利用Graph-PReFLexOR进行原位图推理和知识扩展。

In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR

January 14, 2025

作者: Markus J. Buehler

cs.AI

摘要

自动化科学发现的追求推动了从符号逻辑到现代人工智能的进步，开拓了推理和模式识别的新领域。变压器作为潜在系统发挥作用，其中每种可能的关系都保持潜在性，直到任务施加约束，类似于测量。然而，改进它们的采样需要的不仅仅是概率选择：解决方案必须符合特定的结构或规则，确保一致性和普遍原则的调用。我们提出了Graph-PReFLexOR（基于图的基于偏好的递归语言建模用于探索性推理优化），这是一个将图推理与符号抽象相结合，动态扩展领域知识的框架。受强化学习启发，Graph-PReFLexOR将推理定义为结构化映射，其中任务产生知识图、抽象模式，最终得出答案。受范畴论启发，它将概念编码为节点，将它们之间的关系编码为边，支持层次推理和通过同构表示进行自适应学习。演示包括假设生成、材料设计和创造性推理，例如发现神话概念如“薄弱之地”与材料科学之间的关系。我们提出了一个“知识园增长”策略，整合跨领域的见解，促进跨学科连接。使用30亿参数的Graph-PReFLexOR模型的结果显示出卓越的推理深度和适应性，突显了透明、多学科的人工智能驱动发现的潜力。它为通用自主推理解决方案奠定了基础。

English

The pursuit of automated scientific discovery has fueled progress from symbolic logic to modern AI, forging new frontiers in reasoning and pattern recognition. Transformers function as potential systems, where every possible relationship remains latent potentiality until tasks impose constraints, akin to measurement. Yet, refining their sampling requires more than probabilistic selection: solutions must conform to specific structures or rules, ensuring consistency and the invocation of general principles. We present Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning), a framework that combines graph reasoning with symbolic abstraction to dynamically expand domain knowledge. Inspired by reinforcement learning, Graph-PReFLexOR defines reasoning as a structured mapping, where tasks yield knowledge graphs, abstract patterns, and ultimately, final answers. Inspired by category theory, it encodes concepts as nodes and their relationships as edges, supporting hierarchical inference and adaptive learning through isomorphic representations. Demonstrations include hypothesis generation, materials design, and creative reasoning, such as discovering relationships between mythological concepts like 'thin places' with materials science. We propose a 'knowledge garden growth' strategy that integrates insights across domains, promoting interdisciplinary connections. Results with a 3-billion-parameter Graph-PReFLexOR model show superior reasoning depth and adaptability, underscoring the potential for transparent, multidisciplinary AI-driven discovery. It lays the groundwork for general autonomous reasoning solutions.