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

In Situ Graph Reasoning and Knowledge Expansion Using Graph-PReFLexOR: The work explains how to grow knowledge gardens, how to integrate symbolic and connectionist frameworks, and explains how 'thin places' inspired by Celtic mythology are related to bioluminescence.

Graph-PReFLexOR integrates graph-based reasoning, symbolic abstraction, and recursive reflection. By uniting these approaches, we tackle a significant challenge in AI: Enabling systems to reason, generalize, and adapt across disciplines while maintaining transparency and interpretability. Trained using RL methods, Graph-PReFLexOR utilizes deep isomorphic capacities in Transformers and unlocks the potential to drive transformative discoveries.

Key insights:

1⃣ Graph-PReFLexOR integrates symbolic and connectionist frameworks by embedding dynamic knowledge graphs and symbolic abstractions within a Transformer-based architecture. The connectionist foundation leverages the model’s ability to process and generate language, while symbolic reasoning is introduced through explicit graph construction and abstract pattern representation.

2⃣ During reasoning, the model constructs a graph that maps entities and their relationships, encodes these connections symbolically, and identifies key transformations. This process allows the model to combine the strengths of neural networks—pattern recognition and contextual fluency—with the interpretability and generalization power of symbolic reasoning.

3⃣ Our “knowledge garden" growth algorithm allows us to dynamically and iteratively expand knowledge. Starting from a simple prompt, the model constructs expanding knowledge graphs that capture relationships, abstractions, and reasoning steps. These graphs are then recursively refined and expanded through new prompts, either provided by humans or autonomously generated by the model. Over time, this process creates an interconnected, ever-growing repository of ideas and insights that span multiple disciplines. The knowledge garden framework enables the discovery of hidden relationships, fosters interdisciplinary exploration, and provides a structured and interpretable foundation for advancing scientific inquiry and creative problem-solving, all conducted autonomously or in collaboration with a human user.

4⃣ Potentiality of Transformers: We propose a quantum-inspired metaphor for knowledge processing, likening Transformers to systems in quantum superposition. Analogous to quantum state collapse, it refines possibilities into a single coherent output. This metaphor elegantly captures the balance between creativity and constraint in AI-driven reasoning.

5⃣ Fostering generalization: The model generalizes by identifying isomorphic structures in knowledge graphs, abstracting relational equivalences that enable it to transfer insights across domains while preserving underlying patterns.