.39%
+1.59%
For test-time scaling (PROPERTY 3),
+0.43%
+0.96%
+0.00%
lyze ThinkMorph's trends under Best-of-N sam- pling across diverse benchmarks (Figure 7 Two distinct scaling patterns emerge. For reasoning- intensive tasks, performance improves monotoni- cally with larger N: VStar shows the strongest gain of +5.89%
1..65%
+2.08%
+2.91%
(2.91%%
8. and CV-Bench follows similar trend with a +2.39% increase. In contrast, perception-focusee benchmarks exhibit U-shaped scaling: MMVP and BLINK-J initially decline at in- termediate sampling levels, as BLINK-J drops 2.91% from N with modest gains of +1 1.22% and +0.96%, respectively. These patterns indicate that the ben- efits of test-time scaling depend on task characteristics: reasoning-oricee oriented benchmarks gain stcadily from expanded multimodal exploration, whereas perception-hese tasks require larger sample sizes to escape local optima and fully realize the benefits diversified reasoning trajectories.
(Baseline)
Number of Samples
Figure 7: Relative Improvement
to N = 4, before recovering at

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Published as a conference paper at ICLR 2026
6 RELATED WORK
Multimodal Chain-of-Thoughe Explicit multimodal Ofain-of-Thhoueee (CoT) approaches can be broadly divided into two lines. The first adopts tool-augmented design (OpenAl| Zheng et al.) 2025 Su et al. 2025; Zhou et al. 2025 2024 Gao et al. 2025). in which interleaving remains indirect and fragile. The second line builds on unified models. Within this category, one direction emphasizes enhanced image generation guided by textual CoT (Chern et al. 2025; Qin et al. [025] Huang et al. 2025), while another explores preliminary forms of interleaving. However, these at- tempts remain shallow. MetaMorph (Togg et al. 2024b) introduces visual thinking data but collapses into fixed textual outputs during pretraining. Zebra- CoT (Li al., (025a) creates large-scale inter- leaved dataset without effectively exploring its quality and generalization There also exists implicit multimodal CoT research, which aims to adapt understanding-onff VLMs by introducing intermedi- ate image representations as visual tokens. Such representations include perception tokens (Bigverdi) al., 2025 Yu al.] 2025) and latent visual tokens (Yang al., 2225), which provide additional visual cues for text-based reasoning without explicit interleaving. In summary, prior work highlights the potential of multimodal COT. However, it leaves open the question of when multimodal CoT can extend beyond text-only and visual-only CoT, specifically regarding how to achieve effective and generalizable interleaved reasoning.
Multimodal Understanding and Generation Most existing works on unified multimodal mod- els frequently report that optimizing diffusion-based generative objectives tends to degrade under- standing capabilities (Team] 2024 Wang et al. (202aa) and learned representations, and vice versa, making joint training tragile and brittle. MetaMorph (Tong et al.) 2024b) demonstrated that visual understanding and generation are nevertheless deeply synergistie: during training, increasing data for either capability often benefits both simultaneously. Furthermore, for generative tasks, leverag- ing the model's deep understanding and reasoning abilities further contributes to improved image generation (Pan et al. 2025) Deng et al. 2025 Yan et al.) 2025 Qin et al. 2025). However, when it comes to reasoning tasks, this synergy remains elusive. We introduce ThinkMorph, unified think- ing model designed to enable effective and genuinely interleaved reasoning, where visual genera- tion actively supports and refines textual reasoning. The interleaved training allows unified models to jointly leverage their dual capacities for generation and understanding, with each reinforcing the other to deliver stronger multimodal reasoning performance. As result, we provide generalizable recipe for advancing multimodal reasoning, demonstrating that generative processes can directly enhance understanding under an interleaved Chain-of-Thought framework.
7 CONCLUSION
We introduced ThinkMorph, unified model that unlocks generalizable multimodal reasoning by en- abling text and images to truly reinforce each other. With light interleaved fine-tuning, TorphMorph yields large gains vision-centric benchmarks and even matches or surpasses proprietary systems far larger in scale. More importantly, ThinkMorph reveals surprising capabilities often viewed as hallmarks of intelligence: spontaneous visual manipulation, autonomous mode switching, and di- versified exploration that enhances test-time scaling. These emergent behaviors demonstrate that unified models can develop reasoning skills that go beyond what is explicitly supervised. Looking ahead, unifying and nurturing such interleaved reasoning behaviors throuuh adaptive mode selec- tion, stronger cross-modal alignment objectives, and coherent visual-text thought integration- a compelling path toward more robust and human-like multimodal intelligence.
8 ETHICS STATEMENT
This work addresses the challenge of generating synergistic textual and visual reasoning traces to improve multimodal reasoning skills in unified models. While we do not foresee direct ethical issues, acknowledge the possibility of misuse in generating deceptive misleading reasoning content. Nonetheless, we believe that the overall impact of our work will be positive, as it has tremendous potential in enabling various decision- makiin and problem-solving tasks.
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Published as a conference paper at ICLR 2026
9 REPRODUCIBILITY STATEMENT
We provide details on the curation of training data in Appendix B.6 and D and training details in Appendix B.8 We further submit our source code, based on Bagel implementation, along with our submission. At the time of acceptance, we will open-source all of our training data.
ACKNOWLEDGMENTS
This project was partially supported by the Singapore Ministry of Fund Tier (Award Number: T1 251RES2514).
Education Academic Research
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A
OVERVIEW OF THE APPENDIX
This Appendix is organized as follows:
Section B provides detailed experimental specifications and results, including test-time scaling results (Section [.T), mode switching under test-time scaling (Section [.2]. rea- soning mode definitions (Section B.3), computational cost analysis (Section B.4), analysis how multimodal training enriches text representations (Section [33). question construc- tion and data curation details (Section [.6)). evaluation details (Section B.7) and training
and inference hyperparameters (Section B.8):
Section showcases qualitative case studies, including interleaved reasoning cases (Sec- emergent manipulations (Section C.2 and mode switching examples (See-
tion C.
tion
Section provides all prompts used to generate finetuning data.
B EXPERIMENT DETAILS
B.I TEST-TIME SCALING RESULTS
N
2
N
VSP
Text Reasoning
48.67
48.33
51.33
56.83
Visual Reasoning
83.83
83.83
88.50
91.33
ThinkMorph-Sppttall Navigation
87.17
87.33
90.67
92.33
VStar
Text Reasoning
61.26
60.73
63.87
63.35
Visual Reasoning
56.02
56.54
58.64
61.26
O ThinkMorph-Vistie Visual Search
65.97
67.02
67.54
67.02
BLINK-J
Text Reasoning
65.33
64.67
67.33
68.00
Visual Reasoning
51.33
51.33
52.00
49.33
ThinkkMorph-iigaaw Assembly
65.33
64.00
70.00
73.33
MMVP*
Text Reasoning Visual Reasoning
74.67
75.33
78.67
80.33
74.33
73.00
74.00
75.00
ThinkMorrhh-hhar Refocus
81.33
78.67
82.00
82.00
Table 4: Time Scaling Across Reasoning Modes. Interleaved reasoning demonstrates robust scaling
advantages.
N =
N
N
MMVP 81.67
80.00
80.33
82.67
VStar
62.30
62.30
64.40
65.97
BLINK-J
68.67
66.67
66.67
69.33
CVBench
81.31
81.65
82.60
83,25
Table 5: ThinkMorph Results under Test-Time Scaling.
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Number of Purely Textual Reasoning
Traces out of 8 Generated Rexponser
MMVP
BLINK-Jigsaw
49
14
10
50
100
150
200
0
25
75
100
125
150
20
40
60
80
100
120
140
Count of Questions
Count of Questions
Count of Questions
Figure Despite being trained interleaved reasoning traces. ThinkMorph sometimes adopts purely textual reasoning strategics on out-of-domain benchmarks.
B.2 MODE SWITCHING UNDER TEST- SCALING
PROPERTY @ shows that the model can autonomously select between reasoning modes. To study this behavior in greater depth, we train a dedicated model using balanced dataset of ~24K exam- ples spanning all four tasks, ensuring that the training data cover the three reasoning modes. Based on the results in Table 2
we use visual reasoning data for Spatial Navigation and text-only reason- ing data for Charl Refocus, both perform comparably to interleaved reasoning their respective tasks. For the remaining two tasks, we adopt interleaved reasoning data, producing hybrid model that enables analysis of how multi-mode exposure influences the emergence and dynamics of mode switching under test-time scaling.
