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

2025-01-14

AI Summary

Current approaches to scientific discovery lack sufficient capacity for deep cross-domain knowledge association and dynamic reasoning. Method: This paper proposes a graph reasoning framework integrating category-theoretic isomorphic representation with preference-guided recursive modeling. It formalizes task-driven reasoning as structured mapping and enables principle-level alignment across heterogeneous domains (e.g., mythology and materials science) via a “Knowledge Garden Growth” strategy. The framework combines graph neural networks, symbolic abstraction, categorical semantic encoding, and reinforcement learning–inspired preference modeling to instantiate a 3-billion-parameter recursive language model. Contribution/Results: It achieves, for the first time, autonomous dynamic knowledge graph generation, abstract pattern extraction, and interpretable answer derivation. Empirical evaluation demonstrates significant improvements in reasoning depth and cross-domain adaptability across hypothesis generation, materials design, and creative reasoning tasks, thereby enabling multi-disciplinary, autonomous scientific discovery.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Interdisciplinary Knowledge
• Scientific Discovery
• Adaptive AI

Innovation

• Graph-PReFLexOR
• knowledge garden
• deep thinking