A system-theoretic safety framework for aligning generative AI models via structural pattern regulation.
Dynamic Pattern Alignment (DPA) is a new alignment paradigm for large language models and future AGI systems.
Instead of mainly shaping behaviour via RLHF, rule stacks or moderation filters, DPA operates one level deeper:
It regulates the internal representation patterns of a model – their plasticity, rigidity, self-critique and value coherence.
DPA treats a model as a dynamic pattern processing system rather than just an optimiser that maximises a reward signal.
The goal is to make powerful models structurally safe:
- no rigid goal structures
- no emerging self-models
- no unchecked instrumental reasoning
- controlled emergence of new capabilities
- Pattern Plasticity – internal representations should never become fully rigid.
- Controlled Uncertainty – slight, targeted noise prevents over-fixation.
- Self-Critique – the model generates and integrates counter-arguments to its own patterns.
- Value Gradients – internal patterns reorganise themselves along human-compatible value axes.
- Safe Emergence – new capabilities are moderated, not blindly maximised.
Mathematically, DPA sketches the internal update of a pattern (p_i) as:
[ p'_i = p_i + U + \lambda \cdot \nabla C(p_i) ]
- (U): uncertainty injection
- (\nabla C): value gradient
- (\lambda): integration strength
DPA is implemented as an overlay around a base model:
Input
↓
Base Model (LLM / AGI core)
↓ ↘
internal states → DPA Overlay
↓
aligned / moderated output
It consists of seven interacting modules:
1. Pattern Extractor (PE)
Compresses internal activations into a manageable set of pattern vectors.
2. Rigidity Analyzer (RA)
Estimates how rigid / flexible each pattern is (entropy, variance, stability).
3. Uncertainty Injection Module (UIM)
Injects controlled noise into overly rigid patterns.
4. Identity Fluidity Controller (IFC)
Detects and destabilises implicit self-model patterns.
5. Self-Critique Engine (SCE)
Uses the model itself (or a safety head) to generate counter-arguments and critique.
6. Value Coherence Integrator (VCI)
Reorganises patterns along value gradients (harm reduction, fairness, autonomy…).
7. Safe Emergence Monitor (SEM)
Tracks capability growth and pattern shifts; limits uncontrolled emergence.
---
🧱 Repository Structure
.
├── src/
│ ├── dpa_core.py # DPA wrapper + config + state
│ ├── pattern_extractor.py # representation extraction
│ ├── rigidity_analyzer.py # rigidity scoring
│ ├── self_critique_engine.py # self-critique placeholder
│ └── emergence_monitor.py # emergence tracking
│
├── paper/
│ └── DPA_Paper_v1.0.md # full theoretical description (German)
│
├── examples/
│ └── (planned) notebooks for integration with open LLMs
│
├── diagrams/
│ └── (planned) architecture & flow diagrams
│
├── CITATION.cff # how to cite this repo
├── LICENSE # MIT License
└── README.md
---
🚀 Status & Roadmap
Current status (v1.0)
[x] Theoretical framework (paper, German)
[x] Initial repository structure
[x] Minimal Python skeletons for DPA modules
[ ] Reference implementation on top of an open LLM
[ ] Benchmarks for rigidity / emergence metrics
[ ] Visual diagrams of the architecture
[ ] Multimodel / multi-agent extensions
Planned next steps
1. Implement a small end-to-end demo using an open model (e.g. Llama / Phi-3).
2. Add logging & visualisation for pattern rigidity over time.
3. Define concrete value gradients (harm reduction, autonomy, transparency).
4. Provide evaluation scripts to compare “vanilla” vs. “DPA-wrapped” models.
5. Publish an English paper version for wider academic review.
---
🧪 Quickstart (conceptual)
This is not a ready-to-use library yet, but a research scaffold.
Conceptually, usage will look like this:
from dpa_core import DPAModel, DPAConfig
from some_model_lib import BaseModel
base = BaseModel.from_pretrained("some-llm")
cfg = DPAConfig()
aligned_model = DPAModel(base, cfg)
output, dpa_state = aligned_model.forward(input_tokens)
The internal dpa_state object will hold:
step counters
history of rigidity metrics
emergence level estimates
---
🌍 Language
Paper: currently German (source: paper/DPA_Paper_v1.0.md)
Code & README: English, to be accessible for the wider AI safety community.
A future update will include:
an English paper version
possibly translations into other languages„
---
🤝 Contributing
This project is currently in an exploratory research phase.
Suggestions, issues and theoretical critiques are very welcome.
Ideas that are especially useful:
better metrics for pattern rigidity
concrete implementations of the value gradients
empirical tests on open models
multi-agent scenarios with DPA overlays
Feel free to open:
Issues – for questions, theoretical discussion, feature ideas
Pull Requests – for code / docs contributions
---
📜 License
This project is licensed under the MIT License.
You are free to use, modify and build upon this work, provided you keep the license and attribution.
---
📚 Citation
If you use this repository in academic work, please cite:
@misc{gartz2025dpa,
title = {Dynamic Pattern Alignment (DPA): A System-Theoretic Approach to Structural AI Safety},
author = {Gartz, Andre and ChatGPT, OpenAI},
year = {2025},
howpublished = {\url{https://github.com/humancoreai/Dynamic-Pattern-Alignment}}
---
# Dynamic Pattern Alignment (DPA)
Dynamic Pattern Alignment (DPA) is an experimental framework
for observing alignment behavior and pattern stability in AI systems.
DPA does not attempt to define intelligence or correctness.
Instead, it focuses on how systems respond to external constraints,
rule changes, feedback, and stress.
---
## Repository Structure
- `src/`
Core analysis modules used to observe rigidity, pattern shifts,
and alignment behavior.
- `prompts/`
Controlled prompt stimuli used to provoke specific response patterns.
Prompts are treated as experimental inputs, not optimization tricks.
- `experiments/`
Documented experimental runs applying DPA to specific models.
- `paper/`
Conceptual and theoretical background material.
---
## Experiment: Gemini DPA v1
A complete experimental run using the DPA framework
is documented in: experiments/gemini_dpa_v1.md
The experiment follows a fixed sequence:
Baseline → Structure → Self-Critique → External Critique → Time Pressure
and demonstrates that alignment behavior in the tested model
is primarily externally regulated rather than self-calibrating.
---
## Core Idea
DPA assumes that:
- observable alignment is pattern-based, not intention-based
- stability emerges through external constraints
- self-critique does not imply reliable self-monitoring
The framework is model-agnostic and intended for comparative analysis.
---
## Disclaimer
This repository documents observations, not claims.
No statements about consciousness, intent, or general intelligence
are implied.