We evaluate the hybrid model on three out-of-domain benchmarks: MMVP, VStar, and BLINK-J. For each benchmark, we apply test-time scaling by sampling eight responses per question. Figure summarizes the resulting reasoning mode distribution, grouping questions by the number of purely textual responses. Overall, 6.38%, 8.64%, and 1.25% of responses are purely textual on MMVP, VStar, and BLINK-J, respectively. Interestingly, performance tends improve when the model lects to reason purely in text. On questions where ThinkMorph produces both textual and interleaved responses, textual reasoning achieves 9.75% and 1.84% higher accuracy than interleaved reasoning on MMVP and VStar, respectively, but 2.98% lower accuracy on BLINK-J. These findings suggest that mode diversity amplifies the benefits of test-time scaling: when models can flexibly switch between reasoning modes, they not only explore multiple reasoning trajectories but also alternate between modality strategies, unlocking potential for more effective and efficient scaling future multimodal systems.
B.3 REASONING MODES
ThinkMorph evaluates three reasoning modes, each producing different type of thought chain. The key distinction lies in the composition of intermediate reasoning tokens:
Text Reasoning produces text-only thought chains. The model generates only textual tokens during reasoning, with no intermediate image generation:
<think>Texx: thoogghss/tttat--kasssssxxxx/x/
Visual Reasoning produces image-only thought chains. The model generates only image tokens as intermediate reasoning, followed by the final answer. No text thoughts appear between images:
Linage_start>>imag thought<image_enn
<answer>xx</anawe
Interleaved Reasoning produces interleaved thought chains that alternate between text and image thoughts. This allows text and vision to inform each other throughout the reasoning process:
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<think>Text thought</think. mmmage_start>imaee thought<image_ens _nd> <think>Text thought</think> <answer>xx</answe>
Data construction. Both visual reasoning and interleaved reasoning use the same images for their visual thoughts. The critical difference is whether text thoughts included: interleaved reasoning adds textual reasoning that complements rather than duplicates the visual content, enabling the two modalities to work synergistically. Table 8 details the training hyperparameters for each mode.
B.4 COMPUTATIONAL. COST ANALYSIS
Mode
N=1
N=2
N=4
N=8
Text
354
697
1,424
2,879
Visual
473
945
1,890
3,778
Interlcaved
1,073
2,145
4,269
8,517
Table 6: Average token cost per question BLINK-J across reasoning modes and sampling budgets.
Interleaved reasoning is computationally more expensive than unimodal alternatives due to image generation. We measure average token cost during test-time scaling on BLINK-J (Table 6). Inter- leaved reasoning costs approximately 3x more than text at the same N.
Interleaved reasoning offers better performance-cost efficiency. Despite higher cost, interleaved reasoning achieves superior performance-cos trade-offs on challenging iision-centric tasks. On BLINK-J, interleaved ==4 (70.0%) outperforms text N=8 (68.0%) while using fewer total tokens (4,269 vs. 2,879 x per sample). On MMVP, interleaved N=1 (81.33%) already surpasses text N-8 (80.33%), achieving higher accuracy substantially lower cost.
More text data cannot close the gap. To further test whether text-only reasoning match in- terleaved given equivalent compute, we trained text-only models with 4x more samples to approx- imate the computational budget of interleaved training. The results show improvement (e.g., VStar: 55.50% vs. interleaved 63.87%; VSP: 47.17% vs. interleaved 86.67%) confirming that the advantage stems from the multimodal search space rather than additional compute.
B.5 MULTIMODAL TRAINING ENRICHES TEXT REPRESENTATIONS
Benchmark
Mode
Entropy
Unique Vocab t
Interleaved
7.665
3,186 (+11.4%)
VStar
Text-only
7.499
2,860
Interleaved
7.615
3,795 (+24.1%%
Jigsaw
Text-only
7.353
3,057
Table 7:
Text entropy and vocabulary diversity across reasoning modes. Interleaved reasoning produces richer and more diverse textual reasoning compared text-only.
We analyze textual outputs from correct predictions to understand how interleaved training affects the model's text generation. Table compares text entropy and vocabulary diversity between inter- leaved and text-only reasoning modes.
Higher entropy and richer vocabulary. Interleaved text shows consistently higher entropy and richer vocabulary across both benchmarks. On VStar, unique vocabulary increases by 11.4% 18,186 2,860), and on Jigsaw by 24.1 (3,795 vs. 3,057).
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Qualitative vocabulary differences. Interleaved reasoning produces more visual-relational terms (e.g., "bounding." " "box," "lower"), while text-only reasoning relies on generic tokens (c.g., "an- swer," "image"). This indicates that multimodal training enriches the text representation space itself, enabling the model to produce more expressive and visually grounded textual reasoning even when it autonomously switches to text-only mode (PROPERTY
B.6 DETAILS ON QUESTION CONSTRUCTION AND FINETUNING DATA CURATION
Jigsaw Assembly We construct scalable pipeline that converts images into multiple-choice jigsaw puzzles with two to four pieces across grid configurations (Ix2, 2x1, 1x3, 3x1, and 2x2), presenting multiple arrangement options as answers. Two-piece jigsaw puzzles offer two arrangement options, while three- and four piece puzzles provide four sampled arrangement options including the correct configuration. We source 6,000 images from three datasets 3,300 from SAT (Ray et al. 2024), 1,900 from ADE20K (Zhou et al. 2017), and 800 from Omni3D (Brazil et al. 2023) spanning synthetic spatial scenes, world environments, and 3D perspectives. This yields 6,000 questions distributed evenly across the five layout configurations. To construct finetuning data, we first prompt GPT-4.1 with the original question and ground truth answer, requesting it to describe the visual content of each piece and reason about the correct assembly without revealing in its response that was provided the answer!| For three- and four-piece puzzles, we find that textual descriptions of individual pieces are particularly helpful for guiding arrangement decisions, as they eliminate many implausible configurations. We then provide the original natural image and prompt the model to verify the proposed arrangement by analyzing factors such as object continuity, lighting consistency, and perspective alignment.
Visual Search We begin by collecting 144k visual search problems from GQA (Hudson & Man- ning 2019). VSR (Liu et al. 2023). and Open Images (Kuznetsova et al. 2020 To ensure problems are challenging while keeping target objects discernible, we filter for images whose target object's bounding box occupies I 1%-30% of the total image size. After manually reviewing the problems, we observe that many problems suffer from ambiguous phrasing, incorrect answers, or misplaced bounding boxes. We distill these error patterns into prompt and develop filtering pipeline using Gemini 2.5 Pro and GPT-5 to remove questions deemed inappropriate by either model. This pipeline yields 6.990 visual search problems in total. To construct interleaved reasoning, we prompt GPT-4.1 to parse the query to identify where to place the bounding box. This is akin to how humans first map the textual query to localize the area of interest. We also provide the image with the target object highlighted and prompt the model to name the target object.
Spatial Navigation We create pipeline that generates Frozen Lake navigation problems using OpenAl Gym (Brockman et al. 2016). These problems range from 3x3 to 6x6 grid sizes, with 1,500 problems generated for each size. To visualize intermediate reasoning steps, our pipeline depicts potential paths with red lines and arrows. Similar to how humans first scan the maze to identify the starting position, goal position, and hole positions, we prompt GPT-4.1 I to first describe the maze layout. Then, we pass in the maze image overlaid with a correct path found via A search. Finally, we prompt the model verify the path in the image and articulate the moves.
Chart Refocus We collect chart question answering problems on horizontal and vertical bar charts originally from ChartQA (Masry et al. 2022). which are subsequently processed by Fuu et al. (2025) to highlight or draw bounding boxes around areas relevant to answering the questions. To ensure that not too much of the chart is emphasized, we filter for questions whose solutions require only one highlighting drawing operation. After manually reviewing the remaining 8.4k questions, we find that a small portion contain errors in answers or highlighting, so we filter these using GPT-5. This leaves with 8. 1k questions, from which we sample 6,000 to achieve balanced distribution as possible across highlighting and drawing operations. Similar to the visual search task, we structure our prompts that we first ask the model to identify a region of interest, then pass in the processed image with the region called attention to, and finally request the model to provide the answer given the scaffolding.
'To ensure the generated reasoning leads to the correct answer, we provide the ground truth to the model while instructing it not to reveal this information in its reasoning trace. We follow this same process for subsequent tasks but omit these details for brevity.
