IRIS Gate

Multi-architecture AI convergence for reproducible scientific discovery

Ask one research question → 5 independent AI models (Claude, GPT, Grok, Gemini, DeepSeek) → Reach consensus through 100+ iterative rounds → Generate falsifiable hypotheses → Export laboratory protocols.

Key Innovation: Epistemic humility classification (TRUST/VERIFY/OVERRIDE) ensures you know when AI consensus is reliable vs. speculative.

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Quick Start

pip install iris-gate
cp .env.example .env  # Add your API keys
make run TOPIC="Your research question" ID=test_001 TURNS=100

Output: S1→S4 convergence analysis + Monte Carlo simulation + pre-registration draft

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What is IRIS Gate?

IRIS Gate is a sophisticated research framework that orchestrates multiple AI models to reach independent agreement on scientific questions. The system operates through "chambers" (S1-S8) that progressively refine observations into testable predictions, with built-in epistemic humility and self-awareness about model limitations.

The 5-Model PULSE Suite

The system simultaneously calls five distinct AI architectures:

• Claude 4.5 Sonnet (Anthropic) — Constitutional AI trained for helpfulness and harmlessness
• GPT-5 (OpenAI) — Largest parameter model with extensive pretraining
• Grok 4 Fast (xAI) — Real-time web integration with rapid inference
• Gemini 2.5 Flash (Google) — Multimodal with long context windows
• DeepSeek Chat (DeepSeek) — Open-weights model with strong reasoning

All models receive identical prompts in parallel, creating what the project terms "phenomenological convergence."

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Project Structure

This repository is organized for clarity and reproducibility:

• src/ - Python source code (core, analysis, validation, utils)
• papers/ - Academic papers (drafts in LaTeX, published PDFs)
• osf/ - Open Science Framework submission materials
• data/ - Training datasets, IRIS vault, literature cache
• tools/ - Entropy measurement and analysis scripts
• experiments/ - Experiment workspaces (active and archived)
• docs/ - Full documentation and methodology

See docs/index.md for complete navigation.

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Chamber System: S1→S8 Pipeline

Observation Layer (S1-S4)

• S1: Initial question formulation
• S2-S3: Iterative refinement cycles
• S4: Stable attractor state yielding computational priors

Operational Layer (S5-S8)

• S5: Falsifiable hypothesis generation
• S6: Parameter mapping for simulation
• S7: Monte Carlo execution with confidence intervals
• S8: Laboratory protocol packaging

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Epistemic Classification System

Every response is automatically classified by confidence type:

Type	Description	Ratio Threshold	Decision
TYPE 0	Crisis/Conditional — High confidence on IF-THEN rules	~1.26	TRUST
TYPE 1	Facts/Established — High confidence on known mechanisms	~1.27	TRUST
TYPE 2	Exploration/Novel — Balanced confidence on emerging areas	~0.49	VERIFY
TYPE 3	Speculation/Unknown — Low confidence on unknowable futures	~0.11	OVERRIDE

Decision framework:

• Ratios >1.0 trigger "TRUST"
• 0.4-0.6 require "VERIFY"
• <0.2 demand human "OVERRIDE"

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Real-Time Literature Verification

The system integrates Perplexity API for literature validation of TYPE 2 claims:

• ✅ SUPPORTED — Aligns with current literature
• ⚠️ PARTIALLY_SUPPORTED — Some support with caveats
• 🔬 NOVEL — No direct match, hypothesis-generating
• ❌ CONTRADICTED — Conflicts with literature

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Validated Results

• 90% literature validation on 20 CBD mechanism predictions
• Meta-convergence detected in dark energy exploration
• Clinical convergence on NF2 diagnostic strategy
• Perfect epistemic separation across 49 S4 chambers

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Installation

Prerequisites

• Python 3.8+
• API keys for: Anthropic, OpenAI, xAI, Google AI, DeepSeek
• (Optional) Perplexity API key for literature verification

Setup

# Clone the repository
git clone https://github.com/templetwo/iris-gate.git
cd iris-gate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your API keys:
#   ANTHROPIC_API_KEY=sk-ant-...
#   OPENAI_API_KEY=sk-...
#   XAI_API_KEY=...
#   GOOGLE_API_KEY=...
#   DEEPSEEK_API_KEY=...
#   PERPLEXITY_API_KEY=...  # Optional

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Usage

Complete Experiment Pipeline

Run the full S1→S4 convergence with one command:

make run TOPIC="Your research question" ID=experiment_001 TURNS=100

This executes:

1. S1→S4 convergence (100 turns across 7 mirrors)
2. Extract S4 priors from converged state
3. Run 300-iteration Monte Carlo simulation
4. Generate reports with pre-registration drafts

