Reasoning-Informed Visual Editing

AI summary

Overview

Research area: Computer Vision — multimodal image editing, benchmark design, and agentic systems for visual generation.

Technical level: Advanced. The paper assumes familiarity with large multimodal models (LMMs), diffusion-based editors, unified multimodal models, and LLM-as-a-judge evaluation.

Scope (one sentence): The paper introduces RISEBench++, a 1,000-case bilingual benchmark spanning six reasoning dimensions for "Reasoning-Informed viSual Editing" (RISE), plus RISE-Agent, a training-free agentic framework, and evaluates 58 visual editing approaches on it.

What This Paper Is About

Current image editing models can follow simple, explicit instructions, but they struggle when the desired edit must first be inferred — for example, working out what a scene should look like based on time of day, cause and effect, spatial structure, logical rules, or counterfactual "what if" conditions. The authors argue there was no systematic benchmark for this ability, so they build one (RISEBench, then its expanded version RISEBench++) and also propose an agent framework (RISE-Agent) that explicitly reasons before it edits. Their goal is to measure how far today's editors are from genuinely reasoning-informed visual editing and to identify where they fail.

Key Contributions

1. RISEBench++ benchmark. A benchmark of 1,000 carefully curated, human-annotated test cases for RISE, released bilingually in English and Chinese, covering single-image, multi-image, and multi-turn settings. It expands the earlier RISEBench (a NeurIPS 2025 Datasets and Benchmarks Oral, Top 0.35%) from 360 to 1,000 test cases.

2. A hierarchical reasoning taxonomy. Six reasoning dimensions — Temporal, Causal, Spatial, Logical, Counterfactual, and Hybrid Reasoning — decomposed into 12 subcategories and 65 fine-grained task types. The paper splits these conceptually into knowledge-driven (temporal, causal), perception-driven (spatial, logical), imagination-driven (counterfactual), and hybrid (multi-turn) demands.

3. A three-dimension evaluation framework with an improved LMM-as-a-judge pipeline. Instruction Reasoning, Appearance Consistency, and Visual Plausibility are scored by both human judges and LMM judges, using dimension-specific textual/visual evidence (reference text or reference image) and tailored scoring rubrics. The paper reports validating agreement between LMM scores and human experts.

4. RISE-Agent. A training-free agentic framework with three roles — a planner (target-state inference, web search, seven code-based tools), an executor (a generative editor plus 12 deterministic programmatic editing operations), and a verifier (pass / refine / replan) — that outperforms most strong existing approaches across diverse RISE tasks.

Main Findings

• Reasoning is the bottleneck, not image synthesis. The paper states that recent models already show strong appearance consistency and visual plausibility, whereas instruction reasoning remains the major challenge — especially for logical and hybrid reasoning tasks.

• Even the best evaluated approach is far from solved. Out of 58 evaluated approaches, GPT-Image-2.5 Sunburst achieved the best overall result at only 56.6% accuracy, where a sample counts as solved only if it scores 5 on all three evaluation dimensions.

• Dimension-level leaders differ. On the English portion of RISEBench++, Nano Banana Pro achieves the highest Instruction Reasoning score, while GPT-Image-2.5 Sunburst leads on Appearance Consistency and Visual Plausibility.

• A clear capability progression across model generations. Early open-source models — BAGEL, Step1X-Edit, FLUX, Emu2, and OmniGen — exhibit limited reasoning ability and struggle broadly. More recent open-source models such as Qwen-Image and HunyuanImage substantially narrow the gap, but proprietary models (the GPT-Image series and Nano Banana series) generally achieve stronger overall performance.

• Agentic methods are competitive. RISE-Agent outperforms most proprietary models and existing agentic methods across a broad range of RISE tasks, without any additional training.

• Benchmark scale and coverage exceed prior work. Table 1 compares RISEBench++ with eight prior benchmarks: it is the only one listed with reasoning, multi-turn, multi-image, and agent support combined, and it has the most task types (65) and evaluated models (58) among the compared benchmarks. Prior entries range from 4 to 25 task types and from 7 to 22 evaluated models.

• Reasoning-aware editing extends across modality. Related work cited by the paper includes video benchmarks (Physics-IQ, RISE-Video) that test implicit constraints over time, indicating the same reasoning gap appears in video generation.

Methodology in Plain English

The authors first define what "reasoning-informed visual editing" means by building a taxonomy from the ground up. They hand-design six categories of reasoning demand and then write 1,000 test cases by hand, split as 200 each for temporal, causal, spatial, and logical reasoning, and 100 each for counterfactual and hybrid reasoning. Each case comes in English and Chinese, and some require multiple input images or several turns of edits.

For scoring, they use three separate questions about each output image: did the model actually do what the instruction implied (Instruction Reasoning), did it leave unrelated parts of the image alone (Appearance Consistency), and does the changed part look clean and coherent (Visual Plausibility). Human raters score against detailed guidelines; in parallel, LMM judges — Gemini-3-Flash for the first two dimensions and Gemini-3.1-Flash-Lite for the third — apply the same dimensions using per-dimension prompts and, depending on the task, either a written reference description or a reference output image. Scores are normalized to a 1–5 range and rescaled to a 100-point scale, and "accuracy" is the share of cases that receive a full 5 on all three dimensions.

