原子思维:使用原子步骤推理的多模式缓慢思维
IEEE transactions on pattern analysis and machine intelligence
|January 13, 2026
概括
本研究介绍了AtomThink,这是多式联络大型语言模型 (MLLMs) 的新框架,它适应了推理的复杂性. AtomThink提高了复杂任务的性能,同时避免了对更简单任务的过度思考.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 中的多式推理是一个复杂的挑战.
- 现有的方法经常使用严格的模板或非结构化的方法,导致效率低下.
- 需要适应性推理策略来处理不同问题的复杂性.
研究的目的:
- 开发一个新的框架,AtomThink,用于LLMs的自适应多式联络推理.
- 为灵活推理引入一个自我结构的思维链 (SCoT) 范式.
- 在多式联运任务中提高准确性和效率.
主要方法:
- 提出了自我结构的思维链 (SCoT) 范式,使用最小的语义原子步骤.
- 设计了AtomThink框架与数据引擎,监督微调,政策导向推断和原子能力指标.
- 利用序列化的推理数据进行监督微调.
主要成果:
- 在MathVista和MathVerse数据集上实现了超过10%的平均准确度增长.
- 与最先进的结构化思维链 (CoT) 方法相比,表现出显著的改进.
- 数据利用率提高了5倍,推断效率提高了85.3%.
结论:
- AtomThink 能够在多式大型语言模型 (MLLMs) 中实现自适应推理.
- 在SCoT范式提供灵活和高效的推理结构.
- 原子思维显著提高了多式联网人工智能系统的性能和效率.
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