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思维链模式:基于自适应认知模式的推理

Chain of Mindset: Reasoning with Adaptive Cognitive Modes

February 10, 2026
作者: Tianyi Jiang, Arctanx An, Hengyi Feng, Naixin Zhai, Haodong Li, Xiaomin Yu, Jiahui Liu, Hanwen Du, Shuo Zhang, Zhi Yang, Jie Huang, Yuhua Li, Yongxin Ni, Huacan Wang, Ronghao Chen
cs.AI

摘要

人类解决问题从不固守单一思维模式——即特定的认知处理方式。面对具体任务时,我们并非依赖单一思维模式,而是在求解过程中融合多种思维模式。然而现有的大语言模型推理方法普遍陷入一个误区:在所有步骤中套用相同的固定思维模式,忽视了解决同一问题不同阶段需要截然不同的思维模式。这种单一思维假设阻碍了模型实现更高层次的智能。为突破这一局限,我们提出思维链框架——一种无需训练的主体性框架,可实现步骤级自适应思维编排。该框架将推理分解为四种功能异构的思维模式:空间思维、聚合思维、发散思维与算法思维。元智能体根据动态演进的推理状态实时选择最优思维模式,双向上下文门控机制则通过过滤跨模块信息流来保持效能与效率的平衡。在涵盖数学、代码生成、科学问答和空间推理的六大挑战性基准测试中,思维链框架均取得最先进性能:在Qwen3-VL-32B-Instruct和Gemini-2.0-Flash模型上整体准确率分别超越最强基线4.96%和4.72%,同时兼顾推理效率。代码已开源於https://github.com/QuantaAlpha/chain-of-mindset。
English
Human problem-solving is never the repetition of a single mindset, by which we mean a distinct mode of cognitive processing. When tackling a specific task, we do not rely on a single mindset; instead, we integrate multiple mindsets within the single solution process. However, existing LLM reasoning methods fall into a common trap: they apply the same fixed mindset across all steps, overlooking that different stages of solving the same problem require fundamentally different mindsets. This single-minded assumption prevents models from reaching the next level of intelligence. To address this limitation, we propose Chain of Mindset (CoM), a training-free agentic framework that enables step-level adaptive mindset orchestration. CoM decomposes reasoning into four functionally heterogeneous mindsets: Spatial, Convergent, Divergent, and Algorithmic. A Meta-Agent dynamically selects the optimal mindset based on the evolving reasoning state, while a bidirectional Context Gate filters cross-module information flow to maintain effectiveness and efficiency. Experiments across six challenging benchmarks spanning mathematics, code generation, scientific QA, and spatial reasoning demonstrate that CoM achieves state-of-the-art performance, outperforming the strongest baseline by 4.96\% and 4.72\% in overall accuracy on Qwen3-VL-32B-Instruct and Gemini-2.0-Flash, while balancing reasoning efficiency. Our code is publicly available at https://github.com/QuantaAlpha/chain-of-mindset{https://github.com/QuantaAlpha/chain-of-mindset}.
PDF621February 12, 2026