Johnny：结构化表示空间以增强机器抽象推理能力
Johnny: Structuring Representation Space to Enhance Machine Abstract Reasoning Ability
AI总结：
本文提出Johnny表示空间框架和Spin-Transformer架构，通过结构化表示空间与轻量级注意力优化，提升RPM抽象推理性能并降低计算开销。
摘要：
本文深入研究了增强人工智能抽象推理能力所面临的挑战，特别关注涉及复杂类人概念的Raven渐进矩阵（RPM）任务。首先，文章剖析了一个经验现实：传统的端到端RPM求解模型严重依赖选项池配置，并强调这种依赖性制约了模型的推理能力。为解决这一局限，本文提出了Johnny架构——一种新颖的基于表示空间的RPM求解框架。通过其表示提取模块和推理模块的协同运作，Johnny利用学习到的表示空间补充原始负选项配置，从而显著提升推理性能。此外，为增强模型捕捉局部特征之间位置关系的能力，本文引入了Spin-Transformer网络架构，并配套提出一种轻量级Straw Spin-Transformer变体，该变体通过参数共享和注意力机制优化降低了计算开销。实验评估表明，Johnny和Spin-Transformer在RPM任务上均取得了优越性能，为推进人工智能抽象推理能力提供了创新性方法。
英文摘要：
This paper thoroughly investigates the challenges of enhancing AI's abstract reasoning capabilities, with a particular focus on Raven's Progressive Matrices (RPM) tasks involving complex human-like concepts. Firstly, it dissects the empirical reality that traditional end-to-end RPM-solving models heavily rely on option pool configurations, highlighting that this dependency constrains the model's reasoning capabilities. To address this limitation, the paper proposes the Johnny architecture - a novel representation space-based framework for RPM-solving. Through the synergistic operation of its Representation Extraction Module and Reasoning Module, Johnny significantly enhances reasoning performance by supplementing primitive negative option configurations with a learned representation space. Furthermore, to strengthen the model's capacity for capturing positional relationships among local features, the paper introduces the Spin-Transformer network architecture, accompanied by a lightweight Straw Spin-Transformer variant that reduces computational overhead through parameter sharing and attention mechanism optimization. Experimental evaluations demonstrate that both Johnny and Spin-Transformer achieve superior performance on RPM tasks, offering innovative methodologies for advancing AI's abstract reasoning capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
