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