Funny-Valen-Tine：规划解分布增强机器抽象推理能力
Funny-Valen-Tine: Planning Solution Distribution Enhances Machine Abstract Reasoning Ability
AI总结：
本文提出Funny-Valen-Tine方法，通过显式规划解分布（用高斯混合模型替代对抗训练）增强机器在RPM和Bongard-Logo任务上的抽象推理能力，实验证明其有效且可解释。
摘要：
视觉抽象推理是图像处理的核心。我们提出了Valen，一个统一的概率高亮基线，在RPM（递推）和Bongard-Logo（聚类）任务上均表现出色。分析其内部机制，我们发现求解器隐式地将每个任务视为一个分布，其中主要样本符合分布而辅助样本不符合；因此学习目标由两组样本共同塑造，而非仅由正确解决定。为弥补这一差距，我们首先引入Tine，一个对抗性适配器，将Valen推向正确解密度，但对抗训练不稳定。因此，我们将其替换为Funny，一种快速高斯混合模型，无需对抗博弈即可直接估计正确解密度，并将同一范式扩展到SBR以进行递进模式规划。大量实验表明，显式的分布规划是更强、可解释的抽象推理的关键。代码可在以下网址获取：https://github.com/Yuanbeiming/Funny-Valen-Tine-Planning-Solution-Distribution-Enhances-Machine-Abstract-Reasoning-Ability
英文摘要：
Visual abstract reasoning is core to image processing. We present Valen, a unified probability-highlighting baseline that excels on both RPM (progression) and Bongard-Logo (clustering) tasks. Analysing its internals, we find solvers implicitly treat each task as a distribution where primary samples fit and auxiliaries do not; hence the learning target is jointly shaped by both sets, not by correct solutions alone. To close the gap we first introduce Tine, an adversarial adapter that nudges Valen toward correct-solution density, but adversarial training is unstable. We therefore replace it with Funny, a fast Gaussian-mixture model that directly estimates the correct-solution density without adversarial games, and extend the same paradigm to SBR for progressive-pattern planning. Extensive experiments show explicit distribution planning is the key to stronger, interpretable abstract reasoning. Codes are available in: https://github.com/Yuanbeiming/Funny-Valen-Tine-Planning-Solution-Distribution-Enhances-Machine-Abstract-Reasoning-Ability
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。
