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.
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).
docker run -p 8080:8080 vidraft/marl
pip install marl-middleware
python -m marl serve --port 8080
Use https://huggingface.co/spaces/VIDraft/MARL directly in your browser.
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.
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.
{
"llm": {
"baseURL": "http://localhost:8080/v1",
"model": "gpt-5.4::pharma"
}
}
Then chat: "Find drug repositioning candidates for Alzheimer's using immune checkpoint mechanisms"
{
"llm": {
"model": "claude-sonnet::create"
}
}
Then chat: "Generate 10 movie loglines that have never existed before"
┌─ 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.
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
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