| --- |
| language: en |
| tags: |
| - graph-neural-networks |
| - combinatorial-optimization |
| - tsp |
| - floydnet |
| - diffusion-models |
| - pytorch |
| license: mit |
| datasets: |
| - ocxlabs/FloydNet_TSP_demo |
| --- |
| |
| # FloydNet (Metric TSP / Euclidean 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 (`_euc`) is trained to solve the **Metric (Euclidean) Traveling Salesman Problem**, where edge weights are defined by Euclidean distances between 2D coordinates. |
|
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| FloydNet operates directly on the pairwise relationship tensor (distance matrix), learning to refine global dependencies without explicit geometric engineering. |
|
|
| ## Model Details |
|
|
| * **Model ID:** `ocxlabs/FloydNet_TSP_euc` |
| * **Architecture:** FloydNet (Deep relational layers with Pivotal Attention) |
| * **Task:** Metric Traveling Salesman Problem (Euclidean) |
| * **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) |
|
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| ## Performance |
|
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| On Metric TSP instances (N=100-200), FloydNet matches the performance of specialized geometric heuristics: |
| * **Robustness:** Maintains robust performance (>96% optimality) within the training distribution ($N \le 100$). |
| * **Generalization:** effectively generalizes to larger unseen graph sizes. |
|
|
| ## Usage: Inference & Evaluation |
|
|
| ### 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`. Ensure `--subset` is set to `euc` and the checkpoint path matches. |
|
|
| ```bash |
| source .venv/bin/activate |
| cd example |
| |
| torchrun \ |
| --nproc_per_node=8 \ |
| -m TSP.run \ |
| --subset euc \ |
| --output_dir ./outputs/TSP_euc \ |
| --load_checkpoint path/to/TSP_euc/epoch_01000.pt \ |
| --test_mode \ |
| --split_factor 1 \ |
| --sample_count_per_case 10 |
