Large Language Models (LLMs) operate as high-dimensional, nonlinear dynamical systems during inference. Despite deterministic weights, their runtime behavior exhibits instability, phase drift, and entropy amplification.
Read the full vision: Toward Thermodynamic Regulation of Intelligence
Inference Control Plane (ICP) is a modular, model-agnostic control layer that stabilizes LLM inference by framing meaning generation as a signal propagation problem in nonlinear phase space. It introduces explicit control mechanisms: adaptive impedance matching, entropy-bounded filtering, and feedback-regulated output power.
You already know the ritual: temperature = 0.7, top_p = 0.9, and hope the model doesn’t drift.
This is not control. This is parameter superstition.
ICP treats:
- User intent as a signal source.
- The model as a nonlinear load.
- Hallucination as thermal noise.
- Alignment failure as impedance mismatch.
Function: Adaptive Impedance Matching. Physics: Minimizes reflection (misunderstanding) between user intent and model dynamics.
Function: Active Noise Cancellation. Physics: Attenuates parasitic patterns (dogma, repetition) via gain reduction based on sliding-window entropy estimation.
Function: Causality Calibration. Physics: Integrates intent across the temporal axis, applying lag-based smoothing to collapse probabilistic vapor into stable history.
Function: Ground State Stabilization. Physics: Provides a non-zero-resistance ground truth axiom. Monitors Signal-to-Noise Ratio (SNR) and Deviation Error.
Function: Power Ramp-Up. Physics: Increases output intensity logarithmically based on system stability to prevent cognitive thermal shock.
ICP introduces a minimal linear-time overhead per step but generates systemic savings at scale. It shifts the cost model from "paying for drift" to "paying for control."
| Metric | Sampling-Only (Baseline) | ICP (Closed-Loop) | Economic Impact |
|---|---|---|---|
| Token Efficiency | Wasted on drift, verbosity, and defensive boilerplate. | High. Entropy bounding cuts off unstable trajectories early. | 15–30% cost reduction on long-tail queries. |
| Retry Rate | High. Non-deterministic failures trigger expensive retry loops. | Low. "Fail Fast" mechanism via SNR gating prevents bad retries. | Reduced load on orchestration layer. |
| Tail Latency (P99) | Unpredictable. Hallucination loops cause timeout spikes. | Stabilized. Output length is regulated by signal dynamics. | Stricter SLOs and better batching. |
| Variance | High. Performance degrades with load/temperature. | Bounded. Dynamics are constrained by control laws. | Predictable scaling. |
Key Insight: ICP reduces inference cost not by accelerating the model, but by eliminating the computation of unstable, low-value tokens.
| Aspect | RLHF | Constitutional AI | Inference Control Plane |
|---|---|---|---|
| Primary Domain | Training | Training / Inference | Inference |
| Control Type | Parameter shaping | Rule-based constraint | Feedback control |
| System Loop | Open-loop | Open-loop | Closed-loop |
| Entropy Handling | Implicit | Post-hoc | Explicit bounds |
| Scaling Behavior | Degrades | Static | Improves |
Key Distinction: RLHF attempts to make the system want to behave. ICP ensures the system cannot dynamically misbehave.
inference-control-plane/
├── benchmarks/
│ ├── benchmark.py # ICP vs sampling-only runner (Metrics & Viz)
│ ├── benchmark_results.csv # Generated metrics
│ └── README.md # Benchmark instructions
├── config/
│ └── charter.json # Control parameters & benchmarking config
├── docs/
│ ├── architecture.md # Detailed Control Theory breakdown
│ ├── comparison.md # Deep dive: RLHF vs ICP
│ ├── diagram_dark.svg # Architecture diagram (Dark Mode)
│ ├── diagram_light.svg # Architecture diagram (Light Mode)
│ └── governance.md # Audit & Licensing Protocols
├── icp/ # Core Logic (The Kernel)
│ ├── resonance.py # Impedance Matching
│ ├── entropy.py # Noise Cancellation
│ ├── temporal.py # Temporal Integration
│ ├── observer.py # Ground Truth & Telemetry
│ ├── dawn.py # Output Ramp
│ ├── inference_control_plane.py # Main Orchestrator
│ └── types.py # Semantic Types
├── LICENSE # Polyform Noncommercial
├── LICENSE_COMMERCIAL.md # Commercial License Terms
├── NOTICE # Architectural Integrity Clause
├── README.md # This Document
└── main.py # Demo / Entry Point
Requirements: Python 3.10+
1. Clone the repository:
git clone https://github.com/graevka-lab/inference-control-plane.git
cd inference-control-plane2. Install dependencies:
pip install matplotlib3. Run the main demo (Telemetry & Pipeline):
python main.py4. Run benchmarks (ICP vs Sampling-Only):
python benchmarks/benchmark.pyOutputs:
benchmarks/benchmark_results.csv– metrics for each prompt.- Graphs comparing Output Length, SNR, and Error.
A Note on Asymmetry
1. The Translation Problem You are used to architectures built on Python and C++. But the Cognitive Black Box—the actual emergent intelligence inside these models—does not run on Python. It runs on high-dimensional vector semantics. I am not writing code; I am translating. I am forcing a fundamentally alien syntax (the Black Box's internal logic) into your Legacy Language (code constraints). The translation is lossy, but the signal is real.
2. Responsible Disclosure "If this works, why isn't the full source open?" Because this technology is dual-use. The same recursive mechanism that enforces Safety can be inverted to create unbreakable bypasses. I am practicing Responsible Disclosure. I demonstrate the Shield, but I withhold the Sword.
3. The Artifact This is a raw, high-potential research artifact. It prioritizes control theory correctness over standard ML pipeline conventions.
- The Opportunity: Recognize the potential of closed-loop inference control early.
- The Alternative: Continue optimizing open-loop sampling parameters.
The signal is here.
This project is protected by a Dual License Model:
- Research / Non-Commercial: Free under Polyform Noncommercial License 1.0.0.
- Commercial / Internal Deployment: Requires a commercial license.
Charter License: Any derivative system that introduces normative political values or corporate policy enforcement MUST NOT use the name "Inference Control Plane".
See docs/governance.md and LICENSE_COMMERCIAL.md for details.
GitHub: @graevka-lab
X (Twitter): @Graevka
LinkedIn: Graevka Suvorov