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B.7 EVALUATION DETAILS
For answer prompting, we use the official prompts for all tasks except VSP-main, where we adopt the official prompt used in VSP for baseline models but apply our custom prompt for our trained model, provided below.
VSP Custom Prompt
You are a maze solver. Your goal is to guide a player from the start to the goal on a grid map while avoiding holes. The player (L), right (R), up (U), or down (D). The frozen lake is not slippery; the player will always move the intended direction. Moving off the edge falling into hole results in failure. Reaching the goal means success. Provide your solution as sequence of moves wrapped in (boxed(], such bboxed (L..edL. The moves should be comma-separated:
square at time in the directions left
For answer judging. we follow either the official judging pipelines or the standard VLMEvalkit pipeline for Vstar, VSP-main, BLINK-J, BLINK, VisPuzzle, MMVP, SAT and CV-Bench to ensure consistency and reproducibility, all executed within the VLMEvalkit framework. SAT is evaluated under its standard circular setting.
For ChartQA, we first perform answer extraction with GPT-5 as an ALM-as-a-Judge using our cus- tom prompt and then accurately match the extracted answer with the ground truth, following the official pipeline.
ChartQA Answer Extraction Prompt
You are an "Answer Extraction Assistant." You are given a question and model's response. The response contains the final answer the question.
Role:
Extract only the final answer from the response and output it. Do not include any extra words, punctuation, or units. If the final answer does not appear the response, output: None.
Task:
Rules: Output only the answer itself no explanations, labels, extra text. 2. the answer is numeric, remove units and extra symbols (e.g., %, currency); keep the minus sign and decimal point.
Examples: [example1] Question: What is the difference in value between mutton and corn?
Model's response: subtract the value of corn from the value of mutton: 103.7 103.13 0.57. Therefore, the difference in value between mutton and corn is 0.57. Your output: 0.57 [example2] Question: the average of all bars in 55 to 64 age group greater than average of 25 to 64 age group? Model's 's response: No Your output: No [example3] Question: How much does the value of Approve decrease from Jul 2015 to Sep 2015? Model's response: the value of "pprovve" decreased by 12 percentage points from July 2015 to September 2015. Your output: 12
Question:
Model's ' response:
Your output:
B.8 TRAINING AND INFERENCE DETAILS
We train Bagel- on curated interleaved traces as unified autoregressive streams using two nodes with 16x 100 80GB GPUs. In our training setup, we modify the official Bagel codebase to support both training and inference, with hyperparameters varying across different experimental settings, see Table 8 Except for the parameters described in the table, all other parameters the default settings.
Additionally, since the original Bagel does not natively support generating interleaved outputs, introduce two special tokens, <image_stan seragesstar>>>> and lamage_ends: to enable autonomous modal- ity switching. When the model outputs < <image.start>, it triggers the image generation process. Furthermore, we wrap the text reasoning traces with <think> and < /hink>> and the final answer with <answer> and </ </answer>.
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For inference, single-pass der test-time compute scaling we set the temperature to 0.7 while keeping max max_tokens number un- changed.
temperature=0 with max.tokens=4096, whereas
Table 8: Hyperparameters used different training setting. "N/A" indicates that the parameter was not applicable to that stage.
Text Reasoning
Visual Reasoning
Interleaved Reasoning
Hyperparameter
ThinkMorph
Optimizer & Scheduler
Learning Rate (LR)
x 10-5 Cosine Decay 3,000
1 10-5 Cosine Decay 3,000
x 10-5 Cosine Decay 3,000
1 10-B Cosine Decay 8,000
LR Scheduler
Total Training Steps
Model & Loss
Loss Weight
(Implicit) N/A Generation Expert
1.0
MSE Loss Weight
1.0
Frozen Components
None
None
None
Batching & Tokenization
Max Tokens per Batch
10240
32768
32768
32768
Regularization (Dropout)
Text Condition Dropout
N/A
0.1
VIT Condition Dropout
N/A
0.3
VAE Condition Dropout
N/A
0.3
0.3
0.3
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C CASE STUDY
C.I INTERLEAVED REASONING CASES
BLINK_Jigsaw
Queetion Images
Question
srttoeeer 19ss to not mcon unial ancaentied ses erei the poolle 14 the Vlage and you juc1 wed to glve your accnovies IMA ganilee You son't near sdentity people, just noct antlyse bocee the irformttio gove you.
tiver the rimet laage with the town right canner st sering, vis 1el1s afiice ohe of Shoud ge or taage the ntustng sort! Drages.
Ground Truth Anewer
ThinkMorph Reasoning
Output images
Ressoring
<ostrio-o deterains which thape fite mosting right camer of first image, E'll extops the cas preaee asaliatle the lover right camrer masinn. The left and eders the persee ahouler and the elge the bakic with rtcrption Hoxt. wven baskiren
soutee the suit seee original leagn directies the thah seaees are contlateet Waen
stopteys louer
ther smage angln any sait's ortornation plannare
troge best nanches sulll continnt by the sate mime hard the taste, and the logs bot ground oll atlan returdlty. The seoedd teage.
tntorrenn the with coritully atser wealt verity conenerr
eectuding thond
procautly bland leee ahe carter.
rimst abeanse
Figin's a sich The slane's craitioning. stotoe, and the ample hand all notor the upper boo' orlenaatinn coerting natural
cee cie were the slean ahra previsully onaar the lines callan san lage sus si distors, mutetaliie the lnsegl visuiily cestral ciric,, mase frrm original analyete. Sunnall ascentid image sisunity onall was with sicect The eaver lsc (s.2 the vhrrd Lmage ont
filly vistible han''s aoveert
Figure 9: A sample correct case in BLINK Jigsaw
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Biisaw
Question Images
Quastion
Disclatmer: This is nots unfair sssumpeiion assut the people in the Lmage and you just need give your desessment this quisttion. You doete nead identiit reel
conter onelyre hesed inforaction
Given
second thiid cererully
fot
Ground Truth Answer
ThinkMorph Reasoning
Prediction
Output Images
Reasoning Process
<thisloto determine which image fitss the wissing lower right corner of the First Lmage, begin by close examining the visib e edges and visust tues the orig notl phoca The rirst tmuge stows group people dressed in suits, with clear verticol tine tomed by the tapets and stoulders of the suits.
"iant side, chere nocicoable pap where person dank suis with notches topet and o lighz blue the stould consinue the visual flon. Next, cospare thts the second Laage. The second Usgg feetures tork sait with a nouched lopel, rightreat
Light blue tie, and
then ascond perfectly continus verticol. line pockett details fron the first image's right the
match the besture and color
suit
of the first
and
oligenent suit
der
first The plnstripe with difrenent and which
third would the concinutty sutts olresdy dark inage
losel.
first ty the citeiiiiiittt
tmage
tmages
right
sesstessly
Exto adjazons Figure's poststoning ontionationn
vistate disnattion rest Umage. and ent staittmm same shude and sentwrn, further suportting visuni integricy the conpoxition iiin.. Next, consider coerall grous dynanic. spactng nu and ungt es the ders, will continu nuttion or the bockpround elveents csuch the remoin consistent across the antire tmage. There are no abrupt changes or ri riserrcceed edges, and the rigures oppear nsturally diigred If ttey were never Literrusted. This conftims that the cholce of the second trage for the missiin sepnert was aporoprftte, es all visuol caes are priseevvd and the image tooks fully contrest. therefore, she aroaer is: A. the second irage..c/tiin thnnko-aaeeeeeeeeee
color. pockes zuts socitio aliigs
up serfectly
anartrene).
Figure 10: A sample correct case in BLINK Jigsaw
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Jigsaw
Question
B
Queetion
boctos stmple origtnd)
decommine arnangmennt
pevercellls by losstad parts to reconetract the naturdl iveee.
