Static AI systems degrade over time. Knowledge becomes outdated. User preferences drift. Domain requirements evolve. New patterns emerge. And your AI, frozen at the moment of its training, becomes increasingly irrelevant.
This is the dirty secret of production AI systems. That LLM you deployed six months ago? It's already obsolete. It doesn't know about the library updates, the API changes, the new patterns your team has adopted. Every day, the gap between what it knows and what it needs to know widens.
The Evolution Engine is my answer to this problem. It's the subsystem responsible for continuous learning and adaptation in Mnemonic Hives—the mechanism by which hives become more capable, efficient, and personalized over time without explicit retraining.
The Learning Imperative
A truly autonomous system must learn continuously. But continuous learning is hard. Really hard. The naive approach—just fine-tune on new data—leads to catastrophic forgetting. The model gets better at new tasks while forgetting old ones.
I've established five principles that guide the Evolution Engine:
P1. Learn from Every Interaction. Every query, response, and feedback is a learning opportunity. Don't waste any of them.
P2. Preserve Stability. Learning must not cause catastrophic forgetting or dramatic behavior changes. Users should never wake up to find their AI assistant has become a different entity overnight.
P3. Respect Privacy. Learning must not leak sensitive information, especially in federated settings where multiple hives share knowledge.
P4. Bound Resources. Models must not grow unboundedly. Compression and pruning are essential to keep the system deployable.
P5. Maintain Quality. Learning should improve performance, not degrade it. Every update needs validation.
Learning Modes
Not all learning happens at the same pace. The Evolution Engine supports four distinct learning modes:
Immediate Learning (Milliseconds)
When a user corrects the system, that correction should take effect immediately. "No, my project uses TypeScript, not JavaScript" shouldn't require a retraining run—it should be incorporated into the agent's beliefs right now.
This is handled by the belief system I described in Part 3. Corrections update beliefs directly, and the memory system ensures those corrections persist.
Batch Learning (Minutes)
After accumulating enough interactions, the system processes them in batch. This might happen every 100 interactions or every few hours—configurable thresholds that balance learning speed against computational cost.
Batch learning extracts patterns from multiple interactions: which responses got positive feedback, which domains are being queried frequently, what new terminology is appearing.
Deep Learning (Hours)
Periodically—daily or weekly—the system runs deeper training cycles. This is where LoRA fine-tuning happens, where the actual model weights get updated based on accumulated evidence.
Deep learning is scheduled during low-usage periods and includes validation checks to ensure quality hasn't degraded.
Federated Learning (Days)
When multiple hives share knowledge while preserving privacy, that synchronization happens over longer timescales. Differential privacy techniques ensure no individual user's data leaks through the aggregated updates.
Knowledge Distillation: Learning from Teachers
One of the most powerful learning mechanisms is knowledge distillation—having smaller models learn from larger, more capable ones.
Here's how it works in the Evolution Engine:
When a query is complex enough to require a quality-tier model (Claude, GPT-4), the response from that teacher model becomes training data for the local student model. The student doesn't just learn the answer—it learns the reasoning patterns, the response structure, the way the teacher approached the problem.
The key insight from Hinton's original distillation work: use soft targets. Instead of training the student to match the teacher's final answer, train it to match the teacher's probability distribution over answers. The "dark knowledge" in those probabilities—which wrong answers are almost right, which are completely off—transfers to the student.
I've extended this to multi-teacher distillation. When multiple teachers are available, their outputs are weighted by:
- Quality tier: Claude Opus and GPT-4 get higher base weights than smaller models
- Domain expertise: A code-specialized model gets more weight for programming tasks
- Recent accuracy: Teachers that have been verified accurate recently get boosted
The result is student models that absorb knowledge from multiple teachers, inheriting the best capabilities of each.
LoRA: Efficient Fine-Tuning
Full model fine-tuning is expensive. For a 3B parameter model, you're looking at 12GB of optimizer state alone, plus the gradients, plus the model weights. It's impractical for continuous learning scenarios.
LoRA (Low-Rank Adaptation) solves this by adding small trainable matrices alongside the frozen base model. Instead of updating all 3 billion parameters, you train maybe 10 million—a 300x reduction in trainable parameters.
The key insight: most of the adaptation you need can be expressed as low-rank perturbations to the original weights. You're not fundamentally changing what the model knows; you're adjusting how it applies that knowledge to your specific context.
For the Evolution Engine, I'm using rank-16 LoRA adapters targeting attention and FFN layers. This gives enough capacity for meaningful adaptation while keeping memory requirements low enough to run on consumer hardware.
