Our Methodology

The Foundation: Deep Dive into the Scientific Literature:

Foundation in Scientific Evidence

• Automated, AI-Powered Analysis: You start by feeding it a collection of research papers – your own work, relevant publications, or even a broad corpus of literature. Our engine performs a deep, structured analysis of each paper, mimicking the critical evaluation of a seasoned peer reviewer, creating a template with specific questions such that each paper is distilled in the same format and the same structure. This analysis is guided by sophisticated, self-evolving templates rooted in statistical physics, aiming to extract the core principles underlying the given concepts. The template for concept definitions is iteratively updated from reading each publication, such that each concept in the end is represented as an embedding vector with subvectors that define its more refined details. This is similar to Matryoshka nested vectors.
• Visualizing the Essence: For each paper, the engine automatically generates a custom Mermaid diagram, where parts of the concepts are represented as a logical graph with the elements of Category Theory. This isn't a generic illustration; it's a visual representation of the specific physical concepts and their relationships as described in that paper. This allows to quickly grasp the core ideas and how they connect.
• Quantitative Assessment: Beyond qualitative analysis, the engine calculates a set of quantitative metrics, providing a "cognizant potential" score for each paper. This isn't about assigning a definitive "intelligence" rating; it's about identifying promising avenues for further exploration and self-correction within the strategy of reinforcement learning.

• Topic Modeling and Clustering: The engine uses advanced topic modeling (BERTopic, SciBert as a model and Sentence Transformers) to group papers based on semantic similarity (This is a first approximation, when concepts are extracted from text, next stage is to work directly with scientifically proven evidence, rather than rely on text). This reveals the hidden structure of the field, identifying distinct research areas and emerging trends. It's like creating a map of the intellectual landscape, where each scientific evidence is represented by a point, while the distances are various scores in hyperdimensional space.
• Hierarchical Relationships: We go beyond flat clusters. The engine uncovers hierarchical relationships between topics, showing how different research areas build upon each other or relate as sub-disciplines.
• Interactive Visualizations: Explore the research landscape through interactive visualizations:
  • t-SNE Plots: See how papers cluster together in a 2D space, revealing their conceptual proximity.
  • Intertopic Distance Maps: Understand the relationships between different research areas.
  • Topic Hierarchy Diagrams: Explore the hierarchical structure of the field.
  • Radar Charts: Compare the "cognitive potential" of different research clusters across various metrics.
• Similarity Heatmap: Showing similarity between different clusters.
• Cluster Overlap: Showing clusters overlap in 2D projection.

• Annotation and Debate: The engine isn't a black box. Researchers can annotate the AI's analysis, correcting errors, adding context, and engaging in constructive debate about interpretations. This is the feedback loop which is the basis for understanding user intentions. This is crucial for refining both the AI's understanding also getting into the community's collective knowledge.
• Living Templates: The analysis template itself is dynamic. It's constantly evolving based on user feedback, AI-powered suggestions, and new discoveries in the field. This ensures that scores of each concept are correctly mapped.
• Knowledge Synthesis: The engine doesn't just analyze individual papers; it synthesizes knowledge across the entire corpus, creating dynamic literature reviews, identifying open questions, and even suggesting new research hypotheses. In contrast to LLM random hallucinations, this process is directed by scores and metrics obtained from cluster analysis and the driving engine is Active Inference based on probabilities and minimization of uncertainty.

• Formal Representation: The analysis template incorporates concepts from Category Theory (CT) to provide a formal, abstract framework for representing material intelligence. This includes:
  • Objects: Representing physical entities or concepts (e.g., energy states, memory states, material components).
  • Morphisms: Representing relationships or transformations between objects (e.g., energy transduction, state transitions, interactions).
  • Functors: Representing mappings between different categories (e.g., mapping microscopic interactions to macroscopic behaviors).
  • Adjunctions: Representing relationships between different levels of description (e.g., local rules and global order).
• Limits and Colimits: Use limits and colimits to reconstruct the universal model, representing stability and aggregation, respectively.
• Universal Model Construction: The CT framework enables the identification of universal properties and the construction of a universal model of material intelligence. This involves:
  • Identifying common substructures (objects and morphisms) across different material systems.
  • Defining functors that map specific systems to the universal model.
  • Using CT concepts (limits, colimits) to identify stable states and emergent behaviors.
  • Using Graph Isomorphism Networks (GINs), part of Graph Neural Networks, to check isomorphisms.
• Transfer Learning: The CT framework facilitates transfer learning. Knowledge gained from analyzing one system can be transferred to others, even with different physical implementations, by identifying isomorphic structures and mapping between them.

• Uncertainty Minimization: The platform is designed to minimize uncertainty and maximize understanding through:
  • Iterative template refinement.
  • AI-powered suggestions for new research directions.
  • Targeted exploration of the knowledge graph based on user intentions.
• User Intent Modeling: The system captures user intentions through:
  • Explicit queries and search terms.
  • Selection of papers and concepts of interest.
  • Annotations and feedback.
  • Stated research goals.
• Recommendation Engine: A recommendation engine, guided by active inference, suggests:
  • Relevant papers and clusters.
  • Potential collaborators.
  • Missing information or gaps in knowledge.
  • Alternative research pathways.
• Hypothesis Generation: The platform combines information from existing literature to generate novel research hypotheses.
• User-Driven Exploration: The researcher is an active agent within the ecosystem. Questions, feedback, and annotations guide the AI's analysis and shape the evolution of the platform.

• Skill-Based Matching (in contrast to title-based): The engine analyzes user profiles, including their research interests, publications, annotations, and contributions to the platform. This information is used to create automatically a "skill profile" for each user.
• Project-Based Groups: When a new research project or challenge is initiated (either by a user or suggested by the AI), the engine automatically identifies potential collaborators based on:
  • Complementary Skills: The system seeks users with skills and expertise that complement the project's needs.
  • Shared Interests: The system identifies users who have expressed interest in related topics or worked on similar papers.
  • "Research Neighborhoods": The system leverages the concept of "research neighborhoods" to connect users who are exploring similar areas of the knowledge landscape.
• Dynamic Reconfiguration: Groups are not static. As a project evolves, the engine can suggest adding new members with relevant expertise or removing members whose skills are no longer needed or users becoming inactive. This ensures that groups remain agile and responsive to changing fields.
• Swarm Intelligence Integration: The platform facilitates the formation of dynamic groups inspired by swarm intelligence principles. Users with complementary skills can be algorithmically grouped to tackle specific challenges, mimicking the collaborative problem-solving seen in natural swarms.

Key Components

Project Development