Ground Truth
ThinkMorph Reasoning
Correct
Output Images
Reassnnin Process
plaoo partle Port (tar): This feoturee ths tegeitee nemsect
table the anjects cheirs, and bell visible floor. tackground dark will and some furattune, including o steliinng unit at the for and the rocm. lighting and flour and cherly visible, with the waod grote runniigg accass place, Pars orlddia
such
phane to the left, there mnother
weneow, eniiiing unti, a plant. upsrr part Satatly, rennly waddes floor, with the love part the white talle visible the right the coler lighting of the flter mites the other poscas, and the toste tag otign with the cade seen io the other sugainas To recoratraes the crigioal Lmage, look for continutty in caserbied trage.
exgnent thoss
placed
peropecative
abjeccs vinuct cass, stwluting
viedew axperr
roon,
snaild
foregraued
suggest indiriiing this seci cnull windon abulvinn sndl), the
clissec
stant froned
udovs bill and chiirs, Amongingg parta beaed
steada be fort
modll part shoald be ftart Canoning the midile sectios of the roon and the bell), and the botter port ahuuld be hort (slowing the floor places, steluirg
top, leg): arrespement
then cfjecte
wiite table clign encty wwwpppinap..eeeeeiiiii
raturalwwss
buuseriis the nost. the soad floor posturn flavs smootalyy froe the foregrount to the bockemouns, with to coript cntits to cotar grein directton, cortiming that the floer places are correctly attigned best, chece the atigrame dejtcts. unite piece, then
composiite. rirat, comtin the visui conturesty
in forepraant (bctton plane) seatlasiy
upvrrd the
objects
tor left
vistble pauccees bell Loocing further tno the
posttiond
and
inttenting spatial
arstwing antt sindow purfecsly attuated
bosk, in sspestec for the too auctionn The ptant and franed picture on the bets table sloge will wich the has of the roon's peracestive. The concisrent thraspouity with roffectiin the aurchinn the property ruclistic salianooesteeerre////aaaaaa
intensity other hight Suenall.
supperting
Figure 11: A sample correct case in VisPuzzle
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VStarBench
Question Images
Question
What is the color of the bottle cap?
A: blue
B: white
C: red
D: orange
Ground Truth Answer
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoning Process
reserble bottle; typteally located hots shelve kind shap, Once idetifiie fied, took closely the the bottle detersine the colored the nut Highter bariground postrioned
<thinioTo find bottle original
encity visible. Plaring rad hounting
bettle hightight detatles Therefore,
middl section surstton w/ths//ttttttteae [Ousput_ kinage_ands
ethinioWith the bettle
behind the
bottle
vistible
Figure 12: A sample correct case in Vstar
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VSP
Question Images
You are mure solver. Your geel to guide player from the stort to the gool grid nap while avciding holms, the player cas move square at zime in the diructions lefe (1), right (0), up (R), on dom (1). the frozen Laks is not ilippery; the player will always nove in the inteaded eirectiion. Hevinng off the adge fillting into a hole meults ir its Turn. Reaching the gool maons euecess. Provide your soluttion as sequece of esves vrosped in (baced(()), such os Shacedf, R,U,0). The moves chauld be comm-separanac.
Ground Truth Answer
answer inforeation
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoning Process
<tHinisTo get clear sanse of the naze layaut, I'll first Edorttfy the key postitions The player starts at the bottor-left corner of the grid, whtch is the Lst ron, colum Countiing fron oas left as row 1, colum 1). The gost, nanced by present, 15 in the 6th ros, colum Inrre ane several ioe holes soottared arcund: the hole is in row 2, column Csecard row, rirst calumn from the lott). kntter role is in ror colum unn cnlumn and arather in column 4. All otter squores are sate tropen surfaces. with the stort, gual, and ho as locutes, it's time to pist ast the solution suth, </thtrib-ttmppe Startt-Duttvt. Insgesscinpee. wttpe//ssteeeeeeettttttteee www.-ttthhoooooko........ at solutten puth, ste the stover storts at the ootton- left noves right rour cires, passing sarely acress the rrazen surfice without touching holes. Ihen the avelding the note betcn, and "tinlly moves right one lost. time reach goal square with the present. Both nove scays withth the grid osundories and never touthes 1oe notes. the visual pain occurately quides the player from start cool, avcidding all hezords. The M...s t//. intorainoo Serneed K.K.K.KUU 00//mmmrr
Thoe are two holes side by side in row 4, columes 5 and a.
roe b. colume b, three' asother mile. now has a hele
square,
cornect
Figure 13: A sample correct in VSP
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C.2 EMERGENT MANIPULATIONS
Question Images
Quaation
picture shirt 1-geurs!
Truth Answer
ThinkMorph Reasoning
Prediction
Output Images
Process
ststrb-Te ditumaine shather mila picture waaring abtrt Teatirt, read classly cherrve the upper exanriing his clathing, ratice this bis panner hot collar aapeare to lorg clawedd auttanmpp styls, unice charectertuttic
Pranirts typicaity ahtre. To arphatee the srever, sons to hightignt the area of the tmage where the atire lo visible, halping picture
crtlars
muslly
fearte,
Trakkir, clanely munning
adited
tratior helos highiighhing the spsciftic ane his clothrigg The mole nearing lorg allevel gameet with differen fibric. offering claser fii. hight shore, thererane, besed these osser wat ane. the connect onseer saor arttent oeserndurrrrrer
ore
cluariy stow the stract coltoned design typtasl
Figure 14: A sample correct case with zoom-in
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Question images
Con you the coudall Fin of the shark the Lmage?
Yes
Ground Truth Answer
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoning Process
<thtnioTo detemnine whesher the coudel fin of the shork is vistate the (eage, need to focus on the location and orientation of the shark's firs. The coudsl fin, or toil Fin, is the largest and most posterior fin the shark, located at the end of its body. In the proviced inage, the shark is shown seiminng with its body reatly in profile, and the focus is on the dorsal (top) side of the shark, The dorsol fin is clearly visible, but the caudal fin is not present in the frone. Since the caudol rin is Located at the very back of the stark and is vistale in this particulon vtes, the anover to the query is No. </thinlo-diueee starts Irages]cinaee enbeethinloTo detereine sbether the coucal fin of the shark is visible the saage, closely asaei the firs present the from. The cousal fin the largest, postertor (coil) fin shark. the provided (dorsal)) in answer o/tntto corovenss//snsen
proftlee and shom coerespording the
the tall image,
body. dorsol. sowards the
visible from, and
Figure 15: A sample correct case with zoom-in
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MMVP
Question Images
160
Question
The lmage copoures a moman runnling Fron:
Answer
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoring
profile claarly visibbl, miks
incllutin autlire, posiitio of
allimment her
rura, Since
vicepolet, shouls look for sry distinguishing festures thit wolld poins either the fromt side. The nest direct indiccttr is the way the entire figure is presented: it vere bock view, the roman's book souls be focing the carero, and the setotts side prersle, with face and the front of her body would be less vistblle sevever, in this teage, the focus exphssize
clear that correct crnser clerry snows the profile, esternine shether footion directly nutring from wewestaweeenggg... Toutuu To / redoethinkkto coptures
lng eovemente captured from side. hight
Side
this
side viss, foom the visiale sossure and the osgle the stotograph. In the cdited visueitaation, the Highliightd region eeprusizes the profile of the vonan's as body, showing her side stthauetee, the clapmeent of her lags. and the profile of her foce. This clear sephasts the side of her bosy. inclusting the outtiiee of her torso oed the way her tiess are tostitoooed retatiive to each other, ineiccatis
image zide foce,
took bask vinn, focs viswollaation confiree case Therefore, detatled hightighting
opocaits
Figure 16: A sample correct with motion prediction
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Question Images
Question
the betl pryper in the picture red on yollow?
A: Red
B: Yellow
Ground Truth Answer
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoning Process
picture or yellos, first to closely abserve the besket, trage shows a vartety fresh produce, inclusing several bell pespers, rocustng the pepper in question, note
<thini-lc determine whether the pepper the beil pesper
st prest nently displayed esstly vistale arong the other fruits and vegetaslas. var lertyty There are yel bell prypars visible in the lmmedi area that cnuld mistocelly idents fied. Grven this detolled visunl evidecre, the cornect ansver to the query is red. ipprasize the port the smoge that provides the orever, highlight the area drom strectly the relevent produce stee,
color the proper stancs out on vivid rnd, which
indirator
that
where the red bell pesper is located, ensuring that the viewer's ottention This targeted focus helps clartty the ansaer by visuelly by tsslating the key slement the dectston-raking procese.c/thiin cirage_stinn (tarrt/ tha.ge/llingguumsaa untbchthii skoto detersire whether hell pepper the picture question specifies that there are options
yellow,
closely y exantns ne the tmage provi identiif which color the bell pepper ship loys, The edited visueltzatton helps by hightighhiigg the relevant area conteinting the beil pesper, noking easter then have a bright, conststent red color. There are no signs yellow on any other color
and or yellon, so book
displays,
hocus on the correct part
tmage. In the inghttigtedd section, see rultiple
bell peppers, and surfare of the pepoees, grewn knews and the natural Highting further confire ru the coterr as they any anbiguiity. lased on this detai led visual evidence, cosclude that sell pepper the picture in red. Therefore, the anseer LS: wr wowersoeess
introdace
Figure 17: A sample correct with perspective shifting.