QLoRA: Even More Efficient
QLoRA combines LoRA with 4-bit quantization. The base model is loaded in 4-bit precision (reducing memory by 4x), while LoRA adapters train in full precision. You get the memory efficiency of quantization with the quality of full-precision fine-tuning.
This is what enables a 7B model to train on a laptop with 16GB of RAM—democratizing continuous learning for local deployments.
Domain-Specific Pruning
Not every specialist in a hive needs every capability. A code analysis specialist doesn't need expertise in legal documents. A summarization specialist doesn't need deep knowledge of molecular biology.
The Evolution Engine tracks domain activations over time. If a domain is never activated for a particular specialist, the neural pathways handling that domain can be pruned.
This is structured pruning—removing entire attention heads and FFN neurons, not just setting individual weights to zero. The result is genuinely smaller models that run faster, not just sparse models that still require the same computation.
The decision to prune is based on two metrics:
- Activation frequency: If a domain accounts for less than 1% of activations, it's a candidate for pruning
- Importance scoring: Using gradient-based importance estimation to verify the pruned pathways aren't critical for other domains
The ZipLLM Compression Pipeline
Models grow over time as they learn new domains and accumulate LoRA adapters. Without compression, they'd eventually become too large to deploy.
The ZipLLM pipeline I've designed addresses this through four stages:
Stage 1: Structured Pruning (50% reduction)
Identify low-importance attention heads and remove unused FFN neurons. This reduces a 3B model to approximately 1.5B parameters without significant quality loss.
Stage 2: Quantization (4x reduction)
Apply activation-aware weight quantization (AWQ) to convert to 4-bit precision. The key is using activation statistics to determine quantization scales—weights that interact with high-magnitude activations get finer quantization.
Stage 3: Distillation Recovery
Compression loses quality. We recover it by distilling from the pre-compression model (as teacher) to the compressed model (as student). This typically recovers 80-90% of the quality lost to compression.
Stage 4: Ollama Export
Convert to GGUF format with embedded LoRA weights for deployment via Ollama. The final artifact is a single file that can be deployed anywhere Ollama runs.
The result: a 3B model that started at 6GB ends up as a 500MB deployable artifact, with most of its capabilities preserved.
Federated Learning: Collective Improvement with Privacy
When multiple hives are deployed across different users or organizations, they can learn collectively without sharing raw data.
The protocol is straightforward:
- Each hive trains locally on its own data
- Gradients (not data) are clipped and noised for differential privacy
- A central server aggregates gradients using federated averaging
- The aggregated update is broadcast back to all hives
The privacy guarantee: no individual interaction can be reconstructed from the aggregated update, as long as the noise calibration satisfies the differential privacy bounds.
This enables scenarios like: a company deploys hives to 1000 employees. Each hive learns the company's terminology, conventions, and patterns. Through federated learning, this collective knowledge propagates to all hives—new employees benefit from the accumulated learning of everyone who came before.
Orchestrating the Evolution
The Evolution Engine orchestrates all these components:
// On every interaction
async fn on_interaction(interaction: HiveInteraction) {
// Extract learning event
let event = extractor.extract(interaction);
// Accumulate
accumulator.add(event);
// Check thresholds
if accumulator.should_update() {
trigger_update().await;
}
}
// Periodic update cycle
async fn trigger_update() {
let batch = accumulator.prepare_batch();
// Train
let metrics = trainer.train_batch(batch).await;
// Maybe prune
if update_count % prune_interval == 0 {
trigger_pruning(batch.domain_stats).await;
}
// Maybe compress
if should_compress() {
trigger_compression().await;
}
}The cycle is autonomous. No human intervention required. The hive learns, adapts, compresses, and improves continuously.
What This Enables
With the Evolution Engine, a Mnemonic Hive:
- Improves with use: Every interaction makes it better at helping you
- Stays current: New terminology, new patterns, new knowledge—all incorporated automatically
- Remains deployable: Compression ensures it doesn't outgrow its environment
- Benefits from collective learning: Optional federated learning pools knowledge across instances
- Preserves privacy: Differential privacy ensures sensitive data never leaks
This is what separates a cognitive agent from a static model. The model is frozen in time; the agent evolves.
What's Next
Evolution at the individual level is powerful. But the real magic happens when multiple agents evolve together, forming collective intelligence that exceeds what any individual could achieve.
In the next post, I'll explore how emergence happens in Mnemonic Hives—stigmergic coordination, consensus formation, and the conditions under which the whole genuinely exceeds the sum of its parts.
This is Part 4 of the Simplex Evolution Series. Part 3 covers belief systems and epistemic architecture. Part 5 explores collective intelligence and emergence.