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 comprising 7,702 QA pairs (including 1,592 VQA pairs) across eight categories and 25 visual-critical intents, featuring human-verified queries and ground truths. Furthermore, to evaluate agentic planning independent of end-to-end execution and changing tool backends, we propose a Multi-Agent MLLM-as-a-Judge (MAM) system. MAM evaluates tool-call precision, identifies missed opportunities for tool use, and assesses overall response quality without requiring ground-truth references. Our extensive experiments on 10 state-of-the-art MLLMs reveal that models struggle with coherent interleaved planning and exhibit significant variations in tool-use behavior, highlighting substantial room for improvement and providing actionable guidance for advancing interleaved generation. Dataset and code are available at https://github.com/Qwen-Applications/ATP-Bench.
Thinking with Constructions: A Benchmark and Policy Optimization for Visual-Text Interleaved Geometric Reasoning
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；School of ECE, Peking University（北京大学电子工程学院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；Tencent Youtu Lab（腾讯优图实验室）
Stanford University（斯坦福大学）；Toyota Research Institute（丰田研究院）
What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the li…
RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Xiaohongshu Inc.（小红书公司）；Xi’an Jiaotong University（西安交通大学）
Interleaved Tool-Call Reasoning for Protein Function Understanding
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Institute of Artificial Intelligence, Soochow University（苏州大学人工智能研究院）；Changping Laboratory（昌平实验室）；School of Software Engineering, USTC（中国科学技术大学软件学院）
ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Research Center for SCIR, Harbin Institute of Technology（SCIR研究中心，哈尔滨工业大学）；NExT Research Center, National University of Singapore（NExT研究中心，新加坡国立大学）
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Department of Computer Science, The Hong Kong Polytechnic University（香港理工大学计算机科学系）
Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning
University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Impossible, Inc.（Impossible公司）
Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
Nanjing University（南京大学）；Tencent Youtu Lab（腾讯优图实验室）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；University of Washington（华盛顿大学）；The Chinese University of Hong Kong（香港中文大学）；Australian National University（澳大利亚国立大学）
Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4
State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University（信息处理国家重点实验室，计算机科学学院，PKU-Anker LLM实验室，北京大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）；School of Software & Microelectronics, Peking University（软件与微电子学院，北京大学）；School of Electronics Engineering and Computer Science, Peking University（电子工程与计算机科学学院，北京大学）
Bridging Modal Isolation in Interleaved Thinking: Supervising Modality Transitions via Stepwise Reinforcement
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）；Zhejiang University（浙江大学）；University of Chinese Academy of Sciences（中国科学院大学）
InterMT: Multi-Turn Interleaved Preference Alignment with Human Feedback
Institute for AI, Peking University（人工智能研究院，北京大学）；State Key Laboratory of General Artificial Intelligence, Peking University（通用人工智能国家重点实验室，北京大学）；Hong Kong University of Science and Technology（香港科技大学）
HAM-RAG: Hierarchy-Aware Multimodal RAG for Structure-Faithful Interleaved Generation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；ASCETEX INTERNATIONAL LIMITED（ASCETEX国际有限公司）；MOVENSYS Inc.（MOVENSYS公司）；Schneider Electric（施耐德电气）；Technical University of Munich（慕尼黑工业大学）；The Hong Kong University of Science and Technology（香港科技大学）
