Intelligence Graphs
Intelligence Graphs represent a family of graph-centric methodologies that extend graph applications in AI beyond simple fact retrieval found in Graph RAG systems. Intelligence Graphs offers integration of not only raw knowledge, but also other discrete (and actively evolving) dimensions, such as personality, memory, information evolution, reasoning, and any other information-set that can be mapped to a graph structure.
By structuring knowledge, memory, and reasoning patterns as interconnected graph relationships, we are able to create AI systems that are more personalized, contextually aware, and capable of complex reasoning. This approach offers a lightweight yet powerful alternative to simply scaling neural networks, delivering richer information understanding with greater efficiency and transparency while addressing key limitations to large neural networks.
It is our belief that this approaches can provide a foundation for AGI development. Personalisation across multiple domains and dimensions—knowledge, memory, reasoning patterns, and contextual understanding—is not merely a feature but a cornerstone requirement for artificial general intelligence. Intelligence Graph methodologies provide structural frameworks to enable these multi-dimensional capailities.
Research Topics
- Computational Efficiency & Cost-Benefit Analysis: Compare resource requirements, performance, and ROI between Intel Graphs and large neural models.
- Foundational Architecture for Intelligence Graphs: Define the core components and relationships between different graph types and how they interconnect.
- Multi-Agent Coordination through Game Theory in Graph: Investigate how games in Graphs can enable more synergistic collaboration and optimal growth paths between actors.
- Measuring AI Learning as a Graph: Explore how knowledge evolves and accumulates within these graphs through interactions and feedback to measure AI capabilities over time.
- Reinforcement Learning for Optimised Graph Traversal: Research how existing LLMs can be used to train more effective graphs traversal.
- Multi-Dimensional Personalisation Framework: Establish optimal approaches for personalisation across knowledge, reasoning, and contextual dimensions.
- Context Windows vs Memory Graph: An accuracy and cost comparison between long-term context handling capabilities compared to multi-dimensional memory-knowledge graphs.
For more on our neural graph research, see Multi-Layered Graph Neural Networks.
Detailed Research Topics
1. Computational Efficiency & Cost-Benefit Analysis
Research Question: What are the efficiency gains, cost savings, and performance trade-offs, and tipping point of a Graph compared to pure neural approaches?
Abstract: This research provides a comprehensive cost-benefit analysis comparing Intelligence Graph approaches to large neural network models. We will investigate computational resource requirements, response latency, development and maintenance costs, and capability trade-offs. The work aims to establish quantitative metrics for ROI when implementing graph-based approaches versus purely neural solutions, identifying specific use cases where the economic and performance benefits of Intelligence Graphs are most significant.
Measurement Approaches:
- Computational resource usage (memory, processing, storage) across equivalent tasks
- Response time and throughput comparisons
- Development, training, and maintenance time/cost assessments
- Performance analysis across different task categories
- Transparency and explainability benefits quantified in business terms
- Cost-per-capability calculations and ROI projections
- Resource scaling considerarions
2. Foundational Architecture for Intelligence Graphs
Research Question: How can different graph types (knowledge, memory/context, reasoning, games, and learning) be effectively interconnected within a unified framework?
Abstract: This research explores the optimal architecture for integrating multiple graph types into a coherent Intelligence Graph system. We will investigate node/edge type definitions, cross-graph relationships, and information flow patterns between different graph components. The research will establish reference models for how these graphs complement each other and identify patterns for when different graph types should be emphasised over others.
Measurement Approaches:
- Structural efficiency metrics (edge-to-node ratios, clustering coefficients)
- Knowledge consistency measures across graph types
- Architecture complexity vs. capability enhancement measurements
3. Optimising Multi-Agent Outcomes by Embedding Games (Game Theory) in Graphs
Research Question: Can embedding games into graph structures enable more optimal coordination of action and use of resources between agents?
Abstract: This research examines how Coordination Graphs within the Intelligence Graph framework can facilitate effective multi-agent systems. We will investigate shared knowledge representation, task allocation mechanisms, and collaborative reasoning patterns. The work aims to develop models for agent coordination that allow optimal usage of intelligence resources towards shared goals.