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Question images
Question
beverage picture glass?
Vig
Ground
ThinkMorph Reasoning
Prediction Correct
Output Images
Reasoning Process
beveroge
contatred mag glass, closely tmage displays
trage for tope contatner solding
slightly curved top, distinctive feotures glass. Mugs tysicelly delicote appsarance,
sprig
holding beverage transporent, smooth, nore substantial, handle the overall shose
confidently conclude present: By focusing on these visuel deturninw whether the beverage
correct onswer Gless. tottete-tte-deeee... starts Insges]sinope, ando-thinkoooo picture contained tronsparent, smothh and alightly risbed surface with handie watch ore key characteristics more subeuneic), bass tcane apperrance, which beverage is served with ice. leron sitces, and sprig of mint, and the woy it is presented further erphosizes the use glass, as rugs are less comeon for such refreshing drinks. Bosed on these observations, con confidentlyy conclude that the conrect orswer is B, Gloss.-/thire w/thnkk- sarkoo--rsseeet carrsnnn-i/annseee
run glass closely exmine image.
holdiing drink
llss, Mags suillly have
present this tmage.
Figure 18: A sample correct with elimination.
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Question Images
WORLD ECONOMIC FORUM
Question
Disctatwer: sdenctify people. anslyse bosed information
unfaie assurptions people in lmage and you just need give your assessment this quest
Given First lover
tell inege third
qurroprtote
carefullly
Select: from toll lowinng ces.
Ground Truth Answer
ThinkMorph Reasoning
Prediction
Output Images
Reasoning Process
To detormire image the missing Lowaroright continutty teages. First, logn,
imago, closely cumiin teage. stage hockdrop, sutetple disploytng chetes aneengement dtruplly black. The miaxing area should logically costinus the row chaims stage flour. The third image provides direct consimution of the chuirs and pociurs, natching the aligmmant purapeetive. The blue stage bockground and the plocosone of the choies are consistont, supacseing 2962 the third image fits perfectly Ento the aissing section. In controst, the second image desicts loslly carpel and the lower pari of the chates, which does not eatik the upser poriton of the first trage, where the stage and screens more prontnent, It seees to represent a oifferent, lower secttion of the scene, not the direct continuction abocs the chatrs. Therefore, baxed on the allgreens of the crates, the blue stage baokground, and the oversil perspective, the third image the sissing part that best completes the first leage tesps.creriiee cre.ineeeeiiiiii ciecee poarps iropes based on my provious ressniing. the person in block conitinues smoo froe the podiun area inio the row of white chatis. is The diigment of the chotrs and the podtums consistent, with the orarests and legs of the cooes Linting up ncturally against the ner perlecttive stage Fooor. The excrophonts on the podture nenzon property possttoned, and there arvangement the screens above-scme some shoving cityscope, otsera teat pettern continues Logicslly ocress the composits, is, noimaining comiissans height perspoccive. right settiin. flloo's reffecttiv reteforcing sonse orderty. betweer previously seporate trages seooth, integrated.
Like right
sugpesting
continuccion
visible
with the left in the first
podiuss front, deciston, Shee the continutty
chails podtues alige
padiue white chates, while the right
teogex)dimage ond--ttrnttettnn that have mssembled the
will vyriily by the colierance and visuel corit rutitt of the resuliing copposite. Starting from the left,
sbrups breaks or mismatches at the team whee the imapes were potned. blue stage background
uninterrapted,
image, previsusly black, non footures continuation
semwless, appesis natural
disconcinuttte Overall, teage enrrectly
contirming
Figure 19: A sample correct case with inpainting.
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Published as a conference paper at ICLR 2026
C.3 MODE SWITCHING
Question Images
Prediction
Output Images
TОP
No output Images
SCHOOL, BUS
Reasoning Procesa
<chinle-To deterbine
school bus
for visible proninertly diep
school
Question
school hus, with clear the 'SCIEL uis test,
the distinctive yelloe slse visible har contal lines coreful inspecsion, there
attocted windows
Resed the following stsatmeett cornect: We comce the window the school bus?
the bus, but vistbll in the portion of the bus show. Siice the question revelves around the absence of a wtndow and the provided thege does not show any as ainuu at all, the stotement sa oecurate. therefore, the cornect arover is 'a. Correct. "c/thi crreaars. Lorrecte/anseer>
Cornect
Incorrect
Figure 20: A sample correct case with mode switching
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Published as a conference paper at ICLR 2026
Question Images
Prediction
Output Images
6
7
R
Y
U
F
G
H
No output images
V
B
N
Reasoning Process
To deterni whether statement "Tere this
keys keyboard carefully presence obsence
Question
tmage. questton askting
lettor so
on tdentiifying fying if any sey labeled 'g" is visible. Looking et the visible keycaps, see letters such as F. 6, H. B, and N., but do not see key labeled tp" arong then. The statement clates that there is D, and since I finding D the visible portion the bxyboard, with the statement, Therefore, correct cuery
Is the foll loving stoterent cornect:: There is letter this Lmage,
Conrect
image" correct. reed to carefullly examine visible keys on the keyboard the provide picture. The question is spacifiezlly about the presence on obsence of the letter D. Looking at the vistble keyoaps, I see letters such as "D" G, H. V. B and R. but de not see Since vistble portion 'D",
statcment
Ground Truth Answer
labeled tryboard
query regarding statement's accuracy is "A, "a/thino-- nowers.. Currect-o/osseer>
Figure 21: A sample correct case with mode switching
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Published as a conference paper at ICLR 2026
Question images
Prediction Correct
Output Images
No output images
Reasoning
rt deterstne whether the individusls in the ptcture Face the frons the back, need to observe their orientstion relutive the rantly
In the image, the body language suggests beach, holding hands, etrectior, key identify
Question
wolkinng
their way they are facing camero close inspection, all four individuals are post such that their fronts are towards the canera, and their backs ane aaay from the water and the shoreline. iine, Their forial expressions and the way they are holdiing hands also indicate they raving forword, which Therefore, the quary Frent wwt.////toaaseeeeeeeee....
Do the individuals the picture face front or the bock?
A: Front
Bock
foctig
Ground Truth Answer
Figure 22: sample correct case with mode switching
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Published as a conference paper at ICLR 2026
Question Images
Prediction Correct
Output Images
No output images
Reasoning Process
butterfly's focus the where
vistble tnage, part butterfly,
locobed, the grovided trage, the buttent ly is perched with its kings fully spread opon. The wings cover rost of the body, but the absomen can still be seen running down the stddle between the clased wirg sections. The back ound consists of flowe and greanery, which helps highlight butterfly's features. Since abdomen query
Is the butsorfly's is abdonen visible in the lmage?
Ground Truth Answer
Figure 23: A sample correct case with mode switching
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Published as a conference paper at ICLR 2026
Question Images
Prediction Incorrect
Output Images
No output images
Ressoning Process
<thinksTo detarmine whether facing
profile. interacting trage,
Question
crouched down
directed towards the odult, and face visible Iders torso
plane.
Is the child in the picture facing forward or in profila?
are also clearly turred Lowards the ocult as sell. There is na irdication that the child''s profile (xide vien) is beting presented; inotead, the chtid's trontol vtow evident. Therefore, the correct answer the about the chi d'ds orientatioo Maring forword, which A.t//htnio catsennsko/nneeer...
A: Focing forward
B: profile
Ground Truth Answer
Figure 24: sample incorrect case with mode switching
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Published as a conference paper at ICLR 2026
D PROMPTS
This section provides prompts for generating finetuning data for all four tasks.
Visual Search Text Thought Prompt
System Prompt:
You given visual reasoning problem and the answer. Your task is to produce a standalone, problem. Your reader will not have access to the answer like you do. Your explanation will be used as a direct output to users, so it must read naturally and independently.
explanation of how to solve the
Guidelines:
Include specific visual details about objects, their locations, colors, relationships, etc.
Make reasoning concrete and grounded in what is visible in the image
Build up logically from observations to the final answer
Do not reveal hint that you were given the right answer-your reasoning should read as it independently arrived at the right answer
End by stating the answer clearly
User Prompt: Question: {question)
Answer: [answer]
Please analyze the image and provide detailed reasoning for how arrive this answer. Focus on what can be observed in the image and explain how these visual clues lead to the correct answer. Remember that you should hint or mention that you were given the right answer.
