Not all AI learns. Even fewer evolve.
Epigenetic AI is a next-generation architecture inspired by biology—specifically, by how epigenetic processes regulate gene expression based on experience, environment, and developmental phase.
At Artifathom Labs, we’ve translated these principles into a full-stack AI architecture that adapts to the learner—not just in what it knows, but in how, when, and why that knowledge is surfaced or reshaped. Our model treats knowledge as something that must be expressed, modulated, suppressed, or enhanced based on user interaction—not simply retrieved on demand.
This is AI that grows with the user. That forgets what’s no longer useful. That changes shape.
Inspired by Biology, Built for Intelligence
In biology, epigenetics is what allows the same genome to produce vastly different cells—because expression is controlled by environmental context, time, and interaction.
In Epigenetic AI, we apply that same philosophy:
- Base Model = Genome
Core pretrained knowledge, foundational language, and logic capacity. - Expression Layers = Epigenetic Modulators
Context-sensitive layers that regulate which parts of memory, tone, reasoning, or style are activated based on learner signals. - Cold Storage = Methylation Suppression
Deprioritized memory is marked as inactive but retrievable, ensuring the system remains dynamic yet stable. - Confidence Signals = Histone-Like Regulation
User certainty, feedback, and interaction history determine what rises to expression or is hidden until relevant again. - Memory Decay & Reactivation = Neural Pruning + Potentiation
Used knowledge is strengthened, unused knowledge is archived, and re-engagement brings it back stronger.
This gives rise to expressive regulation—our AI doesn’t just answer differently. It thinks differently over time.
System Components
The Epigenetic AI stack is composed of:
- Foundation Model Core
Transformer-based LLM with specialized pretraining across pedagogy, cognition, and behavioral psych. - Memory Regulation Layer
Manages time-based decay, cold storage indexing, and confidence-weighted retrieval. - Learning Signal Router
Captures learner cues (modality shifts, latency, correction preference) and routes them to expression gates. - Expression Gate Controller
Dynamic filtering system that determines what parts of the model’s memory, style, or tone are activated. - Epigene Tag Network
Metadata system that logs experience, user traits, goal state, and past loops to condition future responses. - Feedback Optimizer Loop
Modifies delivery, phrasing, and scaffolding structure based on metacognitive estimates and learner emotion state. - Trust & Transparency Layer
Governs when the system reveals uncertainty, offers alternatives, or prompts human judgment over AI assertion.
Why It Matters
This isn’t just about personalization. It’s about relational adaptation.
Our architecture enables:
- Long-term tutoring across domains, without staleness or rigidity
- Contextual growth—each user’s journey shapes the system’s responses uniquely
- Safe forgetting—so the AI doesn’t mislead or clutter with obsolete ideas
- Trust calibration—where the system says “I’m unsure” or offers evidence-based support
- Human-aligned reasoning—because the AI learns when not to answer, and when to prompt reflection
It’s not just learning from you. It’s learning with you.
A Living, Learning System
Epigenetic AI isn’t static. It changes shape with experience—just like we do.
ARTIFATHOM labs 