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Galaxia: Hypergraph Language Model (HLM)

Non-Generative. In-Memory. Explainable by Design.

Technology
Pioneering Research on Cognitive Graphs
  • Galaxia originated as a deep-tech R&D initiative focused on advancing the foundations of language understanding and reasoning through cognitive graph architectures.
  • Our mission is to develop efficient, safe, and unbiased language processing technology - systems that can reason transparently, scale efficiently, and remain fully explainable by design.
  • We believe that true artificial intelligence should not only generate answers but also understand, remember, and justify them.
  • This philosophy underpins the development of Galaxia, our core Hypergraph Language Model.
Smabbler graph - excerpt
1. Overview
  • Galaxia is a Hypergraph Language Model (HLM) - a symbolic, non-generative system for reasoning and knowledge representation.
  • It transforms unstructured text into semantic hypergraphs that encode meaning, relationships, and context in a transparent and machine-interpretable form.
  • Unlike large language models (LLMs), Galaxia does not generate predictions or approximate meaning through statistical correlation.
  • Instead, it performs deterministic analysis, classification, and reasoning over structured relationships derived from natural language input.
  • The model operates entirely in memory, scales linearly with available RAM, and requires no GPU acceleration, embeddings, or neural retraining.
2. Core Concept
  • At its foundation, Galaxia combines principles of graph theory, symbolic logic, and linguistic parsing into a unified reasoning architecture.
  • Each data source is converted into a semantic hypergraph - a data structure where nodes represent concepts or entities, and hyperedges represent multi-way relationships among them.
  • Unlike traditional “triple-based” ontologies (subject–predicate–object), Galaxia supports n-ary and recursive relationships, allowing the representation of complex, compositional meaning.
  • This hypergraph structure forms a persistent cognitive memory layer:
  • Knowledge is stored as interpretable structures rather than dense vector embeddings.
  • Reasoning is achieved through graph traversal and composition, not probabilistic prediction.
  • Provenance and explainability are inherent at every inference step.
3. Architecture
  • 1. Semantic Parser
  • Galaxia includes an embedded NLP engine that performs detailed linguistic and syntactic analysis - including grammar decomposition, part-of-speech tagging, sentence classification, and dependency resolution.
  • Beyond conventional parsing, Galaxia employs genetic algorithms to reason about the functional role of words and concepts within sentences, dynamically adapting to new or previously unseen terminology.
  • This mechanism allows the system to infer the grammatical and semantic role of unknown or context-dependent elements - for example, technical jargon, emerging scientific terms, or abbreviations - without prior exposure or retraining.
  • The result is a parser capable of semantic generalization and self-adaptation, extending language understanding beyond static vocabularies or pretrained embeddings.
  • This linguistic layer ensures that every concept and relation in the hypergraph is grounded in explicit grammatical and semantic structure, not statistical approximation.
  • The parser outputs an intermediate, logic-oriented representation optimized for graph construction, enabling Galaxia to capture both linguistic meaning and structural context before reasoning begins.
  • 2. Hypergraph Constructor
  • Transforms the parser’s intermediate linguistic representation into a multi-dimensional semantic hypergraph, where nodes represent entities, concepts, or abstract relations, and hyperedges capture n-ary, multi-level, and recursive dependencies.
  • The constructor supports compositional relation building, meaning new edges and nodes can be dynamically synthesized from existing ones - allowing Galaxia to represent complex meaning structures and unseen relationships across domains.
  • It also performs context normalization and semantic disambiguation, ensuring consistent entity alignment and coherent graph expansion at scale.
  • This process results in a transparent, self-consistent graph layer that serves as Galaxia’s reasoning substrate, ready for in-memory inference.
  • 3. In-Memory Inference Engine
  • Performs symbolic reasoning, retrieval, and compositional inference directly in RAM.
  • The engine integrates genetic-algorithm–based optimization and a neural-inspired propagation layer to explore, refine, and converge on the most coherent reasoning paths within the hypergraph.
  • This hybrid mechanism combines the adaptability of heuristic search with the transparency of symbolic reasoning, maintaining full traceability while enhancing discovery and inference performance.
  • The system scales linearly with memory availability and parallelizes across CPU cores, allowing high-throughput analysis of large data volumes without GPU acceleration.
  • All computation occurs locally in a transparent and interpretable graph space - eliminating the need for external databases or embedding-based similarity search.
4. System of Cognitive Truth
  • Galaxia’s architecture forms a system of cognitive truth - a foundation where every inference, relation, and retrieval is traceable, explainable, and grounded in structured meaning.
  • Unlike black-box neural systems that approximate understanding through statistical probability, Galaxia’s hypergraph-based reasoning ensures verifiable cognitive consistency across data, context, and inference.
  • This design makes Galaxia an ideal backbone for next-generation, trustworthy AI in knowledge-intensive sectors - where transparency, reliability, and semantic coherence define true intelligence.
5. Key Properties
  • Galaxia’s architecture introduces a set of foundational properties that differentiate it from both large language models and traditional knowledge graph systems.
  • Non-Generative Reasoning
  • Galaxia is a non-generative system - it does not predict or hallucinate.