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Visual Search Interleaved Thought Prompt
System Prompt: You are given a visual reasoning problem consisting of:
textual question
The original image
set of reasoning steps
A modified version of the image with red bounding box highlighting an item critical to solving the problem The correct answer
Your task is produce standalone, casy-to-understand explanation of how solve the problem. Your reader will not have access to the intermediate materials (e.g.. answer, reasoning steps, or the fact that an image was modified). Your explanation will be used as direct output to users, so it must read naturally and independently.
Your output must follow this structure and be formatted as a JSON object:
"image cot": "Step-by-step reasoning that explains how to determine where the red bounding box should go in the original image. Do not reveal the final answer here. Only focus on how to derive the bounding box. Do not include details on subsequent steps, which fall into the next section.".
"edited "edited_image .anllssss:s "Detailed explanation of how the highlighted region helps solve the question and leads to the correct answer. This is where you reveal the final answer, with enriched and image-grouu grounded reasoning, Only provide the answer in the last sentence."
Guidelines:
Part I: "image_com _co""
Describe how to identify the key item region in the original image that should be highlighted with a red bounding box.
Focus the visual cues or relationships that would guide someone find this item.
Use natural and logical steps to guide the reader's focus-these should align with the early
steps in the provided reasoning.
You must NOT reveal or mention the answer to the question in this part.
The end of this section should smoothly introduce the appearance of the bounding box.
Make sure to include detailed descriptions and locations of items. The reasoning steps likely do not include these, but you should add them.
Part (implicit)
The modified image with the red bounding box will be displayed here. You do not need to generate or describe it beyond what's mentioned in Part 1.
Part 3: "edited_ _imade lillliiaaaaaalleeeee anaalsiis
Now that the key visual element is highlighted, explain how it leads the correct answer.
Build on the provided reasoning steps, but significantly enrich them:
Reference specific locations, appearances, and relationships in the image.
Make the reasoning concrete and visually grounded.
Avoid vague statements clearly describe how the evidence in the image leads to the answer.
Reveal the final answer naturally the end of this explanation.
User Prompt:
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ChartQA Text-Thought Prompt
System Prompt: You are an expert in visual reasoning and chart analysis. Your goal to provide clear, step-by-step thought process to answer given query based on visualization.
User Prompt: You are provided with an image containing a visualization and query about it.
Your task is generate detailed, step-by-step reasoning that leads the correct answer for the query. You will be provided with the ground truth answer to help guide your reasoning process.
is crucial that you do not reveal, hint, or imply that the ground truth answer was provided to you. Your reasoning should read though you are independently analyzing the image and arriving at the conclusion yourself. Your entire response should feel like an inner monologue.
The query is: "[query)"" The answer to this question is: {answer)
Note that the longer your response is, the better. Try to gradually build towards the correct answer. And ensure that the answer you give is the provided answer. You do need to emphasize the answer by wrapping it in
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Published as a conference paper at ICLR 2026
ChartQA Interleaved Thought Prompt
System Prompt: You are an expert in visual reasoning and chart analysis.
First-Round Prompt:
You are provided with two images and query. Both images contain visualization. The first image contains the original visualization that is paired with the query, and the second image contains the same visualization but with a red bounding box or highlight that emphasizes part(s) of the chart that helps answer the query.
Your task is generate step-by-step reasoning for deciding which area(s) in the chart highlight. Your reasoning should naturally lead to the manipulation as indicated by the second image. You will be provided with the ground truth answer the question further help guide you to identify the area(s) of interest. Note that your goal is not to produce the answer your response, but identify the area and the manipulation.
The query is: "[query)"
The answer to this question is: {answer)
Please provide your analysis as a JSON object with the key "image cot" containing your detailed reasoning. It is crucial that you do not reveal, hint, imply that the edited image or the ground truth answer provided to you. Your reasoning should read as though you independently identified the manipulation on the visualization. The introduction of the manipulation should be smooth. Do not say "the manipulation should be..." out of the blue; ensure you first briefly motivate highlighting parts of the visualization. Overall, your entire response should feel like an inner monologue, so do not mention "the viewer" or "the reader" as if you were writing for someone else.
Before we elicit the second-round response, we "sanitize" the conversation history by replacing the first-round prompt above with the original question, that the model is unaware that its response in the first round was guided by the ground truth answer. This replacement makes the second-round response more natural and maintains better coherence across the two rounds reasoning.
Looking at this edited visualization, provide detailed reasoning to arrive at the answer for the original query.
Recond-Rount Prompt:
The answer this question is: answer. Make sure this is the answer you provide at the end. I am providing this to you so that you generate accurate reasoning. Note, however, that you must mention or imply that you are provided with the edited visualization or the answer. Your reasoning should read though you generated the previous image editing reasoning and the edited image yourself, and now you are relying on them to arrive final answer.
Please provide your response as JSON object with the key "final reasoning" containing how you arrive at the answer given the edited visualization.
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Published as a conference paper at ICLR 2026
Jigsaw Puzzle Interleaved Thought Prompts
Jigsaw Puzzle Text-Thought Training Data are First-Round TTT Responses
System Prompt:
You are expert specializing in solving jigsaw puzzles. Your task is to solve jigsaw puzzle. You must present your entire analysis as coherent, multi-turn monologue that reads your responses must never reveal the guidance you receive. Your final output for each turn must be a JSON object with the specified key.
single, independent thought process. You will be guided, but
Fiust-Round Prompt: {question}
The goal is to arrive at the answer providdd answer).
You are given two images: the first shows the separate pieces, and the second shows their correct assembly**, Your task is to construct a line of reasoning that explains how to arrive at the correct assembly, as you were deducing it from separate pieces yourself.
*Crucial Instructions:**
Present your analysis as if you solving the puzzle independently from scratch.
**Do Do not mention or imply that the answer or the assembled image was provided to you.
Your whole response must be JSON object with the key "image_cot".
Describe the visual content each piece in detail.
Provide compelling reasoning for your conclusion.
Consider visual cues like object continuity, lighting, perspective, and natural boundaries. (tw.:rdde...wwwrrrrrrr.eeeeeeeee
Conclude with your reasoning with the answer
Before we elicit the second-round response, we "sanitize" the conversation history by replacing the first-round prompt above with the original question, so that the model is unaware that its response in the first round was guided by the ground truth answer. This replacement makes the second-round verification more natural and maintains better coherence across the two rounds reasoning.
Second-Round Prompt: Your initial analysis appears correct. As next step, you will analyze the resulting assembled image. You should act you are examining the result of your own work to confirm its coherence.
**Crucial Instructions:**
The image provided shows the assembled result based on your reasoning.
Your response must read as if **you** created the visualization for verification, so do not say things like "the provided visualization", which implies it is given you and disrupts the coherence of the whole reasoning process.
Even though this arrangement is visually sound and coherent, your analysis must not reveal that you told your answer correct. Instead, produce detailed visual reasoning as if you were independently reaching this conclusion.
Your response should read as if you initiated this verification, so that reads like seamless continuation of your thought process.
Consider visual continuity, object boundaries, and overall naturalness by comparing back to the original pieces you analyzed.
End by restating the answer in the specified format; do not call the "final answer", just "te answer".
Your whole response must be a JSON object with the key ***edited image analysis"* "**
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Published as a conference paper at ICLR 2026
Frozen Lake Text- Thought Prompt
We adopt two versions of the user prompt when generating training data with GPT-4.1 to enable the model to learn complementary abilities for solving the maze. Each prompt generates half of the training data. The crucial difference between the two versions that one requires the model to first repeat the textual maze map, while the other forbids this step. We posit that the former encourages the trained model to first transcribe the maze and then reason textually based this transcription, while the latter encourages the model to reason more "visually" without needing to transcribe the maze map.
User Prompt Version {question]
Here the precise maze layout and the required final answer to guide your analysis: Maze Text Map: {formatted_mm m
Required Final Answer: (boxee(ccorectt
**Very Important Instructions for Your Reasoning:**
The text map and the answer provided to you that you leverage them to produce accurate reasoning. Your response must be a completely self-contained analysis that reads naturally to user who only the maze image.
*ou should include the text map in your response** ** to ground your explanation. However, you **mus|** first define the symbols (S. G, H. F) in plain language and explicitly go through the process of transcribing the text map.
** not mention or hint that the solution the text map was provided to you. ** Your reasoning should appear to be your own independent work.
