MemoGen: Can Past Experience Improve Future Text-to-Image Generation?

2026-06-02

AI Summary

Current text-to-image generation models struggle with implicit visual constraints, relational reasoning, and prompts requiring external knowledge, while also failing to effectively leverage historical generation experience. This work proposes MemoGen—a training-free, test-time self-evolution framework that introduces, for the first time, a reusable experience memory mechanism into image generation. By employing an agent evolution layer, MemoGen establishes a closed-loop pipeline that integrates task comprehension, external evidence retrieval, constraint formulation, result evaluation, and memory storage, enabling continuous refinement of generation strategies without updating model parameters. Built upon the Qwen-Image backbone, MemoGen surpasses strong baselines such as Nano Banana Pro and GPT-Image-1 on knowledge- and reasoning-intensive benchmarks like WISE and Mind-Bench after only two rounds of evolution.

Abstract

Modern text-to-image models have achieved strong visual synthesis, yet remain unreliable when prompts require implicit visual constraints, relational reasoning, or external knowledge. Existing retrieval-augmented and agentic generation methods mitigate this issue by acquiring external knowledge, references, or refined prompts for the current request, yet they typically treat each generation as an isolated episode and do not systematically preserve past successes or failures for future use. In this work, we ask whether a text-to-image system can continually improve from its own generation experience without updating the underlying generator. We propose MemoGen, a training-free framework that augments existing image generators with an agentic evolution layer. For each task, MemoGen explicitly infers visual requirements, retrieves external evidence and references when necessary, translates them into executable generation constraints, evaluates the generated result, and stores task understanding, reference choices, visual feedback, successful strategies, and failure lessons as reusable experience memory. Across evolution rounds, the agent retrieves relevant experience to improve similar future generations, selectively repairing previously failed cases while preserving successful ones, thereby enabling test-time self-evolution without parameter updates. Extensive experiments on knowledge-intensive and reasoning-oriented benchmarks demonstrate the effectiveness of this paradigm: after only two evolution rounds, MemoGen built upon the open-source Qwen-Image backbone surpasses strong proprietary systems such as Nano Banana Pro and GPT-Image-1 on WISE and Mind-Bench, showing that explicit experience memory can serve as a powerful continual learning signal for reliable text-to-image generation.

Problem

• text-to-image generation
• experience memory
• relational reasoning
• external knowledge
• continual learning

Innovation

• experience memory
• retrieval-augmented generation
• test-time self-evolution
• training-free framework
• agentic evolution