---
title: Graph-PrefLexOR Scientific Reasoning
emoji: 🧠
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 6.19.0
app_file: app.py
short_description: Graph-native LLM scientific reasoning with graph viz
python_version: "3.12"
startup_duration_timeout: 1h
---

# Graph-PrefLexOR: Graph-Native Reinforcement Learning for Scientific Hypothesis Generation

This Space demonstrates **[lamm-mit/Graph-Preflexor-8b_12292025](https://huggingface.co/lamm-mit/Graph-Preflexor-8b_12292025)** — a Qwen3-8B model fine-tuned with graph-native reinforcement learning (ORPO + Graph-GRPO) to produce structured, traceable scientific reasoning.

## How it works

The model reasons through scientific questions using explicit structured blocks:

1. **`<brainstorm>`** — divergent exploration of hypotheses and mechanisms
2. **`<graph>`** — verbal sketch of the conceptual graph
3. **`<graph_json>`** — machine-readable knowledge graph (nodes + edges)
4. **`<patterns>`** — abstract motifs and design rules
5. **`<synthesis>`** — coherent narrative assembled from the graph

The Space parses the `<graph_json>` block and renders it as an interactive **Mermaid diagram** alongside the chat.

## Paper

[Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination](https://huggingface.co/papers/2607.00924) — [GitHub](https://github.com/lamm-mit/graph-preflexor-grpo)

## Tips

- Ask materials science, biology, or interdisciplinary scientific questions for best results.
- The graph panel updates after generation completes.
- Adjust temperature and max tokens in the Advanced settings.