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arXiv · 2509.13642

LLM-I: LLMs are Naturally Interleaved Multimodal Creators

Zirun Guo·Feng Zhang·Kai Jia·Tao Jin
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Abstract

We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the "one-tool" bottleneck of current unified models, which are limited to synthetic imagery and struggle with tasks requiring factual grounding or programmatic precision. Our framework empowers a central LLM or MLLM agent to intelligently orchestrate a diverse toolkit of specialized visual tools, including online image search, diffusion-based generation, code execution, and image editing. The agent is trained to select and apply these tools proficiently via a Reinforcement Learning (RL) framework that features a hybrid reward system combining rule-based logic with judgments from LLM and MLLM evaluators. Trained on a diverse new dataset using four different model backbones, LLM-I demonstrates state-of-the-art performance, outperforming existing methods by a large margin across four benchmarks. We also introduce a novel test-time scaling strategy that provides further performance gains. Project Page: https://github.com/ByteDance-BandAI/LLM-I.

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Zirun Guo, Feng Zhang, Kai Jia, Tao Jin. 2025-09-17. LLM-I: LLMs are Naturally Interleaved Multimodal Creators. https://arxiv.org/abs/2509.13642

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