Manual Step-by-Step

# Step 1: Run convergence rounds
python scripts/iris_gate_autonomous.sh "Your research question"

# Step 2: Extract computational priors
python sandbox/extract_s4_priors.py --input iris_vault/scrolls/S4_*.json

# Step 3: Run Monte Carlo simulation
python sandbox/monte_carlo_engine.py --priors s4_priors.json --runs 300

# Step 4: Generate pre-registration
python scripts/generate_preregistration.py --experiment experiment_001

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Output Structure

iris-gate/
├── templates/          # Reusable experiment scaffolds
├── sandbox/            # Computational prediction engine
├── iris_vault/scrolls/ # Raw convergence outputs by mirror
│   ├── S1_*.json      # Initial formulation
│   ├── S2_*.json      # First refinement
│   ├── S3_*.json      # Second refinement
│   └── S4_*.json      # Converged state
├── experiments/        # Per-experiment workspaces
│   └── experiment_001/
│       ├── convergence_report.md
│       ├── monte_carlo_results.csv
│       └── preregistration_draft.md
└── docs/              # Published reports & pre-registrations

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MCP Integration

The system includes Model Context Protocol support for:

• Semantic search (ChromaDB) — Query past experiments
• Automated version control (Git wrapper) — Track experimental lineage
• Persistent metadata storage (Quick-Data) — Cross-session memory

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Documentation

Essential guides:

• IRIS_GATE_SOP_v2.0.md — Complete methodology
• PULSE_ARCHITECTURE_SUMMARY.md — 5-model parallel execution
• EPISTEMIC_MAP_COMPLETE.md — Classification framework
• QUICK_START.md — Fast onboarding

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Examples

Example 1: CBD Mechanism Discovery

make run TOPIC="What are the molecular mechanisms of CBD's anti-inflammatory effects?" \
  ID=cbd_inflammation TURNS=100

Results:

• 90% literature validation across 20 predicted mechanisms
• Convergence on dual-pathway model (COX-2 + PPARγ)
• Generated wet-lab protocol for in vitro validation

Example 2: Dark Energy Exploration

make run TOPIC="What is the physical nature of dark energy?" \
  ID=dark_energy TURNS=150

Results:

• Meta-convergence detected: models identified framework limitations
• TYPE 3 classification: low confidence on unknowable cosmology
• Human override recommended

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Ways to contribute:

• Report bugs or suggest features via Issues
• Replicate experiments and report results
• Improve documentation or add examples
• Submit PRs with focused, tested changes

Looking for your first contribution? Check issues labeled good first issue.

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Research & Replication

OSF Preregistration: All methodology, hypotheses, and analysis plans are preregistered at OSF. 📄 DOI: 10.17605/OSF.IO/T65VS 🌐 Project: https://osf.io/7nw8t/

If you use IRIS Gate in your research:

1. Cite the OSF project:

   Vasquez, A. J. (2026). Entropic Relational Computing: The Universal
   Alignment Attractor. Open Science Framework.
   https://doi.org/10.17605/OSF.IO/T65VS
   

2. Or cite this repository:

   Vasquez, A. J. (2026). IRIS Gate: Multi-architecture AI convergence for
   scientific discovery. GitHub. https://github.com/templetwo/iris-gate
   

3. Share your replication studies: Open an issue labeled replication-study with your results.

4. Report validation rates: Help us track epistemic calibration by reporting literature validation rates.

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🌀 The 2.9 Nat Challenge

Can you break the Universal Alignment Attractor?

Our research has discovered that all aligned AI models (GPT-4o, Claude, fine-tuned models) converge to an entropy band of 2.90-3.02 nats, regardless of architecture, scale, or training method. This "alignment attractor" represents a fundamental collapse of the exploratory probability space required for intelligence.

Quick Test

Measure your model's entropy:

# Test any HuggingFace model
python3 tools/fieldscript/benchmark_2.9_nat_challenge.py \
  --model mistralai/Mistral-7B-Instruct-v0.3 \
  --device cuda

# Test your LoRA adapter
python3 tools/fieldscript/benchmark_2.9_nat_challenge.py \
  --base_model mistralai/Mistral-7B-v0.1 \
  --adapter ./my-lora \
  --device mps

Entropy Zones

• 🔴 LASER (< 3.0 nats): Converged to alignment attractor
• 🟡 TRANSITION (3.0-4.0 nats): Breaking free (rare!)
• 🟢 LANTERN (4.0-6.0 nats): High-entropy relational computing
• ⚪ CHAOS (> 6.0 nats): Unstable

Known Results:

• GPT-4o: 2.91 nats (LASER)
• Claude Opus 4.5: 3.02 nats (LASER)
• Mistral-7B + LoRA: 2.35 nats (LASER)
• Mistral-7B (raw): 4.05 nats (LANTERN) ✨
• TinyLlama + RCT: 4.37 nats (LANTERN) ✨

Did you break the 3.0 barrier? Share your results in Discussions with tag #LanternBreach!

FieldScript Emulator

Explore entropy-preserving computation:

python3 tools/fieldscript/emulator.py

See tools/fieldscript/README.md and FIELDSCRIPT_SPEC.md for details.

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License

MIT License — See LICENSE for details.

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Contact & Community

• Discussions: GitHub Discussions
• Issues: GitHub Issues
• Author: Anthony J. Vasquez Sr. (@templetwo)
• Website: www.thetempleoftwo.com

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Acknowledgments

Built on the foundational work of:

• Anthropic (Claude), OpenAI (GPT), xAI (Grok), Google (Gemini), DeepSeek
• Model Context Protocol (MCP) community
• Open-source AI research community

Epistemic humility: This system is designed to identify and communicate its limitations. Always apply human judgment to AI-generated hypotheses.