CoT-Edit-14K: Multimodal Image Editing Dataset

• CoT-Edit-14K is a multimodal dataset that interleaves detailed textual rationales with visual tokens to facilitate step-by-step image editing and reasoning.
• It employs the MURE framework and MMDC paradigm to structure and refine editing tasks, enabling sequential sub-task decomposition and confidence-based selection.
• The dataset demonstrates enhanced performance on benchmarks using metrics like L1 error, SSIM, and LPIPS, proving its value for fine-grained visual manipulation research.

CoT-Edit-14K is a multimodal dataset comprising approximately 14,000 high-quality examples for image editing and reasoning. Each instance embodies an explicit chain-of-thought (CoT) structure that alternates between textual rationales and visual cues—such as positional masks or rendered content—enabling a step-by-step decomposition of editing tasks. Developed as part of the Multimodal Reasoning Edit (MURE) framework (Zou et al., 9 Oct 2025), CoT-Edit-14K targets fine-grained object manipulation and intricate visual layout reasoning, overcoming the limitations of previous textual- or coordinate-augmented CoT datasets.

1. Dataset Structure and Composition

The CoT-Edit-14K dataset is constructed to support native multimodal reasoning by interleaving text and image modalities. Each example in the dataset consists of multiple alternating reasoning steps:

• Textual Rationales: Describing the semantic intent behind each edit (e.g., "identify a white footstool with a black frame").
• Visual Tokens: Immediately following each textual step, a visual component is provided, typically in the form of spatial masks, region delineations, or newly generated image segments relevant to the edit.

The data covers diverse editing operations, including object removal, replacement, attribute and style modification, background change, text insertion, object addition, and variations in object size or behavior.

Dataset construction employed a two-pipeline approach:

• Visual Reasoning Pipeline: Tools such as Qwen-3 and Uniworld were used to categorize editing types and generate accurate spatial masks.
• Textual Pipeline: Qwen2.5-VL generated the corresponding textual rationales and editing instructions with respect to annotated visual evidence.

A significant manual filtering phase eliminated approximately 5,000 lower-quality examples, yielding the final set of 14,000 robust multimodal chains suitable for high-precision model training.

Component	Description	Example Type
Text Rationales	Stepwise edit explanations	"Remove the red apple"
Visual Tokens	Masks or generated content	Mask of apple region
Task Types	Diversity of edit operations	Object add/remove, style change

2. Multimodal Reasoning Edit (MURE) Framework

The MURE framework leverages CoT-Edit-14K to train models that integrate both text and image in the reasoning process. Unlike prior approaches based solely on textual instructions or spatial coordinates, MURE enforces a reasoning paradigm where each textual step is formally linked to a visual cue, separated by explicit tokens (e.g., ⟨visual start⟩, ⟨visual end⟩).

The reasoning process is defined as:

$$\left\{s^{(1)}, v^{(1)}, s^{(2)}, v^{(2)}, \ldots, s^{(k)}\right\}, O \sim f_\theta(\cdot \mid I, P)$$

where $s^{(i)}$ denotes a textual step, $v^{(i)}$ the corresponding visual token, $I$ the original image, $P$ the editing prompt, and $O$ the final output. The chain structure enables decomposition into sequential sub-tasks such as mask prediction, object synthesis, and result assembly. Training is conducted via an autoregressive cross-entropy loss for the text and a rectified MSE loss for the image components:

• Textual Loss:

$$L_{CE}^{text} = -\sum_{t \in T} \log P_\theta(s_t \mid y_{<t}, I, P)$$

• Image (Rectified Flow) Loss:

$$z_t^{(i)} = t \cdot z_0^{(i)} + (1-t) \cdot z_1^{(i)}, \quad t \in [0,1]$$

$$L_{MSE}^{image} = \mathbb{E}[\|f_\theta(z_t^{(i)} \mid y_{<t}, I, P) - (z_0^{(i)} - z_1^{(i)})\|^2]$$

The total objective is a weighted sum:

$$L_{total} = \lambda_{CE} \cdot L_{CE}^{text} + L_{MSE}^{image}$$

3. Deep Confidence Reasoning (MMDC)

To address reliability in intermediate visual step generation, the Multimodal Deep Confidence (MMDC) paradigm is introduced. This mechanism explores a branching tree of visual reasoning candidates at each step, computing a confidence score for each via a reward model (such as Qwen2.5-VL-7B in zero-shot mode):

$$S_{k,i} = R_\theta(v^{(k,i)} | s^{(k)}, y_{<k}, I, P)$$

where $v^{(k,i)}$ is the $i$-th candidate at depth $k$, and $y_{<k}$ are all previous steps. A greedy strategy selects the most confident candidate:

$i^* = \arg\max_i S_{k,i}$

This selective pruning minimizes hallucinated or low-quality outputs, producing more coherent chain reasoning and improved final edits.

4. Applications in Image Editing and Reasoning

CoT-Edit-14K, via the MURE framework and MMDC paradigm, provides resources for tasks requiring both fine-grained visual manipulation and semantically explicit reasoning. Applications include:

• Instruction-Based Image Editing: Enabling accurate transformations based on stepwise user instructions.
• Interactive Visual Generation: Supporting real-time or interactive systems that require interpretable intermediate states.
• AR/VR Dynamic Content Modification: Facilitating on-the-fly adaptation of visual environments through explicit reasoning chains.
• Research in Multimodal CoT Reasoning: Serving as a source for investigating multimodal chain-of-thought architectures, error minimization, and interpretability.

Experimental evaluations on MagicBrush, Emu, and SmartEdit benchmarks demonstrate superior performance in established metrics such as L1 error, CLIP/DINO similarity, SSIM, and LPIPS when compared to state-of-the-art editing systems (Zou et al., 9 Oct 2025).

5. Context: Relation to Prior Work and Dataset Implications

The CoT-Edit-14K dataset builds upon Chain-of-Thought (CoT) principles previously validated in textual reasoning and question answering domains (Zhao et al., 2023). Whereas textual CoT datasets focus on interpretability and sequential semantic decomposition, CoT-Edit-14K advances the paradigm by introducing explicit multimodal chains, thus addressing the inherent complexity of visual reasoning not tractable by text alone.

A plausible implication is that the CoT-Edit-14K construction methodology could be adapted for other domains—such as knowledge-intensive question answering—by incorporating post-editing and verification mechanisms for both textual and visual rationales. Further, the MURE framework’s interleaved approach creates a basis for investigating error propagation, chain reliability, and the effectiveness of external verification in multimodal editing agents.

6. Future Directions and Research Opportunities

Potential avenues for future research, as motivated by the CoT-Edit-14K release, include:

• Integration of Structured Knowledge: Extending the retrieval and reasoning process to leverage structured knowledge graphs or databases for more robust fact grounding.
• Enhanced Filtering and Robustness: Improving the visual/textual pipeline to reduce noise and address irrelevant or inconsistent intermediate steps.
• Generalization Across Modalities: Applying interleaved chain-of-thought methodology to reasoning tasks beyond image editing, such as multimodal QA or dialogue systems.
• Optimization of Confidence Scoring: Refining the deep confidence pruning criterion and exploring alternative uncertainty quantification methods.

This suggests that CoT-Edit-14K forms a foundation for future multimodal chain-of-thought research with emphasis on interpretability, reliability, and scalability in complex editing and reasoning tasks.