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EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing

Umar Khalid, Kashif Munir, Hasan Iqbal, Azib Farooq, Jing Hua, Nazanin Rahnavard, Chen Chen, Victor Zhu, Zhengping Ji

TL;DR

This work tackles the challenge of editing complex visual content under ambiguous, multimodal instructions by introducing EVLM, a vision–language model that employs reflective multimodal reasoning. EVLM combines Chain-of-Thought supervision with Reflection-Aware KL-Divergence Target Optimization (RKTO) and is trained on a Reflective-Edit dataset of 30,000 CoT examples to produce concise, context-aware editing instructions and target masks. The approach yields strong gains in alignment with human intent across 2D, 3D, and 4D editing tasks and demonstrates robust cross-domain performance with diffusion-based editors. By enabling interpretable reasoning and refined alignment, EVLM offers a scalable foundation for multimodal editing that can generalize to varied visual reasoning requirements in real-world applications.

Abstract

Editing complex visual content from ambiguous or partially specified instructions remains a core challenge in vision-language modeling. Existing models can contextualize content but often fail to infer the underlying intent within a reference image or scene, leading to inconsistent or misaligned edits. We introduce the Editing Vision-Language Model (EVLM), a system that interprets ambiguous instructions in conjunction with reference visuals to produce precise, context-aware editing prompts. EVLM's key innovation is a reflective reasoning framework that translates subjective user intent into structured, actionable outputs by aligning with human-rated rationales through Reflection-Aware KL-Divergence Target Optimization (RKTO). By combining Chain-of-Thought (CoT) reasoning with RKTO alignment, EVLM captures fine-grained editing preferences without relying on binary supervision. Trained on a dataset of 30,000 CoT examples with human-annotated rationale quality, EVLM achieves substantial gains in alignment with human intent. Experiments across image, video, 3D, and 4D editing tasks show that EVLM generates coherent and high-quality instructions, providing a scalable foundation for multimodal editing and reasoning.

EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing

TL;DR

This work tackles the challenge of editing complex visual content under ambiguous, multimodal instructions by introducing EVLM, a vision–language model that employs reflective multimodal reasoning. EVLM combines Chain-of-Thought supervision with Reflection-Aware KL-Divergence Target Optimization (RKTO) and is trained on a Reflective-Edit dataset of 30,000 CoT examples to produce concise, context-aware editing instructions and target masks. The approach yields strong gains in alignment with human intent across 2D, 3D, and 4D editing tasks and demonstrates robust cross-domain performance with diffusion-based editors. By enabling interpretable reasoning and refined alignment, EVLM offers a scalable foundation for multimodal editing that can generalize to varied visual reasoning requirements in real-world applications.

Abstract

Editing complex visual content from ambiguous or partially specified instructions remains a core challenge in vision-language modeling. Existing models can contextualize content but often fail to infer the underlying intent within a reference image or scene, leading to inconsistent or misaligned edits. We introduce the Editing Vision-Language Model (EVLM), a system that interprets ambiguous instructions in conjunction with reference visuals to produce precise, context-aware editing prompts. EVLM's key innovation is a reflective reasoning framework that translates subjective user intent into structured, actionable outputs by aligning with human-rated rationales through Reflection-Aware KL-Divergence Target Optimization (RKTO). By combining Chain-of-Thought (CoT) reasoning with RKTO alignment, EVLM captures fine-grained editing preferences without relying on binary supervision. Trained on a dataset of 30,000 CoT examples with human-annotated rationale quality, EVLM achieves substantial gains in alignment with human intent. Experiments across image, video, 3D, and 4D editing tasks show that EVLM generates coherent and high-quality instructions, providing a scalable foundation for multimodal editing and reasoning.

Paper Structure

This paper contains 50 sections, 1 theorem, 15 equations, 10 figures, 14 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation.
  3. Related Work
  4. Reflection and Alignment in Multimodal Models.
  5. Diffusion-Based Editing Across Dimensions.
  6. Approach
  7. Reflective-Edit Dataset
  8. Supervised Fine-Tuning (SFT)
  9. Reflection-Aware KTO (RKTO)
  10. Instruction Effectiveness.
  11. Reflection Reward.
  12. Gradient Estimation and Optimization
  13. Experiments
  14. Model Architecture
  15. Evaluation on Reflective-Edit
  16. ...and 35 more sections

Key Result

Theorem 1

Let rewards $R_{\mathrm{eff}}$ and $R_{\mathrm{reflect}}$ be bounded in $[0,1]$, $w(\cdot)$ be non-decreasing and bounded, and training steps sufficiently small. Define Then any update step that decreases $\mathcal{L}_{\mathrm{RKTO}}$ in expectation also decreases $\mathbb{E}_x[\mathcal{K}(\phi)]$, ensuring joint alignment of output and reflection distributions.

Figures (10)

  • Figure 1: Reference image and prompt for the 3D editing task: "An Einstein Face!" The reference includes an image with a mustache and the image-with-text "Green Jacket." These were provided to GPT-4o, along with supporting prompts (details in supplementary), to guide the generation of accurate editing instructions. GPT-4o encountered challenges integrating textual, visual, and OCR information to produce coherent instructions. Despite multiple attempts, DALL-E 3 guided by GPT-4o was unable to generate the desired edited image that fully aligns with the referenceintent.
  • Figure 2: EVLM enables editing across 2D, 3D, and 4D tasks. Given a reference image, video, or text instruction, EVLM generates precise and context-aware editing transformations. Examples include color and style modifications in 2D and 3D, and texture or dynamic edits in 4D scenarios. These results highlight EVLM's multimodal understanding of spatial, temporal, and semantic cues for complex visual editing.
  • Figure 3: Overview of our data preparation pipeline. Given a reference and target image, GPT-4o produces a structured chain-of-thought rationale through initial, intermediate, and reflective reasoning. Only the reflective and final outputs are used to construct RKTO training data, with human annotators providing “desired” labels when reasoning aligns with intended edits.
  • Figure 4: Image Editing Results Using Reference Images or Text Prompts. The first row demonstrates EVLM's ability to refine vague textual prompts into precise editing instructions and to produce masks for targeted edits. The second row compares EVLM to an image-based editing baseline, illustrating improved alignment to reference-guided transformations.
  • Figure 5: Text-based editing across video, 3D, and 4D tasks. EVLM + IP2P surpasses baselines including Any-V2V, Tune-A-Video, IN2N, and IG2G. It delivers improved style consistency ("Bronze," "Van Gogh Style") and stable transformations across frames.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Theorem 1: Monotonic Alignment Guarantee
  • proof