RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation

2026-06-22

AI Summary

Current image generation models exhibit limitations in handling ambiguous intents, logical reasoning, and out-of-distribution knowledge, primarily due to the absence of deep reasoning capabilities and real-time access to external information. This work proposes the first training-free, plug-and-play multi-stage agent framework that seamlessly integrates retrieval-augmented generation with deep reasoning. By employing a closed-loop “question-answering” mechanism, the framework autonomously identifies gaps in logic and knowledge, dynamically plans retrieval and reasoning steps, and orchestrates them in an adaptive manner. Introducing the agent paradigm into image generation for the first time, the method achieves significant performance gains on the WISE Verified and RISEBench benchmarks, delivering absolute improvements of 0.313 and 19.70 for Qwen-Image and Qwen-Image-Edit-2511, respectively, thereby establishing a new state of the art among open-source models.

Abstract

Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. However, when handling ambiguous intentions, logical reasoning, and Out-of-Distribution (OOD) knowledge, existing image models often yield sub-optimal results due to a lack of deep reasoning capabilities and real-time external information. Although emerging unified understanding-and-generation models attempt to bridge this gap, they remain constrained by their intrinsic parameter scales and static knowledge gaps. Inspired by agentic paradigms, we propose RS-Gen: a plug-and-play, training-free, multi-stage image agentic framework. RS-Gen innovatively introduces a "Questioning-and-Solving" closed-loop mechanism to accurately identify logical issues and knowledge gaps, autonomously planning actions to bridge information deficits and execute deep logical reasoning. Extensive experiments demonstrate that RS-Gen significantly expands the capability boundaries of foundational image generation and editing models. Specifically, on the WISE Verified and RISEBench benchmarks, RS-Gen yields substantial absolute performance gains of 0.313 for Qwen-Image and 19.70 for Qwen-Image-Edit-2511, respectively, successfully elevating both to the state-of-the-art (SOTA) level among open-source models.

Problem

• ambiguous intentions
• logical reasoning
• Out-of-Distribution knowledge
• image generation
• reasoning capability

Innovation

• agentic framework
• reasoning
• search-augmented generation
• closed-loop mechanism
• out-of-distribution knowledge