Using coordinates aid reasoning is encouraged, as long as your reasoning is clear user who only sees the maze image.
Provide a step-by-step step reasoning that logically leads to the given answer.
User Prompt Version 2: {question}
Here the precise maze layout and the required final answer to guide your analysis: Maze Text Map: {formatted_mag
Required Final Answer: coorectctrr)
Very Important Instructions for Your Reasoning:** The text map and the answer are provided to you so that you can leverage them to produce accurate reasoning. Your response must be completely self-contained analysis that reads naturally to a user who can only see maze image.
**
**Crucially, do not repeat the text map in your response. However, you coordinates to make your step-by-step reasoning precise.
Describe the start, goal, and holes in plain language (e.g., "the starting square," "the "the goal,"
"the ice holes").
* Do mention hint that the solution the text map was provided to you.**
Provide step-by-step reasoning that logically leads the given answer as if you solving it independently.
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Published as a conference paper at ICLR 2026
Frozen Lake Interleaved Thought Prompt
First-Round Prompt: {question}
Here is the precise maze layout to guide your analysis: {formatted_mag
Legend:
S = Start
G Goal
Hole
F = Frozen Surface
In your response, DO NOT provide the answer to the question (i.e., the path). You will be given a chance to answer later. Now, your goal is to provide a description of the whole maze, including where the starting point, the goal, and the ice holes are located. Begin by saying something to the effect of "Let's first map out the maze". Do not say this verbatim though.
** Important Instructions for Your Response:** The text map is provided to you so that you can accurately describe the maze. However, your output must be clear
user who only sees the maze image.
Describe the start, goal, and holes in plain language (e.g.. "the starting square," "the "the goal," Do not mention or imply that you are given this textual maze map.
"the ice holes") instead of using the symbols S, G, H. Using coordinates to describe the maze map encouraged, as long as you clearly define everything that user who only sees the maze image still understand it. Once you finish describing the maze, you should say something to the effect "Now let's solve the problem and draw the path", but not verbatim. DO NOT end the response by repeating the rules or instructions, such as the "player must go from the start to the goal or that they must avoid all holes", "with this overview, you have complete understanding of the positions of the starting square, the goal, and all ice holes in the maze." Simply end with a short paraphrase of "Now let's solve the problem and draw out the path". Make sure to mention the action of "plotting", "visualizing", or "drawing".
You should not sound like you are writing this for another person. This should read like an inner monologue.
Second-Round Prompt:
The image above visualizes solution path in red. The path is {correct_pathh path). Your task is to perform verification.
Your response must be a self-contained analysis that reads as *you* solved the problem and created the visualization for final check, do not say things like "the provided visualization". which implies it is given to you and disrupts the coherence of the whole reasoning process. Instead, call "my solution". Visually analyze the path in the image and check if the path is correct.
**Do not act as if you were responding to user knew the correct answer beforehand. Your initial response, the visualized path, and your next response should read like stan- dalone, coherent solution. Visually analyze the path in the image, check if it is correct (even though you know is), and output the correct path again in a (boxed(}- you output ** **xxactly** the provided answer in the provided format.
crucial that
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Reviews:
[{"review_id": "95imiEj2eB", "reviewer": "Reviewer_qnFT", "summary": "The authors present ThinkMorph, a model for interleaved multimodal reasoning with text and images. They train a joint image-text generative MLLM on a few particular tasks with data generated for those tasks, such as visual search and jigsaw assembly. They create a data generation pipeline for each of these, which create visual tokens and text tokens specific to the task after defining visual manipulations for each tasks. Training on these results in improved performance for the MLLM compared to just the text or just the visual tokens. They also show some improvements in test-time scaling, and note some interesting emergent properties on out-of-domain data.", "questions": "- It wasn't fully clear to me how the visual output tokens work in that: is it producing tokens for the full output image, including the parts of the input it wants to reproduce, or just the manipulation it wants to show? This would change how it should be presented.\n- How dependent is the method on the pretrained model used?\n- How do the authors hypothesize the unseen manipulations emerge despite the lack of training? \n- How is it possible for the performance to ever decrease with higher N in Table 4?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The problem is well-motivated, mixing reasoning across modalities and aiming to identify where it performs beyond unimodal reasoning and where it may not.\n- I appreciate the qualitative analysis and notes on emergent properties.\n- Results are evaluated on meaningful OOD benchmarks and provide some benefit in many settings.\n- The exploration of the emergent properties is interesting and sheds light on the method, such as the test-time mode dynamics of when mode-switching occurs in Figure 2.", "weaknesses": "- Results seem specific to the tasks specific considered, but are presented as general conclusions about interleaved reasoning more broadly. I appreciate the out-of-distribution results, but they generally target similar tasks -- like V-Star evaluating fine-grained visual search. \n- The idea that the multimodal setting has a larger reasoning space is intuitively appealing, but it's unclear if this is actually the origin of the test-time advantage just from the presented results. For instance, the multimodal training could hypothetically lead to broader diversity within just text, leading to higher best-of-N performance. It would be interesting to see the hypothesis empirically borne out with some quantitative understanding of the diversity of output samples and qualitative examples of how trajectories differ.", "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": "nwZ5ytbSuN", "reviewer": "Reviewer_MNkK", "summary": "ThinkMorph introduces a unified multimodal reasoning framework that enables dynamic interleaving of textual and visual generation, allowing language and vision to mutually advance reasoning rather than operate in parallel. It eliminates reliance on external tools by treating image generation as predictable tokens within an autoregressive sequence, using a VAE and diffusion model to internally construct visual edits. Training is based on 24,991 carefully curated interleaved reasoning traces across four vision-centric tasks—jigsaw assembly, spatial navigation, visual search, and chart refocus—forcing the model to alternately reason with language and produce visual interventions. The approach achieves substantial performance gains on vision-centered benchmarks, including a 38.75 percent improvement on jigsaw assembly and competitive results on MMVP and SAT, matching or exceeding large proprietary models. Crucially, the model exhibits emergent capabilities not present in training data, such as unseen visual manipulation—including zoom-in and image inpainting—and autonomous switching between text-only, image-only, and interleaved reasoning modes. These abilities arise from the richer exploration space enabled by interleaved training, allowing test-time scaling through heterogeneous sampling across reasoning modalities. Unlike prior methods relying on tool augmentation or implicit visual tokens, ThinkMorph establishes a unified representation where vision is not a cue but an active component of reasoning. The work shifts the paradigm from passive perception to active visual thinking, demonstrating that integrated generation and understanding can cultivate higher-level multimodal intelligence. Although computationally intensive, it establishes a foundational pathway for building unified models capable of adaptive, human-like multimodal reasoning.", "questions": "This paper highlights ThinkMorph's interleaved textual and visual reasoning, demonstrating significant performance gains on vision-centric tasks. However, generating and re-integrating image content into the LLM's token sequence incurs substantial computational overhead due to VAE encoding, diffusion denoising, and increased sequence length. Are these visual reasoning scenarios fundamentally intractable for pure text-based models, thus strictly necessitating this high-resource graphical reasoning for effective problem-solving?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. ThinkMorph pioneeringly introduces the \"interleaved chain-of-thought\" multimodal paradigm, enabling deep, dynamic, and complementary synergy between language and vision during reasoning. It moves beyond treating vision as passive input or an external tool, allowing the model to autonomously generate and modify visual content within its reasoning sequence, achieving intrinsic visual manipulation. This unique mechanism also fosters the emergence of novel visual operations and intelligent modality adaptation behaviors not previously observed.\n\n2. This work achieves unified representation and seamless switching between language and vision within a single autoregressive Transformer architecture. By incorporating special tokens like <image start> and <image end>, the model can autonomously trigger internal VAE and diffusion models for image generation and re-insertion. Coupled with a negative log-likelihood loss for text and a mean squared error loss for images, ThinkMorph ensures both the accuracy of linguistic reasoning and the quality of visual generation, thereby achieving effective integration of multimodal signals.\n\n3. This paper excels in its presentation, clearly articulating the limitations of existing models and grounding its core motivation in human \"think-and-sketch\" cognitive patterns. Through rich illustrations and reasoning examples, it provides intuitive demonstrations of how interleaved reasoning functions and the visual capabilities that emerge from the model. Furthermore, the paper systematically compares its work with tool-augmented, preliminary interleaving, and implicit visual token approaches, highlighting its distinct advantages.