ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation
Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way to convey complex information. Current paradigms rely on either image generation or retrieval augmentation, yet they typically treat the two as mutually exclusive paths, failing to unify factuality with creativity. We argue that the next milestone in this field is Agentic Tool Planning, where the model serves as a central controller that autonomously determines when, where, and which tools to invoke to produce interleaved responses for visual-critical queries. To systematically evaluate this paradigm, we introduce ATP-Bench, a novel benchmark ...
| Authors | Yinuo Liu, Zi Qian, Heng Zhou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 March 2026 |
| Last updated at source | 22 August 2026 |
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