CyCLeGen: Cycle-Consistent Layout Prediction and Image Generation in Vision Foundation Models

2026-03-16

AI Summary

Existing vision models typically rely on disjoint modules for image understanding and generation, hindering coherent reasoning and efficient learning within a unified architecture. This work proposes CyCLeGen, a unified vision-language foundation model that jointly models comprehension and generation capabilities through a cyclic image↔layout generation mechanism within a single autoregressive framework. By integrating cycle-consistency learning with reinforcement learning–driven synthetic supervision, the model acquires introspective abilities and achieves data-efficient self-improvement. Experiments demonstrate that CyCLeGen significantly outperforms current methods across multiple benchmarks for both image understanding and generation, thereby validating the effectiveness and potential of a unified architectural approach.

Technology Category

• Computer Vision: Large Vision Models
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We present CyCLeGen, a unified vision-language foundation model capable of both image understanding and image generation within a single autoregressive framework. Unlike existing vision models that depend on separate modules for perception and synthesis, CyCLeGen adopts a fully integrated architecture that enforces cycle-consistent learning through image->layout->image and layout->image->layout generation loops. This unified formulation introduces two key advantages: introspection, enabling the model to reason about its own generations, and data efficiency, allowing self-improvement via synthetic supervision under a reinforcement learning objective guided by cycle consistency. Extensive experiments show that CyCLeGen achieves significant gains across diverse image understanding and generation benchmarks, highlighting the potential of unified vision-language foundation models.

Problem

• vision foundation models
• image understanding
• image generation
• cycle consistency
• unified architecture

Innovation

• cycle-consistent learning
• unified vision-language model
• autoregressive generation
• layout prediction
• self-improvement