Measurement Approaches:
- Coordination efficiency across varied tasks
- Context preservation during collaborative processes
- Task completion improvements vs. single-agent approaches
- Communication overhead metrics
4. Learning Dynamics in Intelligence Graphs
Research Question: How do Intelligence Graphs accumulate, evolve, and refine knowledge over time for continuous AI learning?
Abstract: This research unifies the study of temporal dynamics and information evolution within Intelligence Graphs. We will examine how knowledge structures grow and adapt through ongoing interaction, how confidence levels change over time, and how the system can effectively learn from its experiences. The work aims to develop a framework to measure imporvements in AI through interaction, tracking the evolution of their knowledge and reasoning capabilities while maintaining coherence across temporally distant interactions.
Measurement Approaches:
- Response Improvement Rate: How much better the system gets at answering questions over time (e.g., “correctly answered 60% of questions on day 1, up to 85% by day 30”)
- Memory Retention Quality: How accurately the system recalls past information and builds on previous conversations
- Contradiction Handling: How the system identifies and resolves conflicting information as it learns more
- Knowledge Density Growth: How much useful information fits into the graph compared to its size (measuring efficiency, not just volume)
5. Reinforcement Learning for Optimized Graph Inference
Research Question: How can reinforcement learning be applied to train LLMs and reasoning models for more effective Intelligence Graph navigation and utilization?
Abstract: This research explores the application of reinforcement learning techniques to optimize how AI systems traverse graphs for more valubable inference. We will investigate how existing LLMs and reasoning models can be used to train models to navigate complex graph structures more efficiently, identify the most relevant paths through a graph for a given task. We will develop specialised models that are optimized for graph operations, potentially creating AI systems that can leverage graph-structured knowledge more effectively than general-purpose models.
Measurement Approaches:
- Finding Time: How quickly the system finds useful information compared to random searching
- Node Selection: How relevant and accurate the nodes are when using the trained vs. untrained navigation
- Path Efficiency: Whether the system takes shorter, more direct routes to information
- Needle in a Haystack: How well the system can pick node-relationships for implied non-explicit connections.
6. Multi-Dimensional Personalisation
Research Question: What are the optimal dimensions and approaches for personalisation within an Intelligence Graph?
Abstract: This research explores the multiple dimensions along which Intelligence Graphs can be personalised to individual users, organisations, or contexts. We will investigate knowledge personalisation, interaction style adaptation, reasoning pattern customisation, and context-sensitivity optimisation. The work aims to develop a framework for effective multi-dimensional personalisation that enhances user experience.
Measurement Approaches:
- User Satisfaction: How happy users are with the personalised experience (simple rating scales)
- Adaptation Speed: How quickly the system learns new user preferences and adjusts behavior
- Time to Complete Tasks: Whether personalisation makes users faster at getting things done
- Recommendation Accuracy: How often the system suggests information or actions that users actually find useful
7. Context Windows vs Memory Graph
Research Question: How do memory-knowledge graphs compare to extended context windows in terms of accuracy, cost-efficiency, and practicality?
Abstract: A comparison between two approaches to extending AI context: extending the fixed context window of language models versus implementing memory-knowledge graphs. We will investigate performance differences across various tasks, computational resource requirements, information retrieval accuracy, and cost implications of both approaches. The work aims to establish clear guidelines for when graph-based memory structures offer significant advantages over simply extending context windows.
Measurement Approaches:
- Accuracy over Time: How well each approach remembers details from early in a conversation or project compared to recent information
- Cost per Information: How much it costs to store and retrieve information using each method
- Response Speed: How quickly users get answers from each approach when asking about past information
- System Load: How much computer resources each method uses as the amount of stored information grows
Use Case for Comparison: Building a large software project where an AI assistant needs to remember code decisions, bug fixes, design patterns, and team discussions over the length of the project. We’ll measure computational cost, completion time and accuracy to a set of predefined guidelines.