LLM-I: LLMs are Naturally Interleaved Multimodal Creators
Authors: name: Zirun Guo, name: Feng Zhang, name: Kai Jia, name: Tao Jin

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.