提出递归推理机，让神经推理模型更像经典逻辑引擎。
Recursive Inference Machines for Neural Reasoning
- 用递归机制模拟传统推理引擎，增强神经推理过程
- 在ARC和数独难题上表现优于原有模型
- 适用于复杂推理与表格分类任务，效果更优
神经推理模型如小规模递归模型（TRMs）通过结合神经主干与专用推理机制解决复杂问题。这类推理机制是随机推理系统的核心，通过将推理规则应用于随机模型来回答复杂查询。本文提出递归推理机（RIMs），一种显式融合递归推理机制的神经推理框架，灵感来自经典推理引擎。我们证明TRMs可视为RIMs的特例，并通过引入重加权组件扩展其能力，在多个挑战性推理基准（包括ARC-AGI-1、ARC-AGI-2和Sudoku Extreme）上取得更好性能。此外，RIMs还可用于其他任务，如表格数据分类，优于TabPFNs。
原文摘要 · Abstract (English)
Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a central component of stochastic reasoning systems, where inference rules are applied to a stochastic model to derive answers to complex queries. In this work, we bridge these two paradigms by introducing Recursive Inference Machines (RIMs), a neural reasoning framework that explicitly incorporates recursive inference mechanisms inspired by classical inference engines. We show that TRMs can be expressed as an instance of RIMs, allowing us to extend them through a reweighting component, yielding better performance on challenging reasoning benchmarks, including ARC-AGI-1, ARC-AGI-2, and Sudoku Extreme. Furthermore, we show that RIMs can be used to improve reasoning on other tasks, such as the classification of tabular data, outperforming TabPFNs.
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