How Artemis City Learns to Forget: Validated Results from Adaptive Hebbian Agents

New simulation data proves that controlled forgetting prevents AI memory poisoning.

The smartest AI systems don't just learn—they know when to forget.

We've just completed a rigorous simulation study validating one of Artemis City's core architectural claims: Adaptive Hebbian agents with memory decay outperform static-memory systems in changing environments.

Here's what we found.

The Problem: Memory Poisoning

Traditional AI memory systems suffer from a fatal flaw: they remember everything.

Sounds good, right? More data = better predictions?

Wrong.

When the world changes—and it always does—yesterday's knowledge becomes today's noise. Markets shift. Regulations update. Customer behavior evolves.

A system trained on 2023 patterns will actively fight against 2024 reality. The historical data doesn't help—it interferes.

We call this catastrophic memory interference.

The Experiment: Three Phases of Chaos

To test Artemis City's adaptive architecture, we designed a brutal scenario: concept drift simulation.

We generated 1,000 sequential data points across three distinct phases, each governed by a completely different mathematical relationship:

Phase	Steps	What Changed
Phase 1	0–333	Linear: `y = 2x + 3z`
Phase 2	334–666	Quadratic: `y = -2x²`
Phase 3	667–1000	Trigonometric: `y = 5·sin(x)`

At step 334, the rules completely changed. At step 667, they changed again.

This simulates real-world scenarios:

A market regime shift (bull → bear → volatile)
A regulatory change (old rules → new compliance requirements)
A product launch (pre-launch behavior → post-launch adoption patterns)

Three Architectures Tested

We pitted three memory strategies against each other:

1. Traditional k-NN (Infinite Memory)

The standard RAG/vector-search approach. Keep everything. Query everything.

Result: Catastrophic interference at every phase boundary.

When the rules shifted from Linear to Quadratic, the system kept pulling Linear-era data. The historical "knowledge" actively degraded predictions.

2. Standard Hebbian (No Decay)

Our baseline agentic architecture. Five neural network agents compete for tasks. Winners get reinforced.

Result: Better than k-NN, but still struggled at transitions.

The weight system locked onto Phase 1 winners, making it slow to adapt when Phase 2 demanded different agents.

3. Adaptive Hebbian (With Decay)

The same five agents, but with a critical addition: adaptive decay that gradually diminishes the influence of older weight updates.

The decay mechanism is continuous and multiplicative—frequently reinforced associations resist decay, while stale patterns fade naturally. Crucially, we do not publish the specific decay parameters or formulas, as they require domain-informed tuning and are core to Artemis City's competitive advantage.

Result: Consistent performance across all three phases.

The Numbers

Here's what the simulation revealed:

Recovery Time After Concept Drift:

Model	Steps to Recover
k-NN	50–100 steps
Standard Hebbian	40–80 steps
Adaptive Hebbian	20–30 steps

The Adaptive model recovered 2–3x faster than static-memory alternatives.

Steady-State Performance:

Model	Drift Resilience
k-NN	Poor (cumulative degradation)
Standard Hebbian	Poor (phase lock-in)
Adaptive Hebbian	Excellent

Only the Adaptive model maintained consistent accuracy across all three phases.

Why Decay Works: The Sliding Window Effect

Adaptive decay creates an implicit sliding window of relevance—old associations that aren't continuously reinforced gradually lose their influence on current decisions. The system doesn't need to explicitly detect drift—it naturally "forgets" obsolete patterns.

But here's the key: it's not random forgetting.

The decay is multiplicative and selective. Frequently reinforced associations resist decay. Important knowledge persists. Only stale, unreinforced patterns fade.

This mirrors how biological memory actually works. We don't remember every detail of every day. We keep what's salient and let the rest go.

The specific decay parameters—which govern how aggressively the system forgets and how quickly it adapts—are intentionally not published. Why? Because those parameters are context-dependent and require deep domain understanding to tune correctly. Publishing them would suggest they're universally applicable, which would be both false and dangerous.

The Plasticity-Stability Trade-off

Our experiments revealed a critical insight: there is no universal decay parameter.

Aggressive decay accelerates adaptation but risks forgetting valuable patterns before they consolidate. Conservative decay preserves knowledge but slows adaptation to real regime shifts. The optimal point exists on a spectrum between these poles, and it's domain-specific, environment-specific, and even task-specific.

This is why we do not publish specific decay parameters or "recommended" values:

A financial system responding to market regime changes needs different decay profiles than a legal precedent system
A real-time recommendation engine has different stability requirements than a long-horizon planning system
Crypto volatility demands faster forgetting than regulatory compliance

Any published "default" value would be simultaneously wrong for most domains and would tempt practitioners to use it as a universal constant.

The right approach is to treat decay as a tunable architectural parameter that requires domain expertise to optimize—much like circuit design requires understanding your specific load.

What This Means for Enterprise Deployment

These findings have direct implications for production AI systems:

Financial Services

Market regimes shift between bull, bear, and volatile states. Adaptive decay prevents models trained on yesterday's market from degrading today's predictions.

Regulatory Compliance

When new regulations take effect, the system automatically down-weights obsolete compliance knowledge. No manual retraining required.

Customer Intelligence

Seasonal shifts, product launches, and competitive dynamics continuously alter user patterns. Adaptive agents maintain responsiveness without accumulating stale behavioral models.

Implementation Philosophy

If you're implementing your own adaptive architecture, the principle is this: tune decay based on your environment's drift characteristics, not on published recommendations. Rapidly changing domains need faster decay; stable domains need slower decay. The balance between plasticity and stability is yours to discover through domain-informed testing.

The Architectural Lesson

This simulation validates a core principle of Artemis City:

Memory decay is not optional. It's architecturally critical.

Systems without controlled forgetting will suffer cumulative memory poisoning in any non-stationary environment. And in the real world, every environment is non-stationary.

The question isn't whether to implement decay—it's how aggressively to tune it.

Methodology Notes

For the researchers in the audience:

Reproducibility: Fixed random seed (42) for all experiments
Agent Architecture: 5× MLPRegressor (100, 50 hidden layers)
Evaluation: Moving average error with window=50
Dataset: 1,000 samples, 3 features, Gaussian noise σ=1.0

Full technical details are available in the Artemis City Whitepaper V2, Section 6.

What's Next

This simulation used synthetic data to validate the core mechanism. Production validation across real enterprise knowledge bases is the next frontier.

We're particularly interested in:

Multi-domain drift (e.g., legal + financial + operational shifts simultaneously)
Adaptive decay rate tuning based on detected drift velocity
Integration with the Hebbian Learning Engine's validation gates

If you're building systems that need to stay accurate in changing environments, Artemis City's adaptive architecture is the foundation you need.

The full simulation notebook and whitepaper are available in the Artemis City repository.