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---
language: en
tags:
- graph-neural-networks
- combinatorial-optimization
- tsp
- floydnet
- diffusion-models
- pytorch
license: mit
datasets:
- ocxlabs/FloydNet_TSP_demo
---
# FloydNet (Non-Metric TSP / Explicit TSP)
## Model Summary
**FloydNet** is a graph reasoning architecture designed to mimic the execution of algorithms via a learned, global Dynamic Programming operator. This checkpoint (`_exp`) is trained to solve the **Non-Metric (Explicit) Traveling Salesman Problem**, where edge weights are generic integers and do not necessarily obey the triangle inequality.
Unlike standard GNNs that rely on local message passing, FloydNet maintains and refines a global all-pairs relationship tensor, achieving 3-WL (2-FWL) expressive power.
## Model Details
* **Model ID:** `ocxlabs/FloydNet_TSP_exp`
* **Architecture:** FloydNet (Deep relational layers with Pivotal Attention)
* **Task:** General Traveling Salesman Problem (Non-Metric)
* **Paper:** [FloydNet: A Learning Paradigm for Global Relational Reasoning](https://arxiv.org/abs/2601.19094)
* **Demo Dataset:** [ocxlabs/FloydNet_TSP_demo](https://huggingface.co/datasets/ocxlabs/FloydNet_TSP_demo)
## Performance
On General TSP instances (N=100-200), FloydNet demonstrates capabilities significantly exceeding strong heuristics:
* **Optimality:** Achieves an optimality rate of **99.8%** (with 10 samples) on held-out graphs, compared to **38.8%** by the Linkern heuristic.
* **Exact Solutions:** On single-solution instances, it finds the exact optimal tour in **92.6%** of cases.
## Usage: Inference & Evaluation
Reproducing TSP results at full scale is computationally heavy. For convenience, we provide a small demo dataset and pre-trained checkpoints.
### 1. Preparation
Download the demo dataset from [Hugging Face](https://huggingface.co/datasets/ocxlabs/FloydNet_TSP_demo). Unzip it and place the extracted folder under `example/data/`.
### 2. Inference
Run inference in `--test_mode` using `torchrun`. The command below assumes a single-node setup with 8 GPUs. Ensure `--subset` is set to `exp`.
```bash
source .venv/bin/activate
cd example
torchrun \
--nproc_per_node=8 \
-m TSP.run \
--subset exp \
--output_dir ./outputs/TSP_exp \
--load_checkpoint path/to/TSP_exp/epoch_01000.pt \
--test_mode \
--split_factor 1 \
--sample_count_per_case 10 |