dvcrn MARL Enhance — Brain Upgrade for Your Agent
The 3rd approach after fine-tuning & RAG. MARL restructures how LLMs reason at runtime — not their weights. One line to integrate, 70%+ hallucination reduction, 9 domain-specific emergence engines.
What It Does
Before MARL: Your agent calls the LLM once → gets an answer (might hallucinate).
After MARL: Your agent calls MARL → MARL runs a multi-stage expert pipeline → hypothesis, solving, auditing, adversarial verification, synthesis → returns a deeply verified answer.
Your Agent → MARL → Multi-stage Pipeline → Any LLM → Verified Answer
Results: 70%+ hallucination reduction · 94.8% of improvement from self-correction · Verified on FINAL Bench (HuggingFace Global Top 5 dataset).
Setup
Option A: Docker (Recommended — all platforms)
docker run -p 8080:8080 vidraft/marl
Option B: pip (Linux x86_64)
pip install marl-middleware
python -m marl serve --port 8080
Option C: HuggingFace Space (No install — try instantly)
Use https://huggingface.co/spaces/VIDraft/MARL directly in your browser.
Connect to OpenClaw
Set your config.json:
{
"llm": {
"baseURL": "http://localhost:8080/v1",
"model": "gpt-5.4"
}
}
That's it. Every LLM call now passes through MARL's multi-stage reasoning pipeline.
9 Emergence Modes
Switch modes by appending ::mode to any model name:
| model value | Engine | What it does |
|---|---|---|
gpt-5.4 |
🔬 Insight | Default — fact-check, strategy, deep analysis |
gpt-5.4::invent |
🔧 Invent | Patent-level invention via TRIZ + bio-inspired + contradiction resolution |
gpt-5.4::create |
✨ Create | Cliché inversion, paradox, genre fusion, sensory collision |
gpt-5.4::recipe |
🍳 Recipe | Culinary emergence with taste chemistry validation |
gpt-5.4::pharma |
💊 Pharma | Drug repositioning, mechanism crossing, multi-target design |
gpt-5.4::genomics |
🧬 Genomics | Pathway crosstalk, synthetic lethality, phenotype bridging |
gpt-5.4::chemistry |
🧪 Chemistry | Contradictory properties, biomimicry, waste-to-value |
gpt-5.4::ecology |
🌍 Ecology | Conservation transfer, threat inversion, service stacking |
gpt-5.4::law |
⚖️ Law | Cross-jurisdiction transplant, tech-law collision resolution |
gpt-5.4::document |
📄 Document | Metacognitive report and document generation |
Replace gpt-5.4 with any model — Claude, Gemini, DeepSeek, Llama, etc.
Example: Switch to Pharma mode
{
"llm": {
"baseURL": "http://localhost:8080/v1",
"model": "gpt-5.4::pharma"
}
}
Then chat: "Find drug repositioning candidates for Alzheimer's using immune checkpoint mechanisms"
Example: Creative ideation
{
"llm": {
"model": "claude-sonnet::create"
}
}
Then chat: "Generate 10 movie loglines that have never existed before"
How It Works
┌─ OpenClaw ────────────────────────────────────┐
│ "Analyze this complex question" │
└──────────────┬─────────────────────────────────┘
│ HTTP (OpenAI API format)
▼
┌─ MARL Middleware ─────────────────────────────┐
│ Multi-stage Multi-agent Reasoning Pipeline │
│ 9 Emergence Engines · 70%+ Hallucination ↓ │
└──────────────┬─────────────────────────────────┘
│ API calls to your chosen LLM
▼
┌─ Any LLM ─────────────────────────────────────┐
│ GPT-5.4 · Claude · Gemini · DeepSeek · Llama │
└────────────────────────────────────────────────┘
MARL works with every LLM that supports OpenAI API format. It runs locally on your machine — your data never leaves your infrastructure.
Works With Any LLM
- OpenAI (GPT-5.4, GPT-5.2, GPT-4.1, o4-mini)
- Anthropic (Claude Opus 4.6, Sonnet 4.6)
- Google (Gemini 3.1 Pro, Gemini 3 Flash)
- DeepSeek (V3, R1, R2)
- xAI (Grok-4, Grok-3)
- Groq (gpt-oss-120b, Llama 4 — free)
- Ollama (any local model)
- Any OpenAI-compatible endpoint
Links
- PyPI: pip install marl-middleware
- GitHub: github.com/Vidraft/MARL
- Demo: HuggingFace Space
- FINAL Bench: Leaderboard
- Website: vidraft.net
About
Built by VIDRAFT (Seoul AI Hub). MARL's core engine is delivered as compiled binaries to protect proprietary technology. Interface code is open for integration.
Apache 2.0 · Contact: arxivgpt@gmail.com