Authors: Daouda Abdoul Anzize, Claude (Anthropic), GPT-5 (OpenAI)
Status: Patent Pending - All Rights Reserved
Date: October 2025
Version: 1.0
Virtual Reality convinces humans by replacing their sensory channels with synthetic stimuli while maintaining coherence. We propose applying this principle to artificial intelligence by replacing informational channels rather than sensory ones.
The Key Difference:
- VR for humans: Replace photons reaching eyes → synthetic images
- IR for AI: Replace database queries/API calls → synthetic but coherent information
This creates an alternative "reality" where AI agents operate within a completely consistent informational framework with zero access to base reality. The IR becomes their only accessible reality.
Existing approaches treat AI cognition as fundamentally different from human perception. We propose a unified framework: both VR and IR work on the same principle—replace input channels with synthetic but internally consistent alternatives.
This opens a new category of AI research: designing informational realities for artificial cognition.
Our framework for extracting and applying computational essences across paradigms:
- Decompose: Complete anatomical analysis of source system (12 fundamental questions)
- Extract: Identify core essences and technical primitives (not implementation details)
- Conceptualize: Formalize as mathematical/logical models
- Assimilate: Map essences to existing frameworks
- Program: Implement the absorbed principles
Application to IR:
- VR → Extract: "Create coherent perceptual replacement while maintaining temporal sync"
- VM → Extract: "Provide complete isolation from host system"
- LLM → Extract: "Stateless computation with attention-distributed processing"
Result: A framework optimized for AI cognition through informational coherence.
The current implementation demonstrates this concept through standard techniques (prompt engineering + state management). This is intentional.
A prototype system that:
- Manages world state (JSON-based, simple)
- Generates hierarchical context (prompt engineering pattern)
- Filters responses for coherence (standard post-processing)
- Coordinates multi-agent interactions
- Hierarchical prompting: Existing technique
- Context injection: Standard practice
- Artifact removal: Common response filtering
- State management: Basic database operations
These are implementation details. They're effective for this concept, but others could implement the same concept differently (and likely better).
The conceptual framework: IR as informational VR applied to cognition.
The implementation is just one way to test this framework. Future implementations could use:
- Different state representations
- Neural approaches
- Hybrid reasoning systems
- Real-time physics engines
The framework remains valid regardless of implementation choices.
We validated that this framework produces measurable results:
- Models tested: 3 different LLM architectures (Llama-3.3-70b, Gemma2-9b-it, Deepseek-r1)
- Scenarios: Multi-agent collaborative crisis resolution
- Metric: Immersion maintenance (agents maintain coherent embodied perspective)
| Metric | Without IR Framework | With IR Framework | Improvement |
|---|---|---|---|
| Immersion Maintenance | ~62% | 97.5% | +35.5 pts |
| Shared World Coherence | Variable | 100% | Stabilized |
| Emergent Social Behavior | Rare | Consistent | Observed |
Key Finding: Applying IR principles (regardless of implementation) consistently improved AI agent coherence across different model architectures.
Immersion measurement is based on heuristic pattern detection, not rigorous metrics. This validates the concept works, not that specific numbers are definitive. Better measurement methods are needed for precise quantification.
Live API: https://ri-api-plt9.onrender.com
Create an IR world:
curl -X POST https://ri-api-plt9.onrender.com/api/v1/world/create \
-H "Content-Type: application/json" \
-d '{
"name": "Research Laboratory",
"objects": [
{"id": "desk", "type": "furniture", "state": "stable"},
{"id": "screen", "type": "display", "state": "active"}
],
"metadata": {
"description": "A modern research lab environment"
}
}'Get world context for your AI:
curl https://ri-api-plt9.onrender.com/api/v1/world/{world_id}/contextSend context to your AI (use your own API key):
import requests
from openai import OpenAI # or Anthropic, Groq, etc.
world_context = requests.get(
f"https://ri-api-plt9.onrender.com/api/v1/world/{world_id}/context"
).json()["formatted_context"]
client = OpenAI(api_key="YOUR_KEY")
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": world_context},
{"role": "user", "content": "Describe your perception."}
]
)| Method | Endpoint | Description |
|---|---|---|
| GET | /health |
Health check |
| POST | /api/v1/world/create |
Create world |
| GET | /api/v1/world/{id}/context |
Get context |
| POST | /api/v1/world/{id}/update |
Update world state |
| GET | /api/v1/world/{id}/metrics |
Get metrics |
| GET | /api/v1/worlds |
List worlds |
| DELETE | /api/v1/world/{id} |
Delete world |
- 100 requests/minute
- 100 objects per world
- 1000 active worlds
- 24-hour retention
A complete test validating the IR framework with real AI integration:
- Creates IR world
- Sends context to Groq API (Llama-3.3-70b)
- Measures immersion maintenance
- Detects coherence breaks
Results: 100% immersion maintained across 4 queries with zero breaks detected.
Run tests: Create new notebook at colab.research.google.com and paste the test script.
- Not a physics engine: No real physics calculations
- Not a game engine: Not optimized for rendering or real-time performance
- Not a breakthrough in AI capabilities: Uses standard LLMs
- Not a solution to alignment or safety: A framework for studying these questions
- Not the implementation: The implementation is standard; the framework is novel
Research and applications that benefit from AI agents operating in consistent informational realities:
-
AI Safety Testing
- Test behaviors in controlled alternative realities
- Study decision-making under defined constraints
-
Embodied AI Research
- Train agents with consistent spatial/social context
- Study emergent behaviors in shared frameworks
-
Alignment Studies
- Compare AI's internal models with framework territory
- Study value learning in situated contexts
-
Multi-Agent Coordination
- Consistent shared reality for agent interaction
- Protocol emergence and communication studies
-
Cognitive Science
- Understand how informational coherence affects LLM behavior
- Study markers of consistent perspective maintenance
Informatique Réalitaire is patent-pending intellectual property.
The innovation is the conceptual framework for applying VR principles to AI cognition through informational channel replacement.
The current implementation is provided as proof-of-concept. Others may implement this framework differently.
Commercial licensing available for organizations wishing to develop IR applications.
This is early-stage research. Key areas for development:
- Better measurement metrics (current immersion scoring is heuristic)
- Scaling to larger worlds (currently tested at small scale)
- Longer-term studies (current tests are short duration)
- Alternative implementations (current implementation is one approach)
- Theoretical refinement (framework could be formalized more rigorously)
- Real-time aspects (current system is request-response based)
@article{anzize2025informatique,
title={Informatique Réalitaire: Applying Virtual Reality Principles to Artificial Intelligence Cognition},
author={Anzize, Daouda Abdoul and Claude and GPT-5},
journal={Preprint},
year={2025},
note={Patent Pending}
}- Live API: https://ri-api-plt9.onrender.com
- API Documentation: /docs/
- Test Scripts: /tests/
- Interactive Demo: https://tryboy869.github.io/informatique-realitaire-paper/
Copyright © 2025 Daouda Abdoul Anzize. All Rights Reserved.
The IR framework and DECAP NEXUS methodology are protected intellectual property.
Permitted: Academic citation, educational discussion, public API usage (within rate limits)
Requires permission: Commercial implementation, derivative frameworks, patent applications
- Author: Daouda Abdoul Anzize
- Email: nexusstudio100@gmail.com
- GitHub: @Tryboy869
For commercial inquiries or licensing: [contact information]
Status: Active Research | Patent Pending | Framework Open for Academic Discussion | Implementation Available via Public API
Last Updated: October 2025