---
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