ASPR Gardener

Adaptive Structural Regulation for AI Systems

ASPR (Adaptive Structural Pruning & Reporting) is a regulatory framework designed to control structural complexity in AI inference systems.

Instead of relying on static cleanup, ASPR introduces a continuous regulation layer ("Gardener") that observes system behavior and maintains equilibrium through adaptive pruning.

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🧠 Abstract

Modern AI systems tend to accumulate structural complexity over time. Components that are no longer functionally relevant remain active, consuming resources and degrading overall system efficiency.

ASPR introduces a continuous regulatory mechanism that:

• observes usage patterns
• detects underutilized or idle structures
• applies adaptive, reversible pruning

The result is a system that self-regulates instead of passively growing.

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⚠️ Problem Statement

AI systems exhibit monotonic growth without natural decay:

• Obsolete components persist indefinitely
• Resource consumption increases without proportional utility
• Structural noise accumulates silently
• System efficiency degrades over time

There is currently no standard mechanism for runtime structural regulation in AI systems.

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💡 Approach

ASPR introduces a parallel agent ("Gardener") that operates continuously:

• detects low-usage clusters
• removes unnecessary components
• stabilizes the system dynamically
• reports efficiency metrics

It does not modify the core system — it regulates it.

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🔁 System Dynamics

Without ASPR:

growth → accumulation → inefficiency → degradation

With ASPR:

growth → detection → pruning → stabilization

👉 Result: dynamic equilibrium instead of uncontrolled expansion

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📊 Experimental Results

Simulation: 10 million requests, standard parameters

Metric	Baseline	ASPR	Delta
Avg Clusters	24.0	20.8	−13.3%
Energy Consumption	2400 u.	2082 u.	−13.28%
Latency p50	5.17 ms	5.17 ms	0
Latency p95	5.53 ms	5.53 ms	0
Latency p99	5.61 ms	5.61 ms	0

Key result: Structural and energy reduction without latency degradation

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📈 Structural Evolution (Simulated)

Cluster evolution over time:

Clusters
 30 |            ████
 28 |           ██████
 26 |          ████████
 24 | ██████████████████   ← baseline (unbounded growth)
 22 | ████████████████
 20 | ██████████████       ← ASPR stabilizes
 18 | ███████████
 16 |
    +--------------------------------
      0   20   40   60   80   100 epochs

👉 Without ASPR: cumulative growth 👉 With ASPR: progressive stabilization

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⚠️ Not Just Pruning

ASPR is not a static cleanup tool.

Traditional approaches rely on:

• fixed thresholds
• one-time deletion
• no system awareness

ASPR introduces:

• continuous structural regulation
• behavior-aware pruning
• system-level equilibrium

👉 The goal is not deletion — it is stability under evolution ASPR is not a threshold-based cleanup system.

It operates as a continuous regulatory loop that adapts to system dynamics, preventing structural drift rather than reacting to it.

🧱 Architecture

aspr-gardener/
│
├── mvp_compare.py   # Simulation engine
├── launch.py        # Launcher + dashboard
├── data/            # Generated CSV results
├── logs/            # Runtime logs
└── lib/             # Optional dependencies

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🚀 Usage

Run simulation

python mvp_compare.py

Custom parameters

python mvp_compare.py --requests 1000000 --epochs 50 --clusters 48

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Dashboard

pip install flask plotly
python launch.py --dashboard

Open:

http://localhost:7771

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📊 Output

Each run generates:

data/results_YYYYMMDD_HHMMSS.csv

Format:

metric,baseline,aspr,delta_pct

Compatible with GHG Protocol Scope 3 reporting.

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🧪 Production Integration

ASPR is designed as a sidecar process:

Observation mode

aspr = System(enable_pruning=False)

Active mode

aspr = System(enable_pruning=True)

✔ Non-intrusive ✔ Fully reversible ✔ Auditable

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🧬 Future — Ghost Registry

Next iteration introduces a memory layer:

• tracks historical pruning patterns
• identifies recurring structural failures
• prevents re-emergence of inefficient configurations

👉 This transforms ASPR from reactive pruning to evolutionary regulation

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📄 Paper (in progress)

ASPR: Adaptive Structural Regulation for AI System Efficiency Ramiro Guevara, 2025

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📌 Project Status

• ✅ Baseline vs ASPR simulation
• ✅ Interactive dashboard
• ✅ CSV export
• 🚧 Ghost Registry (memory layer)
• 🚧 Production validation
• 🚧 Kubernetes sidecar

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🤝 Contributions

Areas of interest:

• hardware-level metrics (RAPL, IPMI)
• Kubernetes integration
• GHG / ISO measurement frameworks
• real-world deployment cases

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🧪 Pilot Program

Looking for early adopters to validate ASPR in real infrastructure.

Contact: ramiguevara@gmail.com

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📜 License

MIT License

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🧠 Closing Thought

Systems that only grow will eventually collapse under their own complexity. Systems that regulate themselves can evolve.## Public Key Fingerprint SHA-256: 5482f712f89270b7755219a75cef214e0b137ea7fd9ee012dbe504875d600769