Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis

TL;DR

Unify-Agent introduces a novel agentic framework for world-grounded image synthesis, effectively integrating reasoning, searching, and generation to handle complex, knowledge-intensive concepts.

Contribution

It presents a unified multimodal agent architecture, a tailored training pipeline with 143K trajectories, and a new benchmark FactIP for evaluating world knowledge grounding in image synthesis.

Findings

Significant improvements over base models in diverse benchmarks.

Approaches the world knowledge capabilities of top closed-source models.

Demonstrates the effectiveness of agent-based modeling in image synthesis.

Abstract

Unified multimodal models provide a natural and promising architecture for understanding diverse and complex real-world knowledge while generating high-quality images. However, they still rely primarily on frozen parametric knowledge, which makes them struggle with real-world image generation involving long-tail and knowledge-intensive concepts. Inspired by the broad success of agents on real-world tasks, we explore agentic modeling to address this limitation. Specifically, we present Unify-Agent, a unified multimodal agent for world-grounded image synthesis, which reframes image generation as an agentic pipeline consisting of prompt understanding, multimodal evidence searching, grounded recaptioning, and final synthesis. To train our model, we construct a tailored multimodal data pipeline and curate 143K high-quality agent trajectories for world-grounded image synthesis, enabling effective supervision over the full agentic generation process. We further introduce FactIP, a benchmark covering 12 categories of culturally significant and long-tail factual concepts that explicitly requires external knowledge grounding. Extensive experiments show that our proposed Unify-Agent substantially improves over its base unified model across diverse benchmarks and real world generation tasks, while approaching the world knowledge capabilities of the strongest closed-source models. As an early exploration of agent-based modeling for world-grounded image synthesis, our work highlights the value of tightly coupling reasoning, searching, and generation for reliable open-world agentic image synthesis.