# SCOPE: Structured Decomposition and Conditional Skill Orchestration for Complex Image Generation

arXiv: https://arxiv.org/abs/2605.08043 · HTML: https://mlsanity.com/paper/2605.08043/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 25 of 25)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://mlsanity.com/paper/2501.17811/) (2025)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [SoK: Agentic Skills -- Beyond Tool Use in LLM Agents](https://mlsanity.com/paper/2602.20867/) (2026)
- [XSkill: Continual Learning from Experience and Skills in Multimodal Agents](https://mlsanity.com/paper/2603.12056/) (2026)
- [SkillRouter: Skill Routing for LLM Agents at Scale](https://mlsanity.com/paper/2603.22455/) (2026)
- [From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning](https://mlsanity.com/paper/2504.16080/) (2025)
- [Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection](https://mlsanity.com/paper/2503.12271/) (2025)
- [Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models](https://mlsanity.com/paper/2602.02185/) (2026)
- [T2I-Copilot: A Training-Free Multi-Agent Text-to-Image System for Enhanced Prompt Interpretation and Interactive Generation](https://mlsanity.com/paper/2507.20536/) (2025)
- [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://mlsanity.com/paper/2603.28767/) (2026)
- [UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision](https://mlsanity.com/paper/2601.03193/) (2026)
- [Mind-Brush: Integrating Agentic Cognitive Search and Reasoning into Image Generation](https://mlsanity.com/paper/2602.01756/) (2026)
- [GEMS: Agent-Native Multimodal Generation with Memory and Skills](https://mlsanity.com/paper/2603.28088/) (2026)
- [PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization](https://mlsanity.com/paper/2509.12446/) (2025)
- [ImAgent: A Unified Multimodal Agent Framework for Test-Time Scalable Image Generation](https://mlsanity.com/paper/2511.11483/) (2025)
- [Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis](https://mlsanity.com/paper/2603.29620/) (2026)
- [CREA: A Collaborative Multi-Agent Framework for Creative Image Editing and Generation](https://mlsanity.com/paper/2504.05306/) (2025)
- [VisualPrompter: Semantic-Aware Prompt Optimization with Visual Feedback for Text-to-Image Synthesis](https://mlsanity.com/paper/2506.23138/) (2025)
- [GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation](https://mlsanity.com/paper/2510.07217/) (2025)
- [World-To-Image: Grounding Text-to-Image Generation with Agent-Driven World Knowledge](https://mlsanity.com/paper/2510.04201/) (2025)
- [Iterative Refinement Improves Compositional Image Generation](https://mlsanity.com/paper/2601.15286/) (2026)

## Built on by (most cited first, 7 of 7)

- [Qwen-Image-Agent: Bridging the Context Gap in Real-World Image Generation](https://mlsanity.com/paper/2606.26907/) (2026)
- [GenClaw: Code-Driven Agentic Image Generation](https://mlsanity.com/paper/2605.30248/) (2026)
- [GenRouter: Unified Workflow Routing for Agentic Image Generation](https://mlsanity.com/paper/2608.16721/) (2026)
- [WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing](https://mlsanity.com/paper/2609.05171/) (2026)
- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)
- [Editable Visual Design](https://mlsanity.com/paper/2609.04034/) (2026)
- [Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation](https://mlsanity.com/paper/2610.16224/) (2026)

## Abstract (arXiv)

While text-to-image models have made strong progress in visual fidelity, faithfully realizing complex visual intents remains challenging because many requirements must be tracked across grounding, generation, and verification. We refer to these requirements as semantic commitments and formalize their lifecycle discontinuity as the Conceptual Rift, where commitments may be locally resolved or checked but fail to remain identifiable as the same operational units throughout the generation lifecycle. To address this, we propose SCOPE, a specification-guided skill orchestration framework that maintains semantic commitments in an evolving structured specification and conditionally invokes retrieval, reasoning, and repair skills around unresolved or violated commitments. To evaluate commitment-level intent realization, we introduce Gen-Arena, a human-annotated benchmark with entity- and constraint-level specifications, together with Entity-Gated Intent Pass Rate (EGIP), a strict entity-first pass criterion. SCOPE substantially outperforms all evaluated baselines on Gen-Arena, achieving 0.60 EGIP, and further achieves strong results on WISE-V (0.907) and MindBench (0.61), demonstrating the effectiveness of persistent commitment tracking for complex image generation.