**摘要**
Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pivotal research frontier. The existing literature focuses primarily on tool-use within vision-perception tasks. However, such approaches typically rely on predefined heuristics for visual manipulation and are inherently incapable of addressing numer
👤 作者: Cong Han, Xiaohan Lan, Haibo Qiu, Yujie Zhong

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🔗 **[AIR ：使用MLLM中的代码进行自适应交错推理](https://arxiv.org/abs/2606.23678v1)**

> AIR: Adaptive Interleaved Reasoning with Code in MLLMs
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-06-23 23:10