  • Instead, it derives insights by analyzing existing graph structures and their interrelations.
  • All outputs are verifiable, deterministic, and grounded in explicit data.
  • Explainability by Design
  • Every reasoning step, traversal, and relation is human-readable and traceable.
  • Inference paths can be fully reconstructed, providing a semantic audit trail for every result.
  • Compositional Semantics
  • Knowledge evolves through structured composition.
  • Galaxia can synthesize new nodes and relations from existing structures, enabling domain adaptation and reasoning across unseen contexts - it doesn’t require training or retraining,
  • Symbolic–Genetic–Neural Hybrid Reasoning
  • Galaxia integrates symbolic logic, genetic optimization, and neural-inspired propagation into a unified reasoning framework.
  • At its core, Galaxia performs symbolic inference over structured knowledge graphs, ensuring every reasoning path remains explicit, interpretable, and verifiable.
  • This symbolic foundation is augmented by a genetic algorithm layer that explores and refines reasoning paths through evolutionary operators - selection, mutation, and recombination - enabling the system to optimize coherence and adaptability across large, complex knowledge spaces.
  • A neural-inspired propagation layer supports distributed activation and contextual generalization within the hypergraph, enhancing sensitivity to semantic patterns without sacrificing explainability.
  • The combination produces a reasoning engine that is:
  • Deterministic at its core (symbolic logic),
  • Adaptive in exploration (genetic algorithms)
  • Parallel and context-aware in inference (neural propagation).
  • This triadic architecture enables Galaxia to reason, adapt, and scale dynamically - maintaining transparency, reproducibility, and cognitive consistency across all inference processes.
  • In-Memory Computation and Context Persistence
  • All reasoning occurs in-memory on CPUs, eliminating the need for GPUs or external databases.
  • Galaxia can process over 60,000 pages (≈200 million characters) in a single pass while maintaining full contextual continuity - a 20× increase over leading LLM capacities.
  • Scalability and Federated Extensibility
  • The hypergraph structure supports modular expansion.
  • Independent knowledge bases can be connected or “sharded” into a federated, multi-graph ecosystem (the Graph Swarm architecture) while maintaining local autonomy and provenance integrity.
6. Data and Knowledge Flow
  • Each stage is deterministic and interpretable - no opaque statistical weights or hidden layers are involved.
  • Input: Structured / unstructured text (documents, reports, research papers, tables).
  • Parsing: Extraction of semantic units and relations.
  • Graph Construction: Transformation into hypergraph format.
  • Reasoning: Traversal, retrieval, compositional inference, and adaptive optimization via genetic and neural-inspired mechanisms for dynamic reasoning path selection.
  • Output: Structured, explainable results or graph-level insights.
7. Comparison to Traditional Approaches
  • (Flip Phone to View)
  • Positioning Versus Current Technologies and Research
Feature/Aspect
Galaxia (HLM)
Mainstream LLM / RAG Pipelines
Traditional Knowledge Graph Systems
Reasoning Model
Symbolic, deterministic, explainable
Generative, probabilistic, statistical
Mostly rule-based reasoning
Knowledge Representation
Semantic hypergraphs (n-ary, recursive)
Dense vector embeddings
Triple-based ontologies (subject–predicate–object)
Explainability and Transparency
Native, fully traceable and audit-friendly
Post-hoc, often limited
Varies, often incomplete
Memory Model
Persistent, in-memory cognitive layer with full context retention
Short-term, limited context window; resets per query
Persistent but static; no adaptive reasoning
Computational Infrastructure
CPU-based, in-memory, linear scaling wit RAM
GPU-heavy, multi-component pipeline (LLM + embeddings + vector DB)
Database-dependent, scale limited by I/O and schema rigidity
Adaptability
Semantic generalization without retraining, supports unseen data
Requires fine-tuning or retraining for new data
Limited adaptability without redesign (requires manual ontology updates)
Scalability
Modular federated “Graph Swarm”
Tightly coupled, high infrastructure overhead
Typically siloed or partially federated
Reasoning Complexity
Complex multi-hop compositional logic & inference
Shallow retrieval or token-level coherence
Simple triple traversal or rule matching
Reliability
Deterministic, no hallucinations, consistent reasoning paths
Probabilistic outputs with hallucination risk
Probabilistic outputs with hallucination risk
8. Technical Advantages
  • Unified reasoning layer: replaces multi-component RAG pipelines.
  • Low computational footprint: operates efficiently on standard CPUs.
  • High transparency: complete traceability of logic and provenance.
  • Modular federation: supports distributed, multi-domain knowledge graphs.
  • Explainable retrieval: connects natural language queries with structured reasoning paths.
9. Summary
  • Galaxia represents a shift from generative approximation toward explainable, symbolic computation at scale.
  • By combining in-memory hypergraph reasoning with compositional structure, it enables AI systems that are transparent, efficient, and inherently reliable.
  • It serves as a foundation layer for explainable intelligence - capable of augmenting LLMs or operating independently as a deterministic reasoning system.
  • This description outlines Galaxia’s conceptual architecture for informational purposes. Specific algorithms and implementations remain proprietary.
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