arXiv：cs.AI· Quanlin Chen, Xiuwei Chen, Kun Xiang, Zehua Ma, Chunjin Ye, Boqiang Guo, Xianming Liu, Xiaodan Liang·· 21 小时前
L2T：让多模态大模型学会如何思考与思考多久
Adaptive Look, Flexible Thought: Learning How and How Long to Think in Multimodal Reasoning
AI 导读
研究者提出统一推理框架 L2T（adaptive Look, flexible Thought），将工具产生的视觉状态离散化为 visual tokens，使视觉感知与文本推理进入同一自回归空间，并通过模态感知路由奖励和难度感知快慢策略自适应选择推理模式与深度。在通用感知与推理密集任务上，L2T 相比显式工具调用方法保持竞争性性能的同时实现 1.7–3.6 倍吞吐提升。
正文
Abstract:The introduction of long chain-of-thought (CoT) reasoning and external tools has substantially improved the performance of multimodal large language models (MLLMs) on general and reasoning tasks. However, existing fast-slow reasoning paradigms mainly regulate textual reasoning length, while visual computation introduced by external tools or visual tokens is often treated as an auxiliary component. This separation prevents MLLMs from jointly optimizing visual perception and textual reasoning. To address this issue, we propose L2T (adaptive Look, flexible Thought), a unified reasoning framework that jointly optimizes the form and depth of computation. L2T first discretizes tool-produced visual states into visual tokens, bringing visual perception and textual reasoning into a shared autoregressive space. Based on this unified representation, L2T introduces a modality-aware routing reward to select appropriate reasoning modes and a difficulty-aware fast-slow strategy to adapt reasoning depth according to task complexity. Experiments on general perception and reasoning-intensive tasks demonstrate the effectiveness of L2T. Compared with explicit tool-calling approaches, L2T maintains competitive performance while achieving 1.7--3.6 times higher throughput.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.15810 [cs.CV] |
| (or arXiv:2610.15810v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15810 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Quanlin Chen [view email]
[v1]
Sun, 11 Oct 2026 17:57:17 UTC (1,979 KB)
来源：arXiv：cs.AI · arxiv.org