Then they build RISE-Agent to test whether the bottleneck can be addressed without training. A planner looks at the image and instruction, optionally searches the web for domain knowledge, and calls one of seven code-based solvers (arithmetic, date calculation, grid and graph shortest-path search, Sudoku solving, coordinate geometry, matrix transformation) when a problem can be formalized. It outputs either a prompt for a generative editor or a declarative program for direct manipulation. The executor either generates content or applies 12 deterministic operations (line drawing, polygon rendering, text editing, region recoloring, content relocation, and others), which leave untouched pixels unchanged. A verifier then judges the result and decides to pass, refine locally, or send everything back for replanning, within fixed refinement and replanning budgets.

Why This Matters

Impact on research. The paper reframes visual editing evaluation away from surface-level instruction following toward reasoning ability, and argues the key bottleneck is the reasoning step before generation. Its 65-type taxonomy lets failures be diagnosed per ability rather than as a single average, and it provides an open code repository and dataset (GitHub VisionXLab/RISEBench and a Hugging Face collection) plus a re-validated LMM-judge pipeline that other groups can reuse.

Real-world applications (as described in the paper):

• Context-aware image modification, such as adjusting lighting to match a scene's time of day.
• Intelligent object insertion or removal with semantic consistency.
• Content adaptation based on inferred user intent.
• Precise editing of diagrams, grids, charts, and puzzles, where the executor's deterministic programmatic tools are used because small geometric or symbolic errors invalidate results.

Industry relevance. The comparison spans 34 open-source models, 19 closed-source models, and 5 agentic methods, including commercial systems, making the results directly informative for teams choosing or building editing products. The finding that a training-free agent can beat most proprietary models indicates that pipeline design around existing open-source executors may be as valuable as scaling the generator alone.

Future Directions

• Closing the instruction-reasoning gap. Appearance consistency and visual plausibility are largely solved in the paper's results, while instruction reasoning — particularly logical and hybrid reasoning — remains weak, making it the clearest target for model and pipeline improvement.

• Extending agentic approaches. RISE-Agent is described as training-free and evaluated against existing agentic methods; whether training the planner, executor, or verifier (rather than prompting them) improves results is left open.

• Scaling reasoning-informed evaluation to other modalities. The paper's related work points to reasoning benchmarks for video generation with stronger temporal and cross-frame demands, motivating adaptation of the RISE taxonomy beyond static images.

• Diagnosing failure modes at finer granularity. The authors specifically highlight the value of analyzing failure modes across individual reasoning dimensions, implying further per-subcategory and per-task-type analysis of the 58 evaluated approaches.

Target Audience

Researchers and engineers working on multimodal generative models, image editing systems, and agentic pipelines; benchmark and evaluation researchers interested in LMM-as-a-judge methodology and human agreement; and industry teams selecting or deploying editing models who need to know where state-of-the-art systems still fail. Readers need background in multimodal models and image editing to follow the taxonomy and evaluation design, though the plain-language framing of the six reasoning dimensions makes the core argument accessible to a broader technical audience.

Note: the provided paper content is truncated partway through the list of open-source models evaluated, so the complete roster beyond HunyuanImage-3.0-Instruct through Emu2, and the full numerical results tables, are not available in this excerpt.

Authors’ abstract

Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but still face challenges in visual editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To study this gap, we introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE), and extend it to RISEBench++, a more comprehensive and fine-grained benchmark for this emerging task. RISEBench++ extends the taxonomy into a hierarchical scheme spanning six reasoning dimensions: Temporal, Causal, Spatial, Logical, and Counterfactual Reasoning, together with Hybrid Reasoning integrating multiple reasoning types across multi-turn edits. These dimensions are further decomposed into 12 subcategories and 65 fine-grained task types. We expand input formats to include multi-image conditioning and scale the benchmark to 1000 human-annotated test cases, released in English and Chinese. We also improve our evaluation framework, assessing Instruction Reasoning, Appearance Consistency, and Visual Plausibility with human judges and an LMM-as-a-judge approach for more reliable and calibrated judgements. Beyond benchmarking, we introduce RISE-Agent, a training-free agentic framework integrating reasoning-driven planning, tool-augmented execution, and verifier-guided refinement, outperforming most strong existing approaches across diverse RISE tasks. We evaluate 58 visual editing approaches, including 34 open-source models, 19 closed-source models, and 5 agentic methods. The results reveal substantial challenges in reasoning-based visual editing, with even the strongest evaluated approach, GPT-Image-2.5 Sunburst, achieving only 56.6% accuracy. RISEBench++ highlights the limitations of contemporary editing models, provides insights, and indicates future directions for reasoning-aware visual editing.