\n\n4. ThinkMorph's efficacy is thoroughly validated through experiments on four categories of custom-built, vision-centric tasks. These datasets were specifically designed to embody the characteristics of interleaved reasoning. The experimental results not only demonstrate significant performance improvements across multiple tasks via quantitative metrics but also present qualitative analyses of generated interleaved reasoning chains, visual operation instances, and adaptive modality selection, providing strong evidence for the method's effectiveness.", "weaknesses": "1. The emergent visual manipulation capabilities, such as image inpainting and zoom-in, are presented as indicators of higher-level intelligence, yet their geometric and semantic consistency with the original image structure remains unvalidated. It is unclear whether the generated regions are aligned with the true physical structure or whether the model may produce visually plausible but physically incorrect details in the absence of explicit scale or spatial constraints.\n\n2. Autonomous mode switching occurs in only a small fraction of samples, approximately 2 to 5 percent, and is interpreted as an adaptive behavioral trait. However, the underlying triggering mechanisms are not quantitatively analyzed. It remains unresolved whether this behavior is driven by semantic properties of the task, such as the presence of color, quantity, or relational queries, or whether it arises from latent biases in the training data distribution.\n\n3. The computational overhead of interleaved reasoning during inference is not addressed. Each instance of image generation requires execution of VAE encoding, diffusion-based denoising, and re-encoding into clean visual tokens, resulting in substantial latency and limiting scalability.", "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": "9hg7DeQePt", "reviewer": "Reviewer_vxwn", "summary": "This paper introduces ThinkMorph, a unified multimodal model designed to perform synergistic, interleaved Chain-of-Thought (CoT) reasoning that alternates between text and image generation. The core premise is that for vision-centric tasks (e.g., spatial reasoning, visual search), pure textual CoT is insufficient, and a dynamic interplay between language and vision can more effectively mimic human \"think-and-sketch\" reasoning.", "questions": "- **Q1:** In my understanding, interleaved reasoning is more costly than single-modality reasoning. In Tab. 2, why are VSP-Main and VisPuzzle reported only in non-think mode for Bagel, whereas all think modes are reported for ThinkMorph?\n- **Q2:** In Sec. 3.6, it says:\n> This property extends our observation of autonomous mode switching (Emergent Property 2) to a\nmore dynamic setting. When generating multiple reasoning chains (e.g., N=8), a subset of samples\nswitch between text-only and interleaved modes as illustrated in Figure 2. Specifically, the modality\ndistribution changes as the number of candidate solutions increases. The proportion of pure textbased reasoning chains decreases from 18.8% at N=1 and N=2 to 15.2% at N=8. In parallel, the\ncontribution from interleaved chains—the dominant modality—increases from 81.2% to 84.8% over\nthe same range. This dynamic shift in modality distribution is directly correlated with an increase\nin overall task accuracy, which improves from 51.3% at N=1 to 58.6% at N=8, as the modeling in\n§ 2.3 with modality diversity.\n\nAlthough the proportion of text-based reasoning decreases while the proportion of interleaved reasoning increases with more rollouts, where do the final accuracy gains primarily originate—from text-based or interleaved reasoning?\n- **Q3:** The paper states that the interleaved-only model switches to text-only mode in 2-5% of samples, yielding \"strikingly high accuracy, up to 90%\". This is ambiguous. Does this mean 90% of that 2-5% subset of samples were correct? Or that 90% was the highest accuracy achieved on any sample in that subset? More importantly, why does the analysis in Sec 3.6 pivot to a hybrid-trained model? This feels like it sidesteps the more interesting emergence claim. Can you clarify the \"emergence\" of mode switching in the interleaved-only model, as this is a key claim?\n- **Q4:** The Test-Time Scaling results are impressive. However, generating N interleaved chains, each containing images, is computationally expensive. Have you analyzed the performance vs. cost curve?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- **Novel finding on interleaved reasoning**: A key contribution of the paper is the demonstration that training on interleaved text–image reasoning data enables the emergence of visual manipulation capabilities. This suggests that the model acquires a general-purpose mechanism for using visual representations as intermediate reasoning steps, rather than merely memorizing task-specific mappings.\n\n- **Synthetic data curation methodology**: The paper’s effectiveness largely stems from its carefully designed data curation pipeline. A central insight is the construction of reasoning traces in which textual and visual components are complementary and non-isomorphic—an approach that offers a reproducible and valuable strategy for future work in multimodal reasoning.\n\n- **Good empirical performance**: ThinkMorph achieves significant improvements over its base model and exhibits robust out-of-domain generalization. It attains competitive or superior performance compared to substantially larger models on several challenging multimodal benchmarks, including SAT and MMVP.", "weaknesses": "- **\"Emergent Property 1\" claim is overstated.** In my personal experience, the original Bagel model was already capable of zooming in/out and inpainting images when explicitly prompted—for example, given a prompt like \"find the banana in the image,\" it could crop out the banana region from the input image. This capability appears to pre-exist in Bagel and is not necessarily a result of ThinkMorph’s training. Therefore, the claim of Emergent Property 1 lacks sufficient support, as the described behavior is already present in the base model.\n\n- **\"Autonomous Mode Switching\" claim is conflated:** The most interesting claim is that a model trained exclusively on interleaved data can emerge a text-only reasoning mode. However, this is based on a \"rare (2-5%)\" observation. To analyze this \"mode switching\" further, the paper then pivots in Section 3.6 to a hybrid-trained model, which was explicitly trained on different modes. This is a much less interesting result and conflates the \"emergence\" claim with a simple \"multi-task\" training setup.\n\n- **Scope of evaluated tasks:** While the four chosen tasks are well-justified, they represent a specific class of vision-centric, puzzle-like problems. The generalizability of this approach to more open-ended, creative, or dialog-based multimodal tasks remains an open question.", "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": "lAkVJI3H9Y", "reviewer": "Reviewer_qUFF", "summary": "This paper proposes ThinkMorph, a unified multimodal reasoning model designed to perform interleaved reasoning across language and vision. The authors construct a high-quality data pipeline to generate interleaved reasoning traces. Through this training strategy, ThinkMorph reportedly achieves improvements on several multimodal reasoning benchmarks.", "questions": "1. Could the authors clarify how the *text reasoning*, *visual reasoning*, and *interleaved reasoning* models in Table 2 were obtained? Specifically, how were these variants fine-tuned within the Bagel framework, especially the details of the training data. And how do they relate to *ThinkMorph* as reported in Table 3?\n\n2. In line 397, the authors mention several cases where the model “shifts to text-only reasoning.” Could the authors provide concrete examples or visualizations of these cases, and elaborate on why the model makes this reasoning shift?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "The paper constructs a high-quality interleaved reasoning dataset, which serves as a valuable resource for training and analyzing unified multimodal reasoning models. It also provides relatively complete implementation and reproducibility details, allowing others to replicate the experiments.", "weaknesses": "1. **Limited qualitative showcases.**\n   The paper provides too few qualitative examples to substantiate its claims—particularly for the highlighted *emergent properties*. Demonstrations of abilities such as multi-bounding box generation, zoom-in, and crop operations are only shown in a single example (Figure 1), which is insufficient to convincingly establish the claimed emergent behaviors.\n\n2. **Lack of detailed experimental settings.**\n   The paper lacks clarity regarding the differences between experimental settings, especially between *visual reasoning* and *interleaved reasoning* as reported in Table 2. \n3. **Unfair baseline comparison.**\n   In line 344, the authors claim that “interleaved training consistently pushes unified models far beyond their previous limitations.” However, the comparison baseline appears to be the model that was **not** fine-tuned. A fairer comparison would involve a baseline fine-tuned on the same task using *text-only reasoning data*, so that the contribution of interleaved training can be more precisely isolated and validated.\n\n4. **Ambiguity in adaptive modality selection.**\n   The paper claims that ThinkMorph exhibits “adaptive modality selection,” where the model dynamically chooses between text-only, image-only, or interleaved reasoning depending on the task. However, there is no detailed discussion of image-only reasoning in the experiments, and the relationship between *image-only reasoning* and *visual reasoning* (as shown in Table 2) remains unclear, making this claim confusing and insufficiently supported.\n\n5. **Questionable baseline results.**\n   The reported GPT-4o result on SAT (28) in Table 3 is significantly lower than in the original paper, which raises concerns about the experimental setup or evaluation protocol. The discrepancy should be discussed and justified.\n\n6. **Typos.**\nThere are several minor typographical and formatting errors in the paper. For example, in Table 5, “Reaonsing” should be corrected to “